61 research outputs found
A Survey of Multi-Robot Motion Planning
Multi-robot Motion Planning (MRMP) is an active research field which has
gained attention over the years. MRMP has significant roles to improve the
efficiency and reliability of multi-robot system in a wide range of
applications from delivery robots to collaborative assembly lines. This survey
provides an overview of MRMP taxonomy, state-of-the-art algorithms, and
approaches which have been developed for multi-robot systems. This study also
discusses the strengths and limitations of each algorithm and their
applications in various scenarios. Moreover, based on this, we can draw out
open problems for future research.Comment: This is my Ph.D. comprehensive exam repor
Decentralized, Noncooperative Multirobot Path Planning with Sample-BasedPlanners
In this thesis, the viability of decentralized, noncooperative multi-robot path planning algorithms is tested. Three algorithms based on the Batch Informed Trees (BIT*) algorithm are presented. The first of these algorithms combines Optimal Reciprocal Collision Avoidance (ORCA) with BIT*. The second of these algorithms uses BIT* to create a path which the robots then follow using an artificial potential field (APF) method. The final algorithm is a version of BIT* that supports replanning. While none of these algorithms take advantage of sharing information between the robots, the algorithms are able to guide the robots to their desired goals, with the algorithm that combines ORCA and BIT* having the robots successfully navigate to their goals over 93% for multiple environments with teams of two to eight robots
A Parallel Distributed Strategy for Arraying a Scattered Robot Swarm
We consider the problem of organizing a scattered group of robots in
two-dimensional space, with geometric maximum distance between robots. The
communication graph of the swarm is connected, but there is no central
authority for organizing it. We want to arrange them into a sorted and
equally-spaced array between the robots with lowest and highest label, while
maintaining a connected communication network.
In this paper, we describe a distributed method to accomplish these goals,
without using central control, while also keeping time, travel distance and
communication cost at a minimum. We proceed in a number of stages (leader
election, initial path construction, subtree contraction, geometric
straightening, and distributed sorting), none of which requires a central
authority, but still accomplishes best possible parallelization. The overall
arraying is performed in time, individual messages, and
travel distance. Implementation of the sorting and navigation use communication
messages of fixed size, and are a practical solution for large populations of
low-cost robots
Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning
We present a sampling-based framework for multi-robot motion planning which
combines an implicit representation of a roadmap with a novel approach for
pathfinding in geometrically embedded graphs tailored for our setting. Our
pathfinding algorithm, discrete-RRT (dRRT), is an adaptation of the celebrated
RRT algorithm for the discrete case of a graph, and it enables a rapid
exploration of the high-dimensional configuration space by carefully walking
through an implicit representation of a tensor product of roadmaps for the
individual robots. We demonstrate our approach experimentally on scenarios of
up to 60 degrees of freedom where our algorithm is faster by a factor of at
least ten when compared to existing algorithms that we are aware of.Comment: Kiril Solovey and Oren Salzman contributed equally to this pape
Probabilistic motion planning for non-Euclidean and multi-vehicle problems
Trajectory planning tasks for non-holonomic or collaborative systems are
naturally modeled by state spaces with non-Euclidean metrics. However, existing
proofs of convergence for sample-based motion planners only consider the
setting of Euclidean state spaces. We resolve this issue by formulating a
flexible framework and set of assumptions for which the widely-used PRM*, RRT,
and RRT* algorithms remain asymptotically optimal in the non-Euclidean setting.
The framework is compatible with collaborative trajectory planning: given a
fleet of robotic systems that individually satisfy our assumptions, we show
that the corresponding collaborative system again satisfies the assumptions and
therefore has guaranteed convergence for the trajectory-finding methods. Our
joint state space construction builds in a coupling parameter , which interpolates between a preference for minimizing total energy at
one extreme and a preference for minimizing the travel time at the opposite
extreme. We illustrate our theory with trajectory planning for simple coupled
systems, fleets of Reeds-Shepp vehicles, and a highly non-Euclidean fractal
space.Comment: 12 pages, 8 figures. Substantial revision
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