This paper presents a randomized motion planner for kinodynamic asteroidavoidance problems, in which a robot must avoid collision with moving obstacles under kinematic, dynamic constraints and reach a specified goal state. Inspired by probabilistic-roadmap (PRM) techniques, the planner samples the statex time space of a robot by picking control inputs at random in order to compute a roadmap that captures the connectivity of the space. However, the planner does not precompute a roadmap as most PRM planners do. Instead, for each planning query, it generates, on the fly, a small roadmap that connects the given initial and goal state. In contrast to PRM planners, the roadmap computed by our algorithm is a directed graph oriented along the time axis of the space. To verify the planner's effectiveness in practice, we tested it both in simulated environments containing many moving obstacles and on a real robot under strict dynamic constraints. The efficiency of the planner makes it possible for a robot to respond to a changing environment without knowing the motion of moving obstacles well in advance.
Inspired by Probabilistic-Roadmap Methods (PRM), the planner described in REF samples the state and time space of a robot by picking control inputs at random in order to compute a roadmap that captures the connectivity of the space.
Kinodynamic motion planning amidst moving obstacles
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
Engineering Computer Science