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K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning

Mike Qin, Irving Solis, James Motes, Marco Morales, Nancy M. Amato

TL;DR

This work presents Kinodynamic Adaptive Robot Coordination (K-ARC), a novel algorithm for multi-robot kinodynamic planning that builds on a previously proposed multi-robot motion planning framework, Adaptive Robot Coordination, and inherits its strength of focusing on coordination between robots only when needed, saving computational effort.

Abstract

This work presents Kinodynamic Adaptive Robot Coordination (K-ARC), a novel algorithm for multi-robot kinodynamic planning. Our experimental results show the capability of K-ARC to plan for up to 32 planar mobile robots, while achieving up to an order of magnitude of speed-up compared to previous methods in various scenarios. K-ARC is able to achieve this due to its two main properties. First, K-ARC constructs its solution iteratively by planning in segments, where initial kinodynamic paths are found through optimization-based approaches and the inter-robot conflicts are resolved through sampling-based approaches. The interleaving use of sampling-based and optimization-based approaches allows K-ARC to leverage the strengths of both approaches in different sections of the planning process where one is more suited than the other, while previous methods tend to emphasize on one over the other. Second, K-ARC builds on a previously proposed multi-robot motion planning framework, Adaptive Robot Coordination (ARC), and inherits its strength of focusing on coordination between robots only when needed, saving computation efforts. We show how the combination of these two properties allows K-ARC to achieve overall better performance in our simulated experiments with increasing numbers of robots, increasing degrees of problem difficulties, and increasing complexities of robot dynamics.

K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning

TL;DR

This work presents Kinodynamic Adaptive Robot Coordination (K-ARC), a novel algorithm for multi-robot kinodynamic planning that builds on a previously proposed multi-robot motion planning framework, Adaptive Robot Coordination, and inherits its strength of focusing on coordination between robots only when needed, saving computational effort.

Abstract

This work presents Kinodynamic Adaptive Robot Coordination (K-ARC), a novel algorithm for multi-robot kinodynamic planning. Our experimental results show the capability of K-ARC to plan for up to 32 planar mobile robots, while achieving up to an order of magnitude of speed-up compared to previous methods in various scenarios. K-ARC is able to achieve this due to its two main properties. First, K-ARC constructs its solution iteratively by planning in segments, where initial kinodynamic paths are found through optimization-based approaches and the inter-robot conflicts are resolved through sampling-based approaches. The interleaving use of sampling-based and optimization-based approaches allows K-ARC to leverage the strengths of both approaches in different sections of the planning process where one is more suited than the other, while previous methods tend to emphasize on one over the other. Second, K-ARC builds on a previously proposed multi-robot motion planning framework, Adaptive Robot Coordination (ARC), and inherits its strength of focusing on coordination between robots only when needed, saving computation efforts. We show how the combination of these two properties allows K-ARC to achieve overall better performance in our simulated experiments with increasing numbers of robots, increasing degrees of problem difficulties, and increasing complexities of robot dynamics.
Paper Structure (30 sections, 4 equations, 3 figures, 1 table, 2 algorithms)

This paper contains 30 sections, 4 equations, 3 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries and Problem Formulation
  3. Motion Planning
  4. Kinodynamic Planning
  5. Multi-Robot Kinodynamic Planning
  6. Related Work
  7. Multi-Robot Kinodynamic Planning
  8. Adaptive Robot Coordination
  9. Method
  10. Overview
  11. Single robot trajectory Optimizer
  12. Subproblem Construction and Planning
  13. Design choices
  14. Experiments
  15. Experiment Setup
  16. ...and 15 more sections

Figures (3)

  • Figure 1: K-ARC's process in constructing the solution: (a) Each robot builds their individual roadmap and finds an initial kinematic path where the paths are divided into segments with equal number of timesteps. (b) We start in the first segment. (c) We convert the kinematic path into kinodynamically feasible trajectory. (d) We repeat the process for the second segment, now there is a conflict between robot 1 and robot 2's path, which we will the proceed to solve through a multi-robot planner.
  • Figure 2: The experiment environments. The paths shown in white are produced by K-ARC. (a) 4-robot open cross scenario (b) 4-robot cluttered cross scenario (c) 2-robot quadrotor scenario (d) 2-robot quadrotor inlet scenario
  • Figure 3: Results for the experiments. The percentage indicates the success rate of the method and is otherwise 100%.