JP6567998B2 - Control method - Google Patents

Description

  The present invention relates to a control method for controlling a movable part using a statistical model.

  A control method for controlling a movable part using a statistical model (such as a hidden Markov model) constructed based on position information (such as a joint angle of a robot) of a moving part is known (for example, see Patent Document 1). .

JP 2004-330361 A

In the above control method, a statistical model is constructed based on position information. For this reason, for example, when the positional relationship between the movable part and the operation target changes between the statistical model construction and the actual control of the movable part, there is an excessive amount between the movable part and the operation target. There is a possibility that a force is generated or the movable part cannot contact the operation target.
The present invention has been made to solve such a problem, and even when the positional relationship between the movable part and the operation target is different between the statistical model construction and the control of the movable part, the movable part and the operation target It is a main object to provide a control method that can suppress a situation in which an excessive force is generated during this period or a movable part cannot contact an operation target.

In order to achieve the above object, one embodiment of the present invention provides:
A model constructed based on the position or speed information of the movable part to be operated and the force information for the movable part, and a transition probability between the nodes is set, and each node has a distribution of the position or speed information and the force information. Sampling the position or speed information and force information of the movable part at time t (t is a natural number), and sampling the target position information or target speed information of the movable part at time t + 1 based on
Calculating target position information or target speed information of the movable part and target acceleration information at time t + 1 based on the sampled target position information or target speed information;
Performing reverse dynamics calculation based on the calculated target position information, target speed information and target acceleration information at time t + 1 to calculate target force information of the movable part at time t + 1;
Calculating a probability that the calculated target position information and target force information at time t + 1 are generated from the statistical model based on the position information and force information at time t and the statistical model;
Calculating expected values of target position information and target force information at time t + 1 based on the calculated target position information and target force information at time t + 1 and the calculated probability;
Controlling the movable part based on the calculated target position information and expected value of the target force information;
A control method characterized by comprising:
According to this aspect, since the consistency between the position command value and the force command value of the movable part can be taken, the positional relationship between the movable part and the operation target is different between the statistical model construction and the movable part control. Even in such a case, it is possible to suppress a situation in which an excessive force is generated between the movable part and the operation target or the movable part cannot contact the operation target.

  According to the present invention, an excessive force is generated between the movable unit and the operation target even when the positional relationship between the movable unit and the operation target is different between the statistical model construction and the control of the movable unit, or It is possible to provide a control method capable of suppressing a situation in which the movable part cannot contact the operation target.

It is a figure which shows schematic structure of the robot arm which concerns on Embodiment 1 of this invention. 1 is a block diagram showing a schematic system configuration of a robot arm and a control device according to Embodiment 1 of the present invention. It is a figure which shows an example of a hidden Markov model. It is a figure which shows the target trajectory generation and robot arm control of each joint part. It is a flowchart which shows the flow of the control method which concerns on Embodiment 1 of this invention.

Embodiments of the present invention will be described below with reference to the drawings.
Embodiment 1
FIG. 1 is a diagram showing a schematic configuration of a robot arm according to the first embodiment of the present invention. The control device according to the first embodiment controls, for example, an articulated robot arm (one specific example of a movable part) 2.

  The robot arm 2 includes a plurality of links 21, joint portions (a wrist joint, an elbow joint, a shoulder joint, etc.) 22 that rotatably connect the links 21, and an end effector 23 that is provided at the tip of the link arm 21 and operates an operation target. And having.

  FIG. 2 is a block diagram illustrating a schematic system configuration of the robot arm and the control device according to the first embodiment of the present invention. Each joint unit 22 includes an angle sensor 24 such as an encoder that detects a joint angle (one specific example of position information) of each joint unit 22, an actuator 25 such as a servo motor that drives each joint unit 22, and each joint And a force sensor 26 that detects an operation force of the unit 22.

  The force sensor 26 is, for example, a torque sensor that detects joint torque (one specific example of force information) of each joint portion 22. Each joint portion 22 is provided with a speed reduction mechanism. The end effector 23 applies an operating force to the object, for example, by gripping or contacting the object. The end effector 23 is provided with an actuator 25 that drives the end effector 23 and a force sensor 26 that detects an operation force of the end effector 23.

  The control device 1, for example, based on the angle information (joint angle etc.) from the angle sensor 24 of each joint portion 22 and the operation force from the force sensor 26, the actuator 25 of each joint portion 22 and the end effector 23. By controlling the above, the robot arm 2 is feedback-controlled.

  The control device 1 includes, for example, a CPU (Central Processing Unit) 1a that performs arithmetic processing and the like, a ROM (Read Only Memory) and a RAM (Random Access Memory) that store arithmetic programs executed by the CPU 1a, control programs, and the like. Each of the hardware components includes a microcomputer 1 including a memory 1b and an interface unit (I / F) 1c for inputting / outputting signals to / from the outside. The CPU 1a, the memory 1b, and the interface unit 1c are connected to each other via a data bus 1d.

  The control device 1 according to the first embodiment performs, for example, a table wiping by the robot arm 2 by imitating a human motion using a statistical model such as a Hidden Markov Model (HMM). Perform physical tasks with force.

  By the way, in order for the robot arm to execute the physical tasks as described above, the control device, in addition to the robot arm position information (joint angle and hand position), the robot arm force information (joint torque and hand reaction force) Need to learn about. In this case, the following problems arise when the control device simply learns the force information, just like the position information of the robot arm. For example, if the positional relationship between the robot arm and the operation target changes during learning and control (such as when the height of the table changes), the robot arm and the operation target are excessively large. There is a possibility that a force is generated or the robot arm cannot contact the operation target.

  On the other hand, the control device 1 according to the first embodiment ensures dynamic consistency using a dynamic equation described later when the target trajectory of the robot arm 2 is generated by the hidden Markov model.

  The control device 1 executes learning processing for learning based on data prepared in advance to construct a hidden Markov model, and execution processing for controlling the robot arm 2 using the hidden Markov model constructed by the learning processing. .

(Learning process)
In the learning process, for example, the control device 1 performs learning using time series data (simulation data, etc.) of joint angles and joint torques of the joint portions 22 of the robot arm 2 prepared in advance, and uses the Baum-Welch method. Build a left-right hidden Markov model (Figure 3).

  The control device 1 actually operates the robot arm 2 remotely, and at this time, the time series of the joint angle and joint torque of each joint portion 22 of the robot arm 2 detected by the angle sensor 24 and the force sensor 26. Learning may be performed using data to construct a hidden Markov model.

  As shown in FIG. 3, in the hidden Markov model, each node q and a transition probability a for transitioning between the nodes q are set. Each node q has a distribution of a joint angle θ and a joint torque τ of each joint portion 22. The control device 1 uses the time series data of the joint angles and joint torques of the joint portions 22 described above, the transition probability a between the nodes, the parameters representing the distribution of the joint angles θ and joint torques τ of the joint portions 22, Construct a hidden Markov model.

  Thus, in the learning process, a hidden Markov model is constructed using the joint angle and joint torque of the robot arm 2. Thereby, time series data including the correlation between the joint angle of the robot arm 2 and the joint torque can be stored, and both the position command and the force command for the robot arm 2 to perform the contact operation of the operation target can be generated simultaneously. . It is also possible to recognize the movement of the robot arm 2 while compressing the information amount of the data.

(Execution process)
In this execution process, the control device 1 simultaneously performs target trajectory generation and robot arm control of each joint portion 22 as shown in FIG. Further, the controller 1 controls the target position information θ of the robot arm 2 at time t + 1, which is the next moment, based on the sensor value at the current time t detected by the force sensor 26 and the angle sensor 24 during the robot arm control. _plan and target force information τ _plan are determined, and compliance control of the robot arm 2 is executed based on the target position information θ _plan and the target force information τ _plan . Thereby, a command value greatly deviating from the current state of the robot arm 2 is generated, and a situation in which the robot arm 2 operates rapidly can be avoided. Furthermore, even when the position command and the force command of the robot arm 2 cannot be strictly met at the same time, the position and force of the robot arm 2 are balanced in accordance with preset parameters using the target position information and the target force information. It can be controlled properly.

  The control device 1 generates the target trajectory of the joint unit 22 by repeating the following [prediction] (1) to (8) and [resampling] at predetermined time intervals.

[prediction]
(1) The control apparatus 1, first, the node q _t hidden Markov model at the current time t, and joint angle theta _t and joint torque tau _t at the current time t, which is detected by the angle sensor 24 and force sensor 26, the Hold.

(2) The control apparatus 1 samples the node q _{t + 1} at time t + 1, which is the next moment, using the transition probability a between the nodes of the hidden Markov model.

(3) The control device 1 determines the target joint angle of the joint portion 22 at time t + 1 (one specific example of target position information) based on the distribution of joint angles of the sampled node q _{t + 1} (output probability at the node q _{t + 1} ). Sampling θ _{t + 1} ^ref .
Thus, the hidden Markov model including the joint angle and joint torque of the joint portion 22 when the target trajectory of the joint portion 22 is generated, the joint angle and joint torque at the current time t detected by the angle sensor 24 and the force sensor 26, Based on this, the target joint angle is sampled.

(4) The control device 1 calculates the target joint angular velocity (one specific example of target speed information) θ (dot) _{t + 1} ^ref of the joint unit 22 by first-order differentiation of the sampled target joint angle θ _{t + 1} ^ref , and the target joint The angle θ _{t + 1} ^ref is second-order differentiated to calculate a target joint angular acceleration (one specific example of target acceleration information) θ (2 dots) _{t + 1} ^ref of the joint unit 22. In addition, as described above, one dot symbol added to the upper part of θ (first-order differential value) is expressed as θ (dot), and two dot symbols added to the upper part of θ (second-order differential value) ) Is expressed as θ (2 dots), and the other parameters are also expressed in the same manner.

なお、本実施形態１において、制御装置１は、処理の高速化を図るために、上記(５)による目標関節角加速度θ（２ドット）_ｔ＋１ ^ｒｅｆの修正を行わなくても良い。 (5) The control device 1 corrects the calculated target joint angular acceleration θ (2 dots) _{t + 1} ^ref using the following (Equation 1). In Equation 1 below, _Kp and _Kd are preset coefficient matrices.
This correction can further increase the control stability.

In the first embodiment, the control device 1 does not have to correct the target joint angular acceleration θ (2 dots) _{t + 1} ^ref according to the above (5) in order to increase the processing speed.

(6) The control device 1 calculates the calculated target joint angle θ _{t + 1} ^ref , the target joint angular velocity θ (dot) _{t + 1} ^ref , the corrected target joint angular acceleration θ (2 dots) _{t + 1} ^ref, and the target reaction force F _{t + 1} ^ref The target joint torque τ _{t + 1} is calculated to realize
The target reaction force F _{t + 1} ^ref is set by the user based on the operation content of the robot arm 2. For example, when the robot arm 2 is brought into strong contact with the operation target, the target reaction force is set to 10 [N], and when lightly contacted, it is set to 1 [N].
The control device 1 is based on the calculated target joint angle θ _{t + 1} ^ref , target joint angular velocity θ (dot) _{t + 1} ^ref , corrected target joint angular acceleration θ (2 dots) _{t + 1} ^ref , and target reaction force F _{t + 1} ^ref. Then, the target joint torque (one specific example of the target force information) τ _{t + 1} of the joint portion 22 is calculated using the following inverse dynamic equation (expression of motion) (Expression 2). In the following (Expression 2), M is an inertia matrix, C is a centrifugal and Coriolis term, and G is a gravity term. J is a Jacobian matrix that relates the joint angular velocity to the velocity of the robot arm 2 (movable part).
Thus, by calculating the target joint torque τ _{t + 1} using the following inverse dynamic equation, the mechanical consistency of the robot arm 2 is ensured when the target trajectory of the joint portion 22 is generated by the hidden Markov model. be able to.

(7) The control device 1 determines the target joint angle θ _{t + 1} ^ref based on the joint angle θ _t and the joint torque τ _t at the current time t detected by the angle sensor 24 and the force sensor 26 and the hidden Markov model. , And the target joint torque τ _{t + 1} is generated from the hidden Markov model (q _{t + 1} , θ _{t + 1} ^ref , τ _{t + 1} is reached) P (θ _{t + 1} ^ref , τ _{t + 1} | θ _t , τ _t , λ) is calculated . Thus, from the joint angle and joint torque (sensor value) at the current time t detected by the angle sensor 24 and the force sensor 26 and the hidden Markov model, the target joint angle θ _{t + 1} ^ref and the target joint torque τ _{t + 1 are obtained.} The probability (likelihood) of becoming is calculated.
The control device 1 calculates the probability P for each particle. Here, the control device 1 performs the following correction process so that the sum of the probabilities P of the respective particles is 1. For example, the control device 1 calculates the sum of the probabilities of each particle and divides the probability of each particle by the calculated sum.

(8) The control device 1 uses the following (Equation 3) based on the target joint angle θ _{t + 1} ^ref , the target joint torque τ _{t + 1} and the corrected probability to calculate the target joint angle at the time t + 1, which is the next instantaneous command value. The estimated value θ (hat) _{t + 1} ^ref and the estimated value τ (hat) _{t + 1} of the target joint torque are calculated as the joint angle and the expected value of the joint torque for each particle. In addition, what attached | subjected the hat symbol to the upper part of (theta) and (tau) is each described as (theta) (hat) and (tau) (hat), and hereafter, it describes similarly about another parameter.
Thus, the command value at the next moment is calculated by the weighted average based on the probability calculated in (7) above.

The control device 1 determines the estimated value θ (hat) _{t + 1} ^ref of the target joint angle and the estimated value τ (hat) _{t + 1 of the} target joint torque calculated in the above (8) and each joint portion 22 detected by the angle sensor 24. Based on the joint angle θ, compliance control of each joint portion 22 is performed. The control device 1 uses the estimated value θ (hat) _{t + 1} ^{ref of} the target joint angle and the estimated value τ (hat) _{t + 1} of the target joint torque calculated in the above (8), respectively, as an angle command θ _plan and a torque command τ _plan. Then, the torque command τ _ref is calculated using the following (formula 4). In the following (Equation 4), K and D are preset coefficient matrices.

The control device 1 controls each actuator 25 by transmitting the calculated torque command τ _ref to the actuator 25 of each joint portion 22.
Furthermore, the control device 1 performs feedback control on the actuator 25 of the joint portion 22 so that the joint torque τ detected by the force sensor 26 follows the calculated torque command τ _ref .

  As described above, the target joint angle is determined based on the hidden Markov model including the joint angle and joint torque of the joint portion 22 and the joint angle and joint torque detected by the angle sensor 24 and the force sensor 26 when the target trajectory is generated. Is sampled. Calculate the target joint angular velocity and target joint angular acceleration based on the sampled target joint angle, and calculate the inverse dynamics based on the target joint angle, the calculated target joint angular velocity and target joint angular acceleration, and the target reaction force The target joint torque is calculated to ensure the mechanical consistency of the robot arm 2. From the angle sensor value and the torque sensor value at the current time t and the hidden Markov model, the probability of the target joint angle and the target joint torque is calculated. An expected value of the target joint angle and target joint torque at time t + 1 is calculated by weighted average based on the calculated probability, and the joint portion 22 of the robot arm 2 is controlled using the expected value of the target joint angle and target joint torque.

  Thereby, the consistency between the position command value of the robot arm 2 and the force command value can be obtained. Therefore, when the hidden Markov model is constructed and when the robot arm is controlled, for example, when the size and shape of the operation target are different, or the positional relationship between the robot arm 2 and the operation target (table height is different, etc.) is different. Even in such a case, it is possible to suppress a situation in which an excessive force is generated between the robot arm 2 and the operation target or the robot arm cannot contact the operation target.

[resampling]
Controller 1 normalizes the probability P calculated above, to calculate a probability density distribution p _k.
The control device 1, so that the probability P of each particle, handouts particles according to the probability density distribution p _k, update the particle at time t + 1. Thereby, particles with low probability can be removed, and particles with high probability can be left.
Controller 1 sets the q _t for node q _{t + 1} for the next frame of the selected particle s, and repeats the processing returns to (1).

FIG. 5 is a flowchart showing a flow of the control method according to the first embodiment. Note that the control process shown in FIG. 5 is repeatedly executed at predetermined time intervals, for example.
The control device 1 samples the node q _{t + 1} at time t + 1 (next moment) using the transition probability a between the nodes of the hidden Markov model (step S101).

The control device 1 samples the target joint angle θ _{t + 1} ^ref at time t + 1 based on the joint angle distribution of the sampled node q _{t + 1} (step S102).
Controller 1, a target joint angle theta _t ^{+ 1 ref} sampled by first-order derivative calculates the target joint angular velocity theta _(dot) ^{t + 1 ref,} the target joint angle θ _t ^{+ 1 ref} 2 derivative to a target joint angle acceleration theta ( (2 dots) _{t + 1} ^ref is calculated (step S103).

The control device 1 corrects the calculated target joint angular acceleration θ (2 dots) _{t + 1} ^ref using the above (Equation 1) (step S104).
The control device 1 is based on the calculated target joint angle θ _{t + 1} ^ref , target joint angular velocity θ (dot) _{t + 1} ^ref , corrected target joint angular acceleration θ (2 dots) _{t + 1} ^ref , and target reaction force F _{t + 1} ^ref. Then, the target joint torque τ _{t + 1} is calculated using the inverse dynamics calculation formula (Formula 2) (Step S105).

The control device 1 determines the target joint angle θ _{t + 1} ^ref and the target based on the joint angle θ _t and the joint torque τ _t at the current time t detected by the angle sensor 24 and the force sensor 26 and the hidden Markov model. The probability P (θ _{t + 1} ^ref , τ _{t + 1} | θ _t , τ _t , λ) that the joint torque τ _{t + 1} is generated from the hidden Markov model is calculated (step S106).

The control device 1 uses the above (Equation 3) based on the target joint angle θ _{t + 1} ^ref , the target joint torque τ _{t + 1} and the corrected probability, and uses the target joint angle estimated value θ (hat) _{t + 1} ^ref and the target at time t + 1. The joint torque estimated value τ (hat) _{t + 1} (command value at the next moment) is calculated as the joint angle for each particle and the expected value of the joint torque (step S107).

The control device 1 uses the estimated value θ (hat) _{t + 1} ^ref of the target joint angle and the estimated value τ (hat) _{t + 1 of the} target joint torque calculated in (8) above, and the joints of each joint detected by the angle sensor 24. Based on the angle θ, compliance control of each joint portion 22 is performed (step S108).
The control device 1, so that the probability P of each particle, handouts particles according to the probability density distribution p _k, update the particle at time t + 1 (step S109).

  As described above, in the first embodiment, the target joint angle is sampled based on the hidden Markov model including the joint angle and joint torque of the joint portion 22 and the joint angle and joint torque detected by the angle sensor 24 and the force sensor 26. . Calculate the target joint angular velocity and target joint angular acceleration based on the sampled target joint angle, and calculate the inverse dynamics based on the target joint angle, the calculated target joint angular velocity and target joint angular acceleration, and the target reaction force To calculate the target joint torque. From the angle sensor value and the torque sensor value at the current time t and the hidden Markov model, the probability of the target joint angle and the target joint torque is calculated. An expected value of the target joint angle and target joint torque at time t + 1 is calculated by weighted average based on the calculated probability, and the joint portion 22 of the robot arm 2 is controlled using the expected value of the target joint angle and target joint torque.

  Thereby, the consistency between the position command value of the robot arm 2 and the force command value can be obtained. Therefore, even when the hidden Markov model is constructed and when the robot arm is controlled, for example, when the size and shape of the operation target are different, or when the positional relationship between the robot arm 2 and the operation target is different, the robot arm 2 and the operation are performed. It is possible to suppress a situation in which an excessive force is generated between the object and the robot arm cannot contact the operation object.

Embodiment 2
In Embodiment 2 of the present invention, the control device 1 is based on time-series data of the hand position of the end effector 23 (one specific example of position information) and the hand reaction force of the end effector 23 (one specific example of force information). Hidden Markov models may be constructed. In this case, the control device 1 replaces the joint angles and joint torques of [prediction] (1) to (8) and [resampling] in the first embodiment with the hand position and the hand reaction force, respectively, and performs similar processing. Do.

(Learning process)
In the learning process, for example, the control device 1 performs learning using time series data of hand positions and hand reaction forces of the end effector 23 prepared in advance, and constructs a left-right hidden Markov model by the Baum-Welch method. .

(Execution process)
The control device 1 repeats the following [prediction] (1) to (8) and [resampling] every predetermined time.

[prediction]
(1) The control apparatus 1, first, holds the node q _t hidden Markov model at the current time t, and hand position and hand reaction force at the current time t is detected by the sensor, the (particles).

(2) The control device 1 samples the node q _{t + 1} at time t + 1 (next moment) using the transition probability a between the nodes of the hidden Markov model.

(3) The control device 1 samples the target hand position (one specific example of the target position information) at time t + 1 based on the distribution of the hand position of the sampled node q _{t + 1} .

(4) The control device 1 calculates the target hand speed (one specific example of the target speed information) by performing first-order differentiation on the sampled target hand position, and performs second-order differentiation on the target hand position to obtain the target hand acceleration ( A specific example of target acceleration information) is calculated.

(5) The control device 1 corrects the calculated target hand acceleration in order to improve control stability. In the second embodiment, the control device 1 may not correct the target hand acceleration according to the above (5).

(6) The control device 1 calculates a target hand reaction force (one specific example of target force information) that realizes the calculated target hand position, target hand speed, corrected target hand acceleration, and target reaction force. .

(7) The control device 1 generates the target hand position and the target hand reaction force from the hidden Markov model based on the hand position and hand reaction force at the current time t detected by the sensor and the hidden Markov model. Probability P is calculated.
The control device 1 calculates the probability P for each particle. Here, the control device 1 performs the following correction process so that the sum of the probabilities P of the respective particles is 1. For example, the control device 1 calculates the sum of the probabilities of each particle and divides the probability of each particle by the calculated sum.
Controller 1, the calculated probability P is normalized to calculate the probability density distribution p _k.

(8) The control device 1 calculates the estimated value of the target hand position and the estimated value of the target hand reaction force (command value at the next moment) at time t + 1 based on the target hand position, the target hand reaction force, and the corrected probability. Calculated as the expected value of the hand position and hand reaction force for each particle.
The control device 1 performs compliance control of each joint portion 22 based on the estimated value of the target hand position and the estimated value of the target hand reaction force calculated in (8) above, and the hand position detected by the sensor. .

[resampling]
The control device 1, so that the probability P of each particle, handouts particles according to the probability density distribution p _k, update the particle at time t + 1.

Embodiment 3
In Embodiment 3 of the present invention, the control device 1 constructs a hidden Markov model based on time-series data of the joint angle and joint torque of each joint portion 22 of the robot arm 2 and the hand reaction force of the end effector 23. To do.

In this case, the control device 1 determines the target joint angle θ _{t + 1} ^ref and the target reaction force at time t + 1 based on the joint angle distribution of the sampled node q _{t +} 1 in [prediction] (3) of the first embodiment. Sampling.

Then, in [prediction] (3), the control device 1 calculates the calculated target joint angle θ _{t + 1} ^ref , the target joint angular velocity θ (dot) _{t + 1} ^ref , and the corrected target joint angular acceleration θ (2 dots) _{t + 1} ^ref Based on the sampled target reaction force F _{t + 1} , the target joint torque τ _{t + 1} is calculated using the equation of motion (reverse dynamics calculation equation) (Equation 3).
Thus, by including the hand reaction force in the hidden Markov model, the target reaction force can be automatically set in the process of [prediction].

  In the third embodiment, processes other than the above [prediction] (3) and (6) are the same as those in the first embodiment, and thus detailed description thereof is omitted.

Embodiment 4
In Embodiment 4 of the present invention, the control device 1 constructs a hidden Markov model based on time-series data of the hand speed of the end effector 23 (one specific example of speed information) and the hand reaction force of the end effector 23. To do.

(Learning process)
In the learning process, the control device 1 performs learning using, for example, time series data of hand speed and hand reaction force of the end effector 23 prepared in advance, and constructs a left-right hidden Markov model by the Baum-Welch method. .

(Execution process)
[prediction]
(1) First, the control device 1 holds the hidden Markov model node q _t at the current time t, the hand speed and the hand reaction force at the current time t detected by the sensor, and (particles).

(2) The control device 1 samples the node q _{t + 1} at time t + 1 (next moment) using the transition probability a between the nodes of the hidden Markov model.

(3) The control device 1 samples the target hand speed at time t + 1 based on the hand speed distribution of the sampled node q _{t + 1} .

(4) The control device 1 calculates the target hand position by first-order integration of the sampled target hand speed, and calculates the target hand acceleration by differentiating the target hand speed by the first order.
Since processes other than the above (1) to (4) are the same as those in the second embodiment, detailed description thereof is omitted.

Embodiment 5
In the fifth embodiment of the present invention, the control device 1 constructs a hidden Markov model based on the time series data of the joint angular velocity and joint torque of each joint portion 22 of the robot arm 2.

(Learning process)
In the learning process, the control device 1 performs learning using, for example, time series data of joint angular velocity and joint torque of each joint portion 22 of the robot arm 2 prepared in advance, and left-right hidden Markov using the Baum-Welch method. Build a model.

(Execution process)
[prediction]
(1) The control apparatus 1, first, holds the node q _t hidden Markov model at the current time t, and joint angular velocity and joint torque at the current time t is detected by the sensor, the (particles).

(2) The control device 1 samples the node q _{t + 1} at time t + 1 (next moment) using the transition probability a between the nodes of the hidden Markov model.

(3) The control device 1 samples the target joint angular velocity at time t + 1 based on the joint angular velocity distribution of the sampled node q _{t + 1} .

(4) The control device 1 calculates the target joint angle by performing first-order integration on the sampled target joint angular velocity, and calculates the target joint angular acceleration by performing first-order differentiation on the target joint angular velocity.
Since the processes other than the above (1) to (4) are the same as those in the first embodiment, detailed description thereof is omitted.

Note that the present invention is not limited to the above-described embodiment, and can be changed as appropriate without departing from the spirit of the present invention.
In the above embodiment, the control device 1 constructs a hidden Markov model and performs control using the constructed hidden Markov model, but is not limited thereto. The control device 1 may construct a graphical statistical model in which transition probabilities are set between the nodes and each node has a distribution of position or velocity information and force information, and control may be performed using the statistical model. The control apparatus 1 may construct | assemble graphical statistical models, such as a Markov model and CRF (Conditional Random Field), for example.

  In the above embodiment, the control device 1 controls the robot arm 2, but is not limited to this. The control device 1 may control, for example, a robot leg having a plurality of joints or a walking support robot that is attached to a human leg and assists in walking, and controls each part of the robot having a plurality of joints. it can. Furthermore, the control device 1 is not only controlled by a robot, but also by a machine tool (imitation of a person's processing method in a processing machine that performs force control), automatic operation of a vehicle or the like (imitation of a human handle operation, etc.), motor Devices including moving parts may be controlled.

  In the present invention, for example, the processing shown in FIG. 5 can be realized by causing the CPU 1a to execute a computer program.

  The program may be stored using various types of non-transitory computer readable media and supplied to a computer. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (for example, flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (for example, magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / W and semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (random access memory)) are included.

  The program may be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.

  DESCRIPTION OF SYMBOLS 1 Control apparatus, 2 Robot arm, 21 Link, 22 Joint part, 23 End effector, 24 Angle sensor, 25 Actuator, 26 Force sensor

Claims (1)

A model constructed based on the position or speed information of the movable part to be operated and the force information on the movable part, and a transition probability between the nodes is set, and each node is distributed between the position or speed information and the force information. Sampling target position information or target speed information of the movable part at time t + 1 based on a statistical model having the following information: and position or speed information and force information of the movable part at time t (t is a natural number); ,
Calculating target position information or target speed information of the movable part and target acceleration information at time t + 1 based on the sampled target position information or target speed information;
Performing reverse dynamics calculation based on the calculated target position information, target speed information and target acceleration information at time t + 1 to calculate target force information of the movable part at time t + 1;
Calculating a probability that the calculated target position information and target force information at time t + 1 are generated from the statistical model based on the position information and force information at time t and the statistical model;
Calculating expected values of target position information and target force information at time t + 1 based on the calculated target position information and target force information at time t + 1 and the calculated probability;
Controlling the movable part based on the calculated target position information and expected value of the target force information;
A control method comprising:

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