Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly
arXiv:2106.02489 · doi:10.1109/TRO.2022.3198020
Abstract
Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multi-robot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world.
13 pages, 16 Figures, 2 Tables, 3 Algorithms
References in corpus (5)
- Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning
- Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
- Learning a Decentralized Multi-arm Motion Planner
- Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort
- Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraints
Cited by in corpus (5)
- RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning
- MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments
- Task and Motion Planning for Humanoid Loco-manipulation
- Constant-time Motion Planning with Anytime Refinement for Manipulation
- Approximate Topological Optimization using Multi-Mode Estimation for Robot Motion Planning