95 research outputs found
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
Fast-dRRT*: Efficient Multi-Robot Motion Planning for Automated Industrial Manufacturing
We present Fast-dRRT*, a sampling-based multi-robot planner, for real-time
industrial automation scenarios. Fast-dRRT* builds upon the discrete
rapidly-exploring random tree (dRRT*) planner, and extends dRRT* by using
pre-computed swept volumes for efficient collision detection, deadlock
avoidance for partial multi-robot problems, and a simplified rewiring strategy.
We evaluate Fast-dRRT* on five challenging multi-robot scenarios using two to
four industrial robot arms from various manufacturers. The scenarios comprise
situations involving deadlocks, narrow passages, and close proximity tasks. The
results are compared against dRRT*, and show Fast-dRRT* to outperform dRRT* by
up to 94% in terms of finding solutions within given time limits, while only
sacrificing up to 35% on initial solution cost. Furthermore, Fast-dRRT*
demonstrates resilience against noise in target configurations, and is able to
solve challenging welding, and pick and place tasks with reduced computational
time. This makes Fast-dRRT* a promising option for real-time motion planning in
industrial automation.Comment: 7 pages, 6 figures, submitted to ICRA 202
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