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Multi-arm Manipulation and Multi-Robot Planning

Multi-arm Task Planning Framework

The work describes the topology of general multi-arm prehensile manipulation. Reasonable assumptions are applied to reduce the number of manipulation modes, which results in an explicit graphical representation for multi-arm manipulation that is computationally manageable to store and search for solution paths. In this context, it is also possible to take advantage of preprocessing steps to significantly speed up online query resolution. The approach is evaluated in simulation for multiple arms showing it is possible to quickly compute multi-arm manipulation paths of high-quality on the fly.

Dobson, A, and KE Bekris. 2015. “Planning Representations And Algorithms For Prehensile Multi-Arm Manipulation”. In IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany.
Manuscript Link

Multi-Robot Motion Planning

Discovering high-quality paths for multi-robot problems can be achieved, in principle, through asymptotically-optimal data structures in the composite space of all robots, such as a sampling-based roadmap or a tree. The hardness of motion planning, however, which depends exponentially on the number of robots, renders the explicit construction of such structures impractical. This work proposes a scalable, sampling-based planner for coupled multi-robot problems that provides desirable path-quality guarantees. The proposed dRRT* is an informed, asymptotically-optimal extension of a prior method dRRT, which introduced the idea of building roadmaps for each robot and implicitly searching the tensor product of these structures in the composite space. The paper describes the conditions for convergence to optimal paths in multi-robot problems. Moreover, simulated experiments indicate dRRT* converges to high-quality paths and scales to higher numbers of robots where various alternatives fail. It can also be used on high-dimensional challenges, such as planning for robot manipulators.

Dobson, A, K. Solovey, R. Shome, D. Halperin, and KE Bekris. 2017. “Scalable Asymptotically-Optimal Multi-Robot Motion Planning”. In 1st IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS), Los Angeles, CA, USA.
Manuscript Link

Dual-Arm Motion Planning with Shared DoF

Due to high-dimensionality, many motion planners for dual-arm systems follow a decoupled approach, which does not provide guarantees. Asymptotically optimal sampling-based planners provide guarantees but in practice face scalability challenges. This work improves the computational scalability of the latter methods in this domain. It builds on top of recent advances in multi-robot motion planning, which provide guarantees without having to explicitly construct a roadmap in the composite space of all robots. The proposed framework builds roadmaps for components of a humanoid robot's kinematic chain. Then, the tensor product of these component roadmaps is searched implicitly online in a way that asymptotic optimality is provided. Appropriate heuristics from the component roadmaps are utilized for discovering the solution in the composite space effectively. Evaluation on various dual-arm problems show that the method returns paths of increasing quality, has significantly reduced space requirements and improved convergence rate relative to the standard asymptotically optimal approaches.

Shome, R., and KE Bekris. 2017. “Improving The Scalability Of Asymptotically Optimal Motion Planning For Humanoid Dual-Arm Manipulators”. In IEEE International Conference on Humanoid Robots, Birmingham, UK.
Manuscript Link