Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Automatic route guidance vehicle
Reexamination Certificate
2000-06-23
2002-04-23
Cuchlinski, Jr., William A (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Automatic route guidance vehicle
C701S023000, C701S200000, C701S300000, C340S990000, C340S995190
Reexamination Certificate
active
06377878
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates to the field of robotic systems and more particularly to convergent control systems and methods suitable for distributed control of multiple robotic vehicles.
When numerous autonomous robotic vehicles are used in convergent search applications, each vehicle and the distributed control system for each vehicle must be inexpensive in order to be cost effective. Inexpensive on-board sensors can have noisy measurements. Also, the amount of compute power and memory on board each vehicle is likely to be small. This results in the need for robustness to noisy sensor measurements while using a simple controller. Kalman filters can be used to filter noisy sensor data, but Kalman filters can be relatively expensive to implement.
Robotic Vehicle Control Systems
Brook's Subsumption architecture is a widely used approach for control of multiple autonomous vehicles. See Brooks, “A Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation, RA-2, pp. 14-23, March 1986. Brooks teaches a layered control system implemented as an augmented finite state machine. Brooks' architecture is fast and simple to implement, but there is no theory regarding what the control laws should be or when to switch between them. Brook's Subsumption approach provides coordination at a higher level and relies on stable controls at a lower level. Consequently, Brooks provides control without predictable convergence characteristics.
Another category of work involving fuzzy control of robotic vehicles generated fuzzy rules with ad hoc, rather than predictable, convergence characteristics. See Maeda et al., “Behavior-Decision Fuzzy Algorithm for Autonomous Mobile Robots,” Information Sciences, Vol. 71, pp. 145-168,1993; and Marapane et al., “Motion Control of Cooperative Robotic Teams Through Visual Observation and Fuzzy Logic Control,” Proceedings of the 1996 International Conference on Robotics and Automation, pp. 1738-1743,1996. The ad hoc rules of Maeda et al. and Marapane et al. cannot guarantee convergence in finding a target; consequently, they are not suitable where provable convergence is required.
Phase plane analysis and variable structure control techniques have been used for stable control of single and multiple robotic vehicles. Sliding mode control is a subset of variable structure control. See, e.g., Feddema et al., “Explaining Finite State Machine Characteristics using Variable Structure Control,” SPIE Conference on Sensor Fusion and Decentralized Control in Autonomous Robotic Systems, Pittsburg, Oct. 14-17, 1997, hereinafter referred to as Feddema'97; and Feddema et al., “Designing Stable Finite State Machine Behaviors using Phase Plane Analysis and Variable Structure Control,” Proceedings of 1998 IEEE International Conference on Robotics and Automation, Belgium, May 16-21, 1998, hereinafter referred to as Feddema'98. The sliding mode control of Feddema'97 and Feddema'98 are good with robotic vehicles having a lot of on-board compute-power, but are unsuitable for robotic vehicles with limited compute-power and limited memory.
Many ways exist to generate fuzzy rules. See Miyata et al, “Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking,” IEICE Trans. Inf. & Syst., Vol. E79-D, No. 5, May 1996, pp. 561-569; Nomura et al, “A Learning Method of Fuzzy Inference Rules by Descent Method,” IEEE International Conference on Fuzzy Systems, March 1992, pp. 203-210; Kim et al, “An Auto-Tuning Fuzzy Rule-Based Visual Servoing Algorithm for a Slave Arm,” IEEE International Symposium on Intelligent Control, August 1995, pp. 177-182; Fukuda and Shimojima, “Fusion of Fuzzy, NN, GA to the Intelligent Robotics,” 1995 IEEE International Conference on Systems, Man, and Cybernetics, Vol. 3, October 1995, pp. 2892-2897. Cho and No developed a provably stable fuzzy controller based upon a linear quadratic regulator theory and Lyapunov stability theory. See Cho and No, “Design of Stability-Guaranteed Fuzzy Logic Controller for Nuclear Steam Generators,” IEEE Transactions on Nuclear Science, Vol. 43, No. 2, April 1996. Cho and No's method can be applied to the control of a single nuclear generator, but Cho and No's method cannot be applied to multiple robotic vehicles.
One example of a need for convergence of robotic vehicles is in military applications where autonomous robotic vehicles are used to locate (converge on) a bomb target. Another example is in chemical applications where autonomous robotic vehicles are used to locate a scent source. In convergent search applications utilizing multiple autonomous robotic vehicles, each vehicle with on-board sensors needs to be inexpensive. An inexpensive system can have a small amount of compute power, a small amount of memory, and noisy on-board sensors. This results in the need for robustness to noisy sensor measurements while using a simple controller. Accordingly, there is an unmet need for a control system suitable for multiple autonomous robotic vehicles that can be used where provable convergence is required.
SUMMARY OF THE INVENTION
This invention provides a distributed control system suitable for controlling one or multiple robotic vehicles to converge to a goal. The present invention controls one or more vehicles without collisions with obstacles, in the presence of noisy measurements and with a small amount of compute-power and memory on board each vehicle. The control system for each vehicle comprises a sensor and a fuzzy controller. The fuzzy controller can comprise a microcontroller, a memory accessible from the microcontroller, and a program stored in the memory that implements fuzzy control rules on the microcontroller. The fuzzy controller can take input from the sensor and can generate a set of vehicle control commands according to fuzzy control rules that result in provable vehicle convergence to a specified goal. The sensor can sense one or more needed inputs or can be a sensor subsystem that senses one or more needed inputs. Control commands can be any set of commands which result in a vehicle converging to a desired goal or scent source.
The present invention provides a new method for distributed robotic vehicle control that can control a single vehicle or multiple vehicles to converge to a goal.
REFERENCES:
patent: 5283575 (1994-02-01), Kao et al.
Brooks, “A Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation, RA-2, pp. 14-23, Mar. 1986.
Maeda et al., “Behavior-Decision Fuzzy Algorithm for Autonomous Mobile Robots,” Information Sciences, vol. 71, pp. 145-168, 1993.
Marapane et al., “Motion Control of Cooperative Robotic Teams Through Visual Observation and Fuzzy Logic Control,” Proceedings of the 1996 International Conference on Robotics and Automation, pp. 1738-1743, 1996.
Feddema et al., “Explaining Finite State Machine Characteristics using Variable Structure Control,” SPIE Conference on Sensor Fusion and Decentralized Control in Autonomous Robotic Systems, Pittsburg, Oct. 14-17, 1997, referred to as Feddema'97.
Feddema et al., “Designing Stable Finite State Machine Behaviors using Phase Plane Analysis and Variable Structure Control,” Proceedings of 1998 IEEE International Conference on Robotics and Automation, Belgium, May 16-21, 1998, referred to as Feddema'98.
Miyata et al, “Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking,” IEICE Trans. Inf. & Syst., vol. E79-D, No. 5, May 1996, pp. 561-569.
Nomura et al, “A Learning Method of Fuzzy Inference Rules by Descent Method,” IEEE International Conference on Fuzzy Systems, Mar. 1992, pp. 203-210.
Kim et al, “An Auto-Tuning Fuzzy Rule-Based Visual Servoing Algorithm for a Slave Arm,” IEEE International Symposium on Intelligent Control, Aug. 1995, pp. 177-182.
Fukuda and Shimojima, “Fusion of Fuzzy, NN, GA to the Intelligent Robotics,” 1995 IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, Oct. 1995, pp. 2892-2897
Driessen Brian J.
Feddema John T.
Kwok Kwan S.
Cuchlinski, Jr. William A
Donnelly Arthur D.
Rountree Suzanne L. K.
Sandia Corporation
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