Abstract
We
present a novel algorithm for robot motion planning in dynamics
environments. Our approach extends Rapidly-exploring Random Trees
(RRTs) in several ways. We assume the need to simultaneously plan and
maintain paths for multiple tasks with respect tot he current state of
a moving robot in a dynamic environment. Our algorithm dynamically
maintains a forest of trees by splitting, growing, and merging them on
the fly to adapt to moving obstacles and robot motion. In
order to minimize tree maintenance, we only validate the task paths,
rather than the entire forest. The root of the inhabited tree moves
with the robot. Dynamic re-planning is integrated with tree and forest
maintenance. Coupling the robot motion with the planner enables us to
support multiple tasks, for example providing an "escape" path while
moving to a goal. The robot is free to move along whichever task path
it chooses.
We highlight the work by showing fast results in simulated environments with moving obstacles.
We highlight the work by showing fast results in simulated environments with moving obstacles.
