Scalable asymptotically-optimal multi-robot motion planning

Abstract

Discovering high-quality paths for multirobot 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 pathquality 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 multirobot 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.

Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs

“…AO methods, however, require building rather dense data structures and can suffer scalability issues when dealing with multirobot problems, where the number of DoFs of the system increases. Recent advances in sampling-based multi-robot motion planning focused on high DoF systems, such as the dRRT * method [19], [20], help deal with the increase in the size of the composite C-space by automatically taking advantage of any natural decoupling present in the problem and precomputation [17], [18], [16] expressing each robot's reachability region. This work aims to build on top of this anytime, AO, sampling-based approach.…”

“…Then, a tensor product roadmapĜ = G 1 × G 2 contains all combinations of vertices and neighborhoods that exist in the constituent roadmaps [16]. Prior work has shown that when the constituent roadmaps are constructed with asymptotically optimal properties, the tensor roadmap is also asymptotically optimal for the multi-robot problem [19], [20]. The approach does not need to explicitly store the tensor roadmap.…”

“…Firstly using results from dRRT * [19], [20] it can be argued that given any pair of states that might represent a pair of mode transitions, the tree expansion strategy in Algo. 2 ensures asymptotic optimality of the connection between them given enough time and large enough number of samples in the underlying roadmaps.…”

A Modular Framework for Motion Planning Using Safe-by-Design Motion Primitives

“…For example, a centralized approach is given in [35], discretizing the workspace into a lattice and using integer linear programming to minimize the total time for robots to traverse in high densities. In [10], a sampling-based roadmap is constructed in the joint robot space using individual robot roadmaps, which is shown to be asymptotically optimal. Prioritized planning enables to safely coordinate many vehicles and is considered in a centralized and decentralized fashion in [6].…”

Improved Discrete RRT for Coordinated Multi-robot Planning

“…In this paper, we present a probabilistic approach which extends and improves a discrete version of Rapidly-Exploring Random Tree (RRT) for multiple robots (Kiril Solovey, 2014). Our approach focuses mainly on scalability with increasing number of agents as well as improving the quality of solution compared to (Dobson et al, 2017) that presents the optimal version of the dRRT algorithm but keeps the number of robots relatively low. We show that the proposed extensions allow solving problems with tens of robots in times comparable to CARP with a slightly worse quality of results.…”