Task space motion planning using reactive control

In this paper we present an approach to reduce the effort for planning robot motions by shifting the planning problem to a high-level representation. We combine classical sampling-based random tree planning with a reactive controller connecting sampling points with nontrivial trajectories, utilizing redundant DOFs to locally avoid obstacles. While the reactive planner operates locally on a short time scale, the complementary sampling-based method is able to find globally feasible solutions due to its larger preview horizon. Additionally, planning is done in a low-dimensional task space instead of the high-dimensional joint space. Comparing the average planning time and number of tree extensions for several scenarios and planning methods, we demonstrate that this hybrid planning approach is capable of solving a large fraction of planning queries while saving considerable planning time.

Authors

• Matthias Behnisch
• Robert Haschke
• Michael Gienger

Keywords

• Planning
• Aerospace electronics
• Robots
• Trajectory
• Joints
• Collision avoidance
• Path Planning
• Reactive Control
• Task Space
• Low-dimensional Space
• Joint Space
• Random Tree
• Planning Methods
• Planning Approach
• Planning Time
• Control System
• Left Hand
• Cost Function
• Urban Planning
• Target Location
• Search Space
• Inverse Problem
• Position In Space
• Behavioral Reactions
• Configuration Space
• Obstacle Avoidance
• Humanoid Robot
• Rapidly-exploring Random Tree
• Joint Velocity
• Planning Algorithm
• Global Component
• Average Running Time
• Gradient Projection Method
• Space Planning
• External Objects
• Null Space