The virtual wall approach to limit cycle avoidance for unmanned ground vehicles.

Robotics and Autonomous Systems 01/2008; 56:645-657. DOI: 10.1016/j.robot.2007.11.010
Source: DBLP

ABSTRACT Robot Navigation in unknown and very cluttered environments constitutes one of the key challenges in unmanned ground vehicle (UGV) applications. Navigational limit cycles can occur when navigating (UGVs) using behavior-based or other reac- tive algorithms. Limit cycles occur when the robot is navigating towards the goal but enters an enclosure that has its opening in a direction opposite to the goal. The robot then becomes efiectively trapped in the enclosure. This paper presents a solution named the Virtual Wall Approach (VWA) to the limit cycle problem for robot navigation in very cluttered environments. This algorithm is composed of three stages: detection, retraction, and avoidance. The detection stage uses spatial memory to identify the limit cycle. Once the limit cycle has been identifled, a label- ing operator is applied to a local map of the obstacle fleld to identify the obstacle or group of obstacles that are causing the deadlock enclosure. The retraction stage deflnes a way-point for the robot outside the deadlock area. When the robot crosses the boundary of the deadlock enclosure, a virtual wall is placed near the endpoints of the enclosure to designate this area as ofi-limits. Finally, the robot activates a virtual sensor so that it can proceed to its original goal, avoiding the virtual wall and obstacles found on its way. Simulations, experiments, and analysis of the VWA implemented on top of a preference-based fuzzy behavior system demonstrate the efiectiveness of the proposed method.

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    ABSTRACT: Navigation and guidance of autonomous vehicles is a fundamental problem in robotics, which has attracted intensive research in recent decades. This report is mainly concerned with provable collision avoidance of multiple autonomous vehicles operating in unknown cluttered environments, using reactive decentralized navigation laws, where obstacle information is supplied by some sensor system. Recently, robust and decentralized variants of model predictive control based navigation systems have been applied to vehicle navigation problems. Properties such as provable collision avoidance under disturbance and provable convergence to a target have been shown; however these often require significant computational and communicative capabilities, and don't consider sensor constraints, making real time use somewhat difficult. There also seems to be opportunity to develop a better trade-off between tractability, optimality, and robustness. The main contributions of this work are as follows; firstly, the integration of the robust model predictive control concept with reactive navigation strategies based on local path planning, which is applied to both holonomic and unicycle vehicle models subjected to acceleration bounds and disturbance; secondly, the extension of model predictive control type methods to situations where the information about the obstacle is limited to a discrete ray-based sensor model, for which provably safe, convergent boundary following can be shown; and thirdly the development of novel constraints allowing decentralized coordination of multiple vehicles using a robust model predictive control type approach, where a single communication exchange is used per control update, vehicles are allowed to perform planning simultaneously, and coherency objectives are avoided.

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