A General Task and Motion Planning Framework For Multiple Manipulators

Many manipulation tasks combine high-level discrete planning over actions with low-level motion planning over continuous robot motions. Task and motion planning (TMP) provides a powerful general framework to combine discrete and geometric reasoning, and solvers have been previously proposed for single-robot problems. Multi-robot TMP expands the range of TMP problems that can be solved but poses significant challenges when considering scalability and solution quality. We present a general TMP framework designed for multiple robotic manipulators. This is based on two contributions. First, we propose an optimal task planner designed to support simultaneous discrete actions. Second, we introduce an intermediate scheduler layer between task planner and motion planner to evaluate alternate robot assignments to these actions. This aggressively explores the search space and typically reduces the number of expensive task planning calls. Several benchmarks with a rich set of actions for two manipulators are evaluated. We show promising results in scalability and solution quality of our TMP framework with the scheduler for up to six objects. A demonstration indicates scalability to up to five robots.

Authors

• Tianyang Pan
• Andrew M. Wells
• Rahul Shome
• Lydia E. Kavraki

Keywords

• Robot motion
• Scalability
• Grasping
• Benchmark testing
• Manipulators
• Cognition
• Planning
• General Framework
• Multiple Users
• Path Planning
• Benchmark
• Solution Quality
• Simultaneous Activation
• Optimization Task
• Continuous Motion
• Discrete Action
• Task Planning
• Multiple Robots
• Geometric Reasoning
• Discretion
• Number Of Steps
• Reachable
• Heuristic Search
• Curse Of Dimensionality
• Scale Test
• Optimal Plan
• Single Robot
• Feasible Plan
• Motion Constraints
• Sequential Execution
• Benchmark Problems
• Naive Approach