Singhal et al., 1997 - Google Patents
Dynamic bayes net approach to multimodal sensor fusionSinghal et al., 1997
View PDF- Document ID
- 7807085467068463518
- Author
- Singhal A
- Brown C
- Publication year
- Publication venue
- Sensor Fusion and Decentralized Control in Autonomous Robotic Systems
External Links
Snippet
Autonomous mobile robots rely on multiple sensors to perform a varied number of tasks in a given environment. Different tasks may need different sensors to estimate different subsets of world state. Also, different sensors can cooperate in discovering common subsets of world …
- 230000004927 fusion 0 title abstract description 21
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Singhal et al. | Dynamic bayes net approach to multimodal sensor fusion | |
Fox et al. | Bayesian techniques for location estimation | |
Xie et al. | Congestion-aware multi-agent trajectory prediction for collision avoidance | |
Lebedev et al. | The dynamic wave expansion neural network model for robot motion planning in time-varying environments | |
Asgharivaskasi et al. | Active bayesian multi-class mapping from range and semantic segmentation observations | |
US20210309264A1 (en) | Human-robot collaboration | |
Jain et al. | Efficient hierarchical robot motion planning under uncertainty and hybrid dynamics | |
Veiga et al. | Efficient object search for mobile robots in dynamic environments: Semantic map as an input for the decision maker | |
Jacinto et al. | Navigation of autonomous vehicles using reinforcement learning with generalized advantage estimation | |
Dhiman et al. | A review of path planning and mapping technologies for autonomous mobile robot systems | |
Miao | Robot path planning in dynamic environments using a simulated annealing based approach | |
Arnob et al. | Improving Reliable Navigation Under Uncertainty via Predictions Informed by Non-Local Information | |
Majed et al. | Sensing-based self-reconfigurable decision-making mechanism for autonomous modular robotic system | |
Corah | Sensor planning for large numbers of robots | |
Kaplow et al. | Variable resolution decomposition for robotic navigation under a POMDP framework | |
Kraetzschmar et al. | Application of neurosymbolic integration for environment modelling in mobile robots | |
Thomas et al. | Inverse Reinforcement Learning for Generalized Labeled Multi-Bernoulli Multi-Target Tracking | |
Veiga et al. | From Reactive to Active Sensing: A Survey on Information Gathering in Decision-theoretic Planning | |
Chatila et al. | A case study in machine intelligence: Adaptive autonomous space rovers | |
Liljeström | Probability based path planning of unmanned ground vehicles for autonomous surveillance: Through world decomposition and modelling of target distribution | |
Yinka-Banjo et al. | Collision avoidance in unstructured environments for autonomous robots: a behavioural modelling approach | |
Zimmer | Adaptive approaches to basic mobile robot tasks | |
Dutta et al. | Uncertainty measured markov decision process in dynamic environments | |
Stadler | Learned functions for perceptually informed robot navigation | |
Ray et al. | Human-Centered Autonomy for UAS Target Search |