CN106406338B - Autonomous navigation device and method of omnidirectional mobile robot based on laser range finder - Google Patents
Autonomous navigation device and method of omnidirectional mobile robot based on laser range finder Download PDFInfo
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Abstract
The invention relates to the technical field of autonomous navigation of mobile robots, in particular to an autonomous navigation device of an omnidirectional mobile robot based on a laser range finder and a method thereof. The utility model provides an autonomous navigation device of omnidirectional mobile robot based on laser rangefinder, includes the encoder of measuring mobile robot wheel's travel distance, measures mobile robot's rotation angular velocity and acceleration's inertial measurement unit, and encoder and inertial measurement unit connect the controller respectively, still include laser rangefinder, and the host computer is connected to the laser rangefinder, and the host computer is connected the controller, and the execution unit is connected to the controller. The invention relates to a method for automatically avoiding obstacles in the travelling process, which is characterized in that a laser range finder is used for positioning a mobile robot, a two-dimensional plane map of an environment is established, and autonomous navigation is performed according to a target task.
Description
Technical Field
The invention relates to the technical field of autonomous navigation of mobile robots, in particular to an autonomous navigation device of an omnidirectional mobile robot based on a laser range finder and a method thereof.
Background
The intelligent mobile robot can autonomously move in an unknown or partially unknown environment and has the functions of environment perception, behavior planning, dynamic decision, decision execution and the like. The robot generally needs to solve three problems in the autonomous movement process, namely, the positioning problem of the robot, the task planning problem of the robot and the behavior planning problem of the robot. Because of the lack of prior map information in an unknown environment, the mobile robot needs to acquire surrounding environment information through sensors such as a vehicle-mounted laser range finder, a monocular camera and a binocular camera to construct an environment map and estimate the current position of the robot, namely synchronous positioning and map construction (Simultaneous Localization and Mapping, SLAM). Planning a feasible path of the robot according to the target task based on the constructed map information; the robot needs accurate positioning information in the process of travelling along the planned path, and realizes dynamic behavior decision according to the real-time position information and the environment information to complete the target task.
SLAM is divided into two aspects of positioning and mapping, and is a core technology for realizing autonomous navigation of a mobile robot in an unknown environment. The traditional dead reckoning method utilizes information such as a vehicle-mounted odometer and the like to position the robot, but accumulated errors exist in the odometer, and the positioning accuracy rate of the accumulated errors is reduced along with the movement of the robot. When the environment map is known, the observed environment information can be compared with the known map information, so that the pose estimation of the robot is corrected in real time, and the accumulated error of positioning is reduced. The SLAM considers the condition that the environment information is unknown, on one hand, the pose estimation of the robot needs priori map information, on the other hand, the establishment of the map needs accurate pose information of the robot, the positioning and the map establishment complement each other and are mutually influenced, the accurate positioning provides an accurate map, and the accurate map provides accurate positioning. At present, SLAM is mainly based on a SLAM method based on a kalman filter and a particle filter, but as a robot moves and a map expands, the kalman filter needs to maintain a huge state variable matrix, and the particle number of the particle filter needs to increase, so the computational complexity of the SLAM algorithm based on the kalman filter and the particle filter increases with the movement of the robot. Meanwhile, when the kinematic model and the observation model of the robot are nonlinear, especially when an accurate observation model cannot be obtained, the accuracy of the SLAM algorithm based on the Kalman filtering and the particle filtering is affected. According to the invention, an SLAM algorithm based on an iterative closest point algorithm (ICP) is considered, and the calculation cost of using a point matching iterative calculation method is fixed due to the limited number of point clouds obtained by a laser range finder, so that compared with the SLAM algorithm based on Kalman filtering and particle filtering, the ICPSLAM algorithm has the advantages of reduced calculation complexity and better instantaneity.
The global path planning algorithm mainly comprises a fuzzy planning algorithm, an ant colony optimization algorithm, a particle swarm algorithm, an A-algorithm and other methods, and the methods generally need to accurately model and describe the obstacle in a certain space. In addition, when the environment tends to be complex or dynamically changed, how to make the path of the robot globally optimal or suboptimal and how to avoid the concussion and deadlock of the planned path remain to be solved. The rapid expansion random tree (RRT) algorithm is a single query motion planning method based on sampling, searches and guides the search to a blank area through random sampling points of a state space, so that a feasible path from a starting point to a target point is found, and the rapid expansion random tree (RRT) algorithm is suitable for path planning of complex environments and changing scenes.
The conventional obstacle avoidance algorithm comprises a visible view method, an artificial potential field method and a vector field histogram method. The invention considers using obstacle avoidance algorithm based on obstacle area, when the expected speed of the mobile robot falls in the established obstacle area, correcting the expected speed, and avoiding the obstacle through the minimum correction while ensuring the forward movement towards the target point.
Disclosure of Invention
The invention aims to overcome at least one defect in the prior art, and provides an autonomous navigation device of an omnidirectional mobile robot based on a laser range finder and a method thereof.
In order to solve the technical problems, the invention adopts the following technical scheme: the autonomous navigation device of the omnidirectional mobile robot based on the laser range finder comprises an encoder for measuring the moving distance of wheels of the mobile robot, an inertial measurement unit for measuring the rotation angular velocity and the acceleration of the mobile robot, and a laser range finder connected with an upper computer, wherein the encoder and the inertial measurement unit are respectively connected with a controller, and the controller is connected with an execution unit.
According to the invention, the three-wheeled omnidirectional mobile robot is used as a robot motion platform, the vehicle-mounted encoder and the inertial measurement unit are utilized to acquire the position information and the posture information (abbreviated as the posture information) of the mobile robot, and meanwhile, the vehicle-mounted laser range finder is utilized to acquire the point cloud information of the obstacle closest to the mobile robot in each direction. The current observation data of the robot is matched with the previous observation data by using an iterative closest point algorithm (ICP) to correct the pose estimation of the mobile robot, and a plane map of the environment is established. Under the condition that an environment map is known, a target task point is set, a feasible path is planned by using a rapid search random tree algorithm (RRT), and navigation control is performed on the mobile robot. In the autonomous navigation process, the robot detects the obstacle according to the real-time laser data, performs local path planning, and realizes autonomous obstacle avoidance.
In the invention, a vehicle-mounted encoder measures the moving distance of wheels of a mobile robot, an inertial measurement unit measures the rotation angular velocity and the acceleration of the mobile robot, a vehicle-mounted embedded controller acquires the measured values of the encoder and the inertial measurement unit, calculates pose information of the mobile robot by combining with a kinematic model of the mobile robot, and sends the pose information to an upper computer system; the upper computer system simultaneously acquires laser point cloud information of the laser range finder, calculates an expected value (comprising translation speed and rotation speed) of the movement speed of the mobile robot through an algorithm, and sends the expected value of the movement speed to the vehicle-mounted embedded controller; the embedded controller reversely solves the motion speed of the wheels through the motion model of the mobile robot, and then the PID algorithm is applied to carry out closed-loop control on the rotation speed of the wheels.
Further, the mobile robot chassis is provided with three omni wheels. Preferably, the three omni-directional wheels are distributed according to an equilateral triangle.
The control method of the autonomous navigation device of the omnidirectional mobile robot based on the laser range finder comprises the following steps:
s1, synchronous positioning and map construction;
s2, path planning;
s3, position tracking and autonomous obstacle avoidance.
The device comprises three main technical modules of synchronous positioning and mapping, path planning and autonomous obstacle avoidance. The synchronous positioning and mapping adopts an ICP-SLAM algorithm, firstly, an odometer and an inertial sensor of the mobile robot are utilized, the pose information of the mobile robot is estimated by combining a kinematic model of the robot, then, the ICP algorithm is used for comparing laser point cloud information with map information, the pose is corrected, and the map information is updated. The global path planning module sets a target position under the condition that a map and an initial position are known, and searches an optimized feasible path on the premise that the robot is not collided with obstacles such as a wall and the like by using an RRT algorithm. The autonomous obstacle avoidance module obtains accurate positioning information by using an ICP algorithm, calculates expected movement speed according to a planned global path, judges an obstacle in the advancing direction by utilizing laser point cloud information, and dynamically corrects the expected movement speed of the robot by local path planning so as to realize autonomous obstacle avoidance of the robot in advancing.
The invention provides a complete solution to the autonomous navigation problem of a mobile robot in an unknown indoor environment based on a laser range finder, and particularly comprises simultaneous positioning and mapping, path planning, position tracking and obstacle avoidance algorithms. Firstly, pose information of a mobile robot is acquired through an encoder and an inertial measurement unit, environment information is acquired through a laser range finder, and an ICP-SLAM algorithm is used for synchronous positioning and mapping. And then planning a global path by using an RRT algorithm according to the target task, and designing an obstacle avoidance strategy according to the real-time position and the environmental information of the mobile robot to make a local path planning.
Compared with the prior art, the beneficial effects are that: the invention designs a global ICP algorithm for matching the currently acquired laser point cloud information with the constructed map and a local CP algorithm for matching the currently acquired laser point cloud information with the laser point cloud information at the last moment, and corrects the pose information of the mobile robot by combining the global ICP algorithm and the local ICP algorithm, thereby improving the accuracy of pose estimation and reducing the situation of matching failure.
A position tracking algorithm comprising an inner ring and an outer ring is designed, wherein the inner ring uses an encoder and an inertia measurement unit to estimate pose information of the mobile robot; and in the outer ring part, the pose information of the robot is further corrected by utilizing the pose information estimated by the inner ring and combining the laser point cloud and a known map. Based on the position tracking strategy of the inner ring and the outer ring, the real-time performance of the system is improved while the accurate positioning is ensured, and the control delay caused by time consumption of calculation is avoided.
Aiming at four different conditions of the distance between the mobile robot and the obstacle, corresponding obstacle avoidance strategies are respectively designed, so that the mobile robot can advance along the shortest path as far as possible while avoiding the obstacle.
Drawings
Fig. 1 is a block diagram of a mobile robotic system.
Fig. 2 is a schematic diagram of a kinematic model of a three-wheeled omnidirectional mobile robot.
FIG. 3 is a flow chart of ICP-SLAM algorithm.
FIG. 4 is a flow chart of a position tracking and autonomous obstacle avoidance algorithm.
Fig. 5 is a schematic diagram of an obstacle avoidance algorithm.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent; for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationship depicted in the drawings is for illustrative purposes only and is not to be construed as limiting the present patent.
As shown in fig. 1, the autonomous navigation device of the omnidirectional mobile robot based on the laser range finder comprises an encoder for measuring the moving distance of wheels of the mobile robot, and an inertial measurement unit for measuring the rotation angular velocity and acceleration of the mobile robot, wherein the encoder and the inertial measurement unit are respectively connected with a controller, the autonomous navigation device further comprises the laser range finder, the laser range finder is connected with an upper computer, the upper computer is connected with the controller, and the controller is connected with an execution unit.
In the embodiment, a vehicle-mounted encoder measures the moving distance of wheels of the mobile robot, an inertial measurement unit measures the rotation angular velocity and the acceleration of the mobile robot, a vehicle-mounted embedded controller acquires the measured values of the encoder and the inertial measurement unit, and calculates pose information of the mobile robot by combining with a kinematic model of the mobile robot and sends the pose information to an upper computer system; the upper computer system simultaneously acquires laser point cloud information of the laser range finder, calculates an expected value (comprising translation speed and rotation speed) of the movement speed of the mobile robot through an algorithm, and sends the expected value of the movement speed to the vehicle-mounted embedded controller; the embedded controller reversely solves the motion speed of the wheels through the motion model of the mobile robot, and then the PID algorithm is applied to carry out closed-loop control on the rotation speed of the wheels.
As shown in fig. 2, the chassis of the mobile robot is formed by distributing three omni-directional wheels according to an equilateral triangle, and the full-range can be realized by controlling the rotation speeds of the three omni-directional wheelsAnd 360 degrees of omnibearing translation and rotation of the mobile robot on a plane are realized. The motion of the omnidirectional mobile robot is divided into a rotation part and a translation part. The speeds of the three wheels are respectively (v) 1 ,v 2 ,v 3 ) The moving speed of the moving robot body coordinate system along the x-axis and the y-axis is (v) x ,v y ). Under the condition that only translation is not rotated, the kinematic equation of the omnidirectional mobile robot is as follows:
when there is only rotation and no translation, the rotation angular velocity is w, then there is:
v 1 ＝v 2 ＝v 3 ＝L*w. (2)
when both rotation and translation are present, the speed of each omni-wheel is the superposition of the speeds of translation and rotational movement. If the movement speed of each omni wheel is known, the translation speed and the rotation speed of the mobile robot can be deduced. Due to strong coupling of translational and rotational movements, the robot needs to do not perform translational and rotational movements at the same time as much as possible in the control process, so that the accuracy of pose estimation of the mobile robot is improved.
The synchronous positioning and map construction module is shown in fig. 3, obtains the movement information of the mobile robot according to the airborne odometer and the inertial sensor, and predicts the pose information of the mobile robot by combining with the kinematic model. And acquiring point cloud information around the estimated position in the current existing map, taking the point cloud information as a virtual model point set, and simultaneously taking real point cloud information of the current environment acquired by a laser range finder as a data point set, and performing ICP algorithm matching on the point cloud information and the data point set to obtain a global correction value of the pose information. And on the other hand, performing ICP matching by using the laser point cloud information acquired last time and the point cloud information acquired currently to obtain a local correction value of the pose information. And weighting the two groups of correction values according to the matched error values to obtain final correction pose information. And updating map information and adding the current laser observation value.
The iterative closest point algorithm is a core algorithm of the synchronous positioning and map construction moduleFinding the closest point between the data point set and the model point set through an iterative loop to form a matching pair, and solving a rotation matrix R of the data point set relative to the model point set k And translation vector T k So that in each iteration the similarity measure S LTF Minimum. ICP and its various improvements can be categorized into the following steps: 1. a subset of the set of data points P and the set of model points M is selected. 2. Find matching point set pairs. 3 weight each matching pair. 4. Some matching pairs are rejected. 5 find geometry transformation minimizing similarity metric S LTF . The method comprises the following specific steps:
1: initializing a rotation matrix R 0 And translation vector T 0 Randomly selected or obtained by other conditions.
2: finding the nearest point in the model point set for each point of the data point set P to form a matching pair, and simultaneously removing the wrong matching pair to form a matching point set pair:
N＝{(i,j)|x i ∈P,y j ∈M,y j ＝argmin(||(R k - 1 x i +T k - 1 -y j )|| 2 )}. (3)
the sum of the squares of the matching distances is
3: finding the optimal geometric transformation (i.e. rotation matrix R k And translation vector T k ) The sum of the squares of the distances (i.e. the similarity measure) of the transformed set of data points P' and the set of model points M is minimized:
4: and (2) if the stopping condition is met or the iteration times reach the upper limit value and match, otherwise, returning to the step (2). ICP (inductively coupled plasma)There are two types of stopping conditions for the algorithm: (1) Standard deviation e=s LTS /|N p The I is small enough; (2) The standard deviation variation |e' |/e of the two preceding and succeeding times is sufficiently small.
In step 2, the method for finding the closest point is commonly used with a closest point search algorithm based on a k-d tree and on Delaunay triangulation. Experimental research results show that the k-d tree is suitable for point search of a high-dimensional point set, and the Delaunay triangulation algorithm is more suitable for a low-dimensional point set, so that the invention considers the use of the nearest point search algorithm based on Delaunay triangulation. The algorithm divides the scatter set into non-uniform triangular meshes, and the meshes meet two basic criteria, namely, the null circle characteristic is that no other points exist in the circumscribed circle range of any one triangular mesh in the Delaunay triangular mesh. The second is to maximize the minimum angle characteristic, i.e. the minimum angle of the triangle formed by Delaunay triangulation among the triangulation that may be formed by the set of points. In the ICP algorithm, the model point set M is firstly subjected to Delaunay triangulation, and then the closest point of the data point set P in the model point set is found by adopting a triangulation search strategy to serve as a corresponding point, so that a matching pair is formed.
Ideally, the data point set P and the model point set M can be completely matched, if one point x in the data point set p Find the corresponding point y in the model point set M pm Then model point is concentrated y pm The corresponding point found in the set of data points should also be x p . And can be used as a judging criterion of matching pairs. Let the data point concentrate P x1 The corresponding point in the model point set is M y1 Model point set M y1 Corresponding point in the data point set is P x2 ，P x2 Corresponding point of (2) is M y2 . When the following relationship is satisfied, we consider that the search of the corresponding point is correct, otherwise, the corresponding relationship is eliminated.
Where ε is a constant related to the standard deviation.
The common methods for searching the optimal geometric transformation in the step 3 include SVD singular value decomposition, quadruple, orthogonal matrix, double quadruple method and the like, and the scheme adopts the SVD decomposition method.
In the synchronous positioning and mapping process, the estimated pose information of the mobile robot is used as the initial estimation of the ICP algorithm, and the estimated value and the true value have only small errors, so that the iteration times of the ICP algorithm can be greatly reduced, and the convergence rate is improved. Meanwhile, point cloud information around the estimated mobile robot position on the existing map is used as a model point set, so that the size of the model point set is further reduced, and the ICP algorithm calculation speed is improved.
In a known environment map and initial position P s In the case of (1), the target position P is set e Global path planning is performed using a fast extended random tree (RRT) algorithm. The RRT algorithm takes an initial point in a state space as a root node, gradually increases leaf nodes in a random sampling expansion mode, and finally generates a random expansion tree. When the leaf nodes of the random tree include the target points or points in the target area, a path from the initial point to the target point, which is composed of the leaf nodes of the random tree, is the planned path. The planned path is given in the form of a series of discrete nodes, no obstacle exists between adjacent nodes, and the target position point can be directly reached according to the planned path. It is considered that in a real environment, the mobile robot cannot move exactly according to the planned path due to the inertia of the mobile robot movement and the presence of unknown obstacles. In the planning path given in the node form, the mobile robot can take each node as a sub-moving target, and carry out local path planning according to the actual situation, so that the situation that the global path needs to be planned again due to the reasons of deviation track or unknown obstacle is reduced, and the robustness of the navigation process is improved.
In consideration of the size of the mobile robot, the environment map is first expanded before a path is found using the RRT algorithm, and the expanded region is called a collision region S, thereby transferring the size of the mobile robot into an obstacle. During the RRT algorithm, the mobile robot is considered as a particle process, and when the particles representing the mobile robot are located in the collision area, the mobile robot has collided with an obstacle in the actual environment.
The RRT algorithm assumes that the search tree is T, the current node number is N, and the node is ti j (t j ) J is the current node number, i is its parent node number. The initial position is taken as the root node t of the search tree 01 The line segment formed by two points on the map is L (p 1 ,p 2 ). A random point p is generated in the map blank area (i.e., no obstacle is hit when the mobile robot is located at the random point). Finding the node p closest to the random point and capable of being directly communicated in the existing random tree k I.e. there is no obstruction between the node and the random point and no collision zone.
The node is used as a father node, a search tree is expanded along the connection line direction of the father node and the random point, and a new child node t is generated k(N+1) And the distance between the child node and the parent node is the step size s.
t k(N+1) ＝{t|t∈L(t k ,p),||t-t k ||＝s}. (9)
Generating a random point extended random tree continuously until the child node of the random tree reaches the target point, and backtracking from the child node and the parent node to obtain the found path path= (p) 1 ,p 2 ,...,p n ) N is the number of nodes of the path.
The path formed by a series of nodes is obtained through the RRT algorithm, but is often not an optimal path, and the non-adjacent nodes have the possibility of reaching directly, so that the path can be further optimized. The optimization steps are as follows:
starting from the initial node, judging whether the node i is communicated with the node i+2 or not, namely whether the mobile robot can directly move from the node i to the node i+2; if yes, removing the node i+1, and judging the node i and the node i+3 in the next step; if the nodes cannot be directly communicated, the node i+1 and the node i+3 are judged in the next step. And so on. Similarly, the determination can also be started from the target point position at the same time.
The module comprises two parts, namely position tracking and obstacle avoidance. The position tracking obtains accurate pose information as input of an obstacle avoidance algorithm, the obstacle avoidance algorithm calculates expected moving speed according to a planned path and current pose information, corrects the expected moving speed by combining laser sensor information, and finally sends the corrected moving speed to the mobile robot. As shown in fig. 4.
The tracking and positioning process is similar to the positioning and mapping process, the pose of the mobile robot is estimated by using an odometer and an inertial sensor, and the pose information is corrected by using an ICP algorithm. Since the laser information acquisition frequency is only 10HZ at maximum, the icp algorithm calculates the frequency to be 3HZ at worst. In order to ensure real-time control of the robot, a tracking algorithm of an inner ring and an outer ring is designed. The inner ring part takes pose information estimated by dead reckoning as input of an obstacle avoidance algorithm part, the control frequency is 15Hz, the outer ring corrects the pose by using an ICP algorithm, the corrected pose information is taken as input of the obstacle avoidance algorithm part, and the lowest control frequency is 3HZ. As shown in fig. 3, the estimated pose of the mobile robot is obtained at time t, including the positions of the X-axis and the y-axis in the world coordinates and the yaw angle X of the mobile robot t ＝(x t ,y t ,α t ) Simultaneously, the acquired laser information and the information of the known map are matched through an ICP algorithm, and the pose X is estimated at the moment t As input to the obstacle avoidance algorithm. At the time t+1, t+2, … and t+k-1, the pose information X estimated at the current time is estimated t+1 ,X t+2 ,...,X t+k-1 As input to the obstacle avoidance algorithm. The pose of the mobile robot estimated at the time t+k is X t+k At this time, matching is completed by an ICP algorithm which starts to operate at the moment t, and the corrected pose at the moment t is obtainedConsidering the sampling frequency of the laser range finder, the time interval between the time t and the time t+k is set to be more than 0.1s. Thereby moving the robotThe correction pose of (2) is:
pose we will correctAnd as the input of the obstacle avoidance algorithm, simultaneously acquiring laser information at the time t+k, and matching the ICP algorithm again according to the steps so as to correct the pose information of the robot.
As shown in fig. 4, after pose information of the mobile robot is obtained, the obstacle avoidance algorithm performs local path planning according to real-time environment information. Firstly, determining a current moving target, wherein each node is a sub-moving target because the planning path is given in the form of a node, and when the current moving target reaches the vicinity of the current moving target node or the position of the mobile robot is judged to move to the next node along a straight line beyond the current moving target node, switching the target node, namely taking the next path node as the target node. And then calculating the expected motion direction of the mobile robot according to the current position and the target node position, correcting the motion direction by using an obstacle avoidance algorithm, and transmitting the calculated translation and rotation speed to the mobile robot to control the motion of the mobile robot.
The RRT algorithm plans a feasible path, but in the actual control process, the mobile robot does not completely move according to the expected path due to the kinematic characteristics of the mobile robot, unknown obstacles exist in the environment and other reasons, and local path planning is required to be performed according to the current real-time pose information, the moving speed and the environment information so as to avoid the obstacles and approach the target.
The local path planning algorithm is shown in fig. 5. The safe distance is set to d. Let the current position be S, the target position be E, and the ideal motion direction be V SE . The predicted position P in the next control period is predicted using the above information. The obstacle avoidance process is divided into the following four cases:
(1) As shown in fig. 5 (a), the point closest to the current position S in the obstacle is O1, and if the distance SO1 between the two points is smaller than d, it indicates that the mobile robot is about to collide, and at this time, the mobile robot moves in a direction away from the point O1 regardless of the position of the target point.
(2) If SO1 is greater than d, then the obstacle point O closest to the predicted position P is found, and the obstacle region VO is established with the point O as the center and d as the radius, i.e., the hatched portion in fig. 5 (b). If the desired direction of movement V SE If the robot does not fall into the area, the robot will not collide with the point, and the expected speed can be kept unchanged.
(3) As shown in FIG. 5 (c), if V SE In the obstacle region VO, the mobile robot collides for a limited time, and the speed direction needs to be corrected. At this time, if the point P is located outside the obstacle region, and the tangential point T between the point S and the circle VO is found, the final movement direction will be the tangential direction:
(4) As shown in FIG. 5 (d), if the point P is located within the obstacle region VO, the velocity V SE Is the correction of (2)The direction, corrected speed is:
because the measuring range of the used laser range finder is 240 degrees, when the final advancing direction of the robot has a larger phase difference with the current direction, the robot is made to perform rotary motion, so that the mobile robot can fully measure the environmental information in the advancing direction, and collision caused by no detection of an obstacle is avoided. Under other conditions, the mobile robot is controlled to only do translational motion, so that the increase of positioning errors caused by the simultaneous occurrence of rotation and translational motion is avoided.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (4)
1. The control method of the autonomous navigation device of the omnidirectional mobile robot based on the laser range finder is characterized in that the autonomous navigation device comprises an encoder for measuring the moving distance of wheels of the mobile robot, an inertial measurement unit for measuring the rotation angular velocity and the acceleration of the mobile robot, the encoder and the inertial measurement unit are respectively connected with a controller, the control method further comprises the laser range finder, the laser range finder is connected with an upper computer, the upper computer is connected with the controller, and the controller is connected with an execution unit;
the chassis of the mobile robot is provided with three omnidirectional wheels;
the three omni-directional wheels are distributed according to an equilateral triangle;
the control method of the autonomous navigation device of the omnidirectional mobile robot based on the laser range finder comprises the following steps:
s1, synchronous positioning and map construction;
s2, path planning;
s3, position tracking and autonomous obstacle avoidance;
in the step S3, accurate pose information is obtained through position tracking and is used as input of an obstacle avoidance algorithm, the obstacle avoidance algorithm calculates expected moving speed according to a planned path and current pose information, the expected moving speed is corrected by combining laser sensor information, and finally the corrected moving speed is sent to the mobile robot;
the tracking and positioning process is similar to the positioning and mapping process, the pose of the mobile robot is estimated by using an odometer and an inertial sensor, and the pose information is corrected by using an ICP algorithm; since the acquisition frequency of the laser information is only 10HZ at maximum, the ICP algorithm calculates the frequency at worstThe rate is 3HZ; in order to ensure real-time control of the robot, a tracking algorithm of an inner ring and an outer ring is designed; the inner ring part takes pose information estimated by a dead reckoning method as input of an obstacle avoidance algorithm part, the control frequency is 15Hz, the outer ring corrects the pose by using an ICP algorithm, the corrected pose information is taken as input of the obstacle avoidance algorithm part, and the lowest control frequency is 3HZ; obtaining the estimated pose of the mobile robot at the time t, including the positions of the X axis and the y axis in the world coordinate and the yaw angle X of the mobile robot t ＝(x t ,y t ,α t ) Simultaneously, the acquired laser information and the information of the known map are matched through an ICP algorithm, and the pose X is estimated at the moment t As input to the obstacle avoidance algorithm; at the time t+1, t+2, … and t+k-1, the pose information X estimated at the current time is estimated t+1 ,X t+2 ,...,X t+k-1 As input to the obstacle avoidance algorithm; the pose of the mobile robot estimated at the time t+k is X t+k At this time, matching is completed by an ICP algorithm which starts to operate at the moment t, and the corrected pose at the moment t is obtainedConsidering the sampling frequency of the laser range finder, setting the time interval between the time t and the time t+k to be more than 0.1s; the corrected pose of the mobile robot is as follows:
pose to be correctedAs the input of the obstacle avoidance algorithm, laser information at the time of t+k is obtained at the same time, matching of the ICP algorithm is carried out again according to the steps so as to correct the pose information of the robot;
after pose information of the mobile robot is acquired, the obstacle avoidance algorithm performs local path planning according to real-time environment information; firstly, determining a current moving target, wherein each node is a sub-moving target because the planning path is given in the form of a node, and when the current moving target reaches the vicinity of the current target node or the position of the mobile robot is judged to move to the next node along a straight line beyond the current target node, switching the target node, namely taking the next path node as the target node; calculating the expected motion direction of the mobile robot according to the current position and the target node position, correcting the motion direction by using an obstacle avoidance algorithm, and transmitting the calculated translation and rotation speed to the mobile robot to control the motion of the mobile robot;
the RRT algorithm plans a feasible path, but in the actual control process, the mobile robot does not completely move according to the expected path due to the kinematic characteristics of the mobile robot and unknown obstacle reasons in the environment, and local path planning is required according to the current real-time pose information, the moving speed and the environment information so as to avoid the obstacle and approach the target;
the local path planning algorithm is specifically implemented as follows:
the safe distance is set to d; let the current position be S, the target position be E, and the ideal motion direction be V SE The method comprises the steps of carrying out a first treatment on the surface of the Predicting a predicted position P in a next control period by using the information; the obstacle avoidance process is divided into the following four cases:
(1) The point closest to the current position S in the obstacle is O1, if the distance SO1 between the obstacle and the point is smaller than d, the collision of the mobile robot is indicated, and the mobile robot moves in a direction away from the point O1 without considering the position of the target point;
(2) If SO1 is larger than d, finding out the obstacle point O nearest to the predicted position P, taking the point O as the center of a circle, d as the radius to establish an obstacle region VO, if the expected movement direction V SE If the robot does not fall into the area, the robot is indicated not to collide with the point, and the expected speed can be kept unchanged;
(3) If V SE The robot falls in the obstacle region VO, and the mobile robot collides in a limited time, so that the speed direction needs to be corrected; at this time, if the point P is located outside the obstacle region, and the tangential point T between the point S and the circle VO is found, the final movement direction will be the tangential direction:
(4) If the point P is located in the obstacle region VO, the speed V SE Is the correction of (2)The direction, corrected speed is:
because the measuring range of the used laser range finder is 240 degrees, when the final advancing direction of the robot has a larger phase difference with the current direction, the robot is made to perform rotary motion, so that the mobile robot can fully measure the environmental information in the advancing direction, and collision caused by no detection of an obstacle is avoided; under other conditions, the mobile robot is controlled to only do translational motion, so that the increase of positioning errors caused by the simultaneous occurrence of rotation and translational motion is avoided.
2. The control method of the autonomous navigation device of the omnidirectional mobile robot based on the laser rangefinder according to claim 1, characterized in that: in the step S1, the movement information of the mobile robot is obtained according to the encoder and the inertia measurement unit, and the pose information of the mobile robot is estimated by combining a kinematic model; acquiring point cloud information around the estimated position in the current existing map, taking the point cloud information as a virtual model point set, and simultaneously taking real point cloud information of the current environment acquired by a laser range finder as a data point set, and performing ICP algorithm matching on the point cloud information and the data point set to obtain a pose information global correction value; on the other hand, the last acquired laser point cloud information and the current acquired point cloud information are used for ICP matching to obtain a local correction value of the pose information; weighting the two groups of correction values according to the matched error values to obtain final correction pose information; and updating map information and adding the current laser observation value.
3. The control method of the autonomous navigation device of the omnidirectional mobile robot based on the laser rangefinder according to claim 2, characterized in that: in the step S1 described above, a step of,
the iterative nearest point algorithm is a core algorithm of the synchronous positioning and map construction module, and finds the nearest point of the data point set and the model point set through an iterative loop to form a matching pair, and solves a rotation matrix R of the data point set relative to the model point set k And translation vector T k So that in each iteration the similarity measure S LTF Minimum; ICP and its various improvements can be categorized into the following steps: 1. selecting a subset of the data point set P and the model point set M; 2. searching a matching point set pair; 3, giving weight to each matching pair; 4. rejecting certain matching pairs; 5 find geometry transformation minimizing similarity metric S LTF The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following specific steps:
1: initializing a rotation matrix R 0 And translation vector T 0 Randomly selecting or obtaining through other conditions;
2: finding the nearest point in the model point set for each point of the data point set P to form a matching pair, and simultaneously removing the wrong matching pair to form a matching point set pair:
N＝{(i,j)|x i ∈P,y j ∈M,y j ＝argmin(||(R k-1 x i +T k-1 -y j )|| 2 )}. (3)
the sum of the squares of the matching distances is
3: finding the optimal geometric transformation (i.e. rotation matrix R k And translation vector T k ) The sum of the squares of the distances (i.e. the similarity measure) of the transformed set of data points P' and the set of model points M is minimized:
4: if the stopping condition is met or the iteration times reach the upper limit value and match, otherwise, returning to the step 2, wherein the stopping condition of the ICP algorithm is two: (1) Standard deviation e=s LTS /|N p The I is small enough; (2) The standard deviation change |e-e' |/e of the front and rear two times is small enough;
in the step 2, the method for searching the closest point uses a closest point searching algorithm based on Delaunay triangulation; the algorithm divides the scattered point set into uneven triangular grids, and the grids meet two basic criteria, namely, the null circle characteristic is that no other points exist in the circumscribed circle range of any triangular grid in the Delaunay triangular grid; the second is to maximize the minimum angle characteristic, namely, the minimum angle of the triangle formed by Delaunay triangulation is the largest in the triangulation possibly formed by the scattered point set; in the ICP algorithm, firstly performing Delaunary triangulation on a model point set M, and then adopting a triangulation search strategy to find the nearest point of a data point set P in the model point set as a corresponding point to form a matching pair;
ideally, the data point set P and the model point set M can be completely matched, if one point x in the data point set p Find the corresponding point y in the model point set M pm Then model point is concentrated y pm The corresponding point found in the set of data points should also be x p The method comprises the steps of carrying out a first treatment on the surface of the Can be used as the judgment criterion of matching pairs in turn; let the data point concentrate P x1 The corresponding point in the model point set is M y1 Model point set M y1 Corresponding point in the data point set is P x2 ，P x2 Corresponding point of (2) is M y2 The method comprises the steps of carrying out a first treatment on the surface of the When the following relation is satisfied, searching the corresponding point is correct, otherwise, eliminating the corresponding relation;
wherein ε is a constant related to the standard deviation;
the best geometrical transformation method is SVD decomposition method.
4. The control method of the autonomous navigation device of the omnidirectional mobile robot based on the laser rangefinder according to claim 1, characterized in that: in the step S2 described above, a step of,
in a known environment map and initial position P s In the case of (1), the target position P is set e Performing global path planning by using a fast-expansion random tree algorithm; the RRT algorithm takes an initial point in a state space as a root node, gradually increases leaf nodes in a random sampling expansion mode, and finally generates a random expansion tree; when the leaf nodes of the random tree contain the target points or the points in the target area, a path between the initial point and the target point, which is formed by the leaf nodes of the random tree, is a planning path; the planned path is given in a series of discrete node forms, no barrier exists between adjacent nodes, and the planned path can directly reach the target position point; considering that in a practical environment, the mobile robot cannot move strictly according to a planned path due to the motion inertia of the mobile robot and the existence of unknown obstacles; in the planning paths given in the node form, the mobile robot can take each node as a sub-moving target, and carry out local path planning according to the actual situation, so that the situation that the global path needs to be planned again due to the reasons of deviating tracks or unknown obstacles is reduced, and the robustness of the navigation process is improved;
taking the size of the volume of the mobile robot into consideration, performing expansion processing on the environment map before searching a path by using an RRT algorithm, wherein the expanded area is called a collision area S, so that the volume of the mobile robot is transferred into an obstacle; in the RRT algorithm process, the mobile robot is regarded as particle processing, and when particles representing the mobile robot are positioned in a collision area, the mobile robot collides with an obstacle in the actual environment;
RRT algorithm hypothesis search treeT, N is the current node number, T is the node ij (t j ) J is the number of the current node, i is the number of the father node; the initial position is taken as the root node t of the search tree 01 The line segment formed by two points on the map is L (p 1 ,p 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Generating a random point p in the map blank area (i.e., not hitting an obstacle when the mobile robot is located at the random point); finding the node p closest to the random point and capable of being directly communicated in the existing random tree k I.e. no obstacle shielding and no collision area between the nodes and the random points;
the node is used as a father node, a search tree is expanded along the connection line direction of the father node and the random point, and a new child node t is generated k(N+1) The distance between the child node and the father node is the step length s;
t k(N+1) ＝{t|t∈L(t k ,p),||t-t k ||＝s}. (9)
generating a random point extended random tree continuously until the child node of the random tree reaches the target point, and backtracking from the child node and the parent node to obtain the found path path= (p) 1 ,p 2 ,...,p n ) N is the number of nodes of the path;
the path formed by a series of nodes is obtained through the RRT algorithm, but is often not an optimal path, and the non-adjacent nodes have the possibility of direct arrival and can be further optimized; the optimization steps are as follows:
starting from the initial node, judging whether the node i is communicated with the node i+2 or not, namely whether the mobile robot can directly move from the node i to the node i+2; if yes, removing the node i+1, and judging the node i and the node i+3 in the next step; if the nodes cannot be directly communicated, judging the node i+1 and the node i+3 in the next step; and so on; similarly, the determination can also be started from the target point position at the same time.
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