BI 2 RRT*: An efficient sampling-based path planning framework for task-constrained mobile manipulation

Mobile manipulators installed in warehouses and factories for conveying goods between working stations need to meet the requirements of time-critical workflows. Moreover, the systems are expected to deal with changing tasks, cluttered environments and constraints imposed by the goods to be delivered. In this paper, we present a novel planning framework for generating asymptotically optimal paths for mobile manipulators subject to task constraints. Our approach introduces the Bidirectional Informed RRT* (BI 2 RRT*) that extends the Informed RRT* [1] towards bidirectional search and satisfaction of end-effector task constraints. In various experiments, we demonstrate the efficiency of BI 2 RRT* for both unconstrained and constrained mobile manipulation planning problems. As the results show, our planning framework finds better solutions than Informed RRT* and Bidirectional RRT* in less planning.

Authors

• Felix Burget
• Maren Bennewitz
• Wolfram Burgard

Keywords

• Planning
• Mobile communication
• Convergence
• Manipulators
• Path planning
• Manifolds
• Planning Framework
• Mobile Manipulator
• Optimal Path
• Planning Problem
• Task Constraints
• Constraint Satisfaction
• Convergence Rate
• Angular Velocity
• Singular Value Decomposition
• Current Solution
• Configuration Space
• Pitch Angle
• Fourth Row
• Roll Angle
• Yaw Angle
• Planning Time
• Planning Algorithm
• Positive Deflection
• Coordination Task
• Solution Path
• World Frame
• End-effector Pose
• Task Space
• Scenario Planning
• Mobility Tasks
• Errors In Task
• Joint Space
• Collision
• First-order Method