Multi-agent motion planning with sporadic communications for collision avoidance

Boardman, Beth Leigh; Harden, Troy Anthony; Martinez, Sonia

doi:10.1016/j.ifacsc.2020.100126

Title: Multi-agent motion planning with sporadic communications for collision avoidance

Abstract

Here, a novel multi-vehicle motion planning and collision avoidance algorithm is proposed and analyzed. The algorithm aims to reduce the amount of onboard calculations and inter-agent communications needed for each vehicle to successfully navigate through an environment with static obstacles and reach their goals. To this end, each agent first calculates a path to the goal by means of an asymptotically optimal rapidly-exploring random tree (RRT*) with respect to the static obstacles. Then, other agents are treated as dynamic obstacles and potential collisions are determined by means of collision cones. Collision cones depend on the position and velocity from other agents and are grown conservatively between inter-agent communications. Based on the available information, each agent determines if a deconfliction maneuver is needed, if it can continue along its current path, or if communication is needed to make a decision about a conflict. With probability one, our algorithm guarantees that the agents keep from colliding with each other. Under an assumption on the existence of a solution for a vehicle to its goal, this algorithm also solves the planning problem with probability one. Simulations illustrate a group of agents successfully reaching their goal configurations and examine how the uncertainty affects themore »« less

Authors:

^[1];

^[1]; Martinez, Sonia ^[2]

Los Alamos National Laboratory
Univ. of California, San Diego, CA (United States)

Publication Date:: Thu Nov 26 00:00:00 EST 2020

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1727412

Alternate Identifier(s):: OSTI ID: 1810982

Report Number(s):: LA-UR-18-29960
Journal ID: ISSN 2468-6018

Grant/Contract Number:: 89233218CNA000001; LA-UR-18-29960

Resource Type:: Accepted Manuscript

Journal Name:: IFAC Journal of Systems and Control

Additional Journal Information:: Journal Volume: 15; Journal Issue: C; Journal ID: ISSN 2468-6018

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 33 ADVANCED PROPULSION SYSTEMS; Multi-Agent; Path Planning; Collision Avoidance; Motion Planning; Event-triggered

Citation Formats


                    Boardman, Beth Leigh, Harden, Troy Anthony, and Martinez, Sonia. Multi-agent motion planning with sporadic communications for collision avoidance.  United States: N. p., 2020. 
Web.  doi:10.1016/j.ifacsc.2020.100126.

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                    Boardman, Beth Leigh, Harden, Troy Anthony, & Martinez, Sonia. Multi-agent motion planning with sporadic communications for collision avoidance.  United States.  https://doi.org/10.1016/j.ifacsc.2020.100126

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                    Boardman, Beth Leigh, Harden, Troy Anthony, and Martinez, Sonia. Thu .  
"Multi-agent motion planning with sporadic communications for collision avoidance".  United States.  https://doi.org/10.1016/j.ifacsc.2020.100126.  https://www.osti.gov/servlets/purl/1727412.

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@article{osti_1727412,

  title        = {Multi-agent motion planning with sporadic communications for collision avoidance},

  author       = {Boardman, Beth Leigh and Harden, Troy Anthony and Martinez, Sonia},

  abstractNote = {Here, a novel multi-vehicle motion planning and collision avoidance algorithm is proposed and analyzed. The algorithm aims to reduce the amount of onboard calculations and inter-agent communications needed for each vehicle to successfully navigate through an environment with static obstacles and reach their goals. To this end, each agent first calculates a path to the goal by means of an asymptotically optimal rapidly-exploring random tree (RRT*) with respect to the static obstacles. Then, other agents are treated as dynamic obstacles and potential collisions are determined by means of collision cones. Collision cones depend on the position and velocity from other agents and are grown conservatively between inter-agent communications. Based on the available information, each agent determines if a deconfliction maneuver is needed, if it can continue along its current path, or if communication is needed to make a decision about a conflict. With probability one, our algorithm guarantees that the agents keep from colliding with each other. Under an assumption on the existence of a solution for a vehicle to its goal, this algorithm also solves the planning problem with probability one. Simulations illustrate a group of agents successfully reaching their goal configurations and examine how the uncertainty affects the communication frequency of the multi-agent system.},

  doi          = {10.1016/j.ifacsc.2020.100126},

  journal      = {IFAC Journal of Systems and Control},

  number       = C,

  volume       = 15,

  place        = {United States},

  year         = {Thu Nov 26 00:00:00 EST 2020},

  month        = {Thu Nov 26 00:00:00 EST 2020}

}

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Works referenced in this record:

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Similar Records

Title: Multi-agent motion planning with sporadic communications for collision avoidance

Abstract

Citation Formats

Use of relaxation methods in sampling-based algorithms for optimal motion planning conference, May 2013

Sampling-based algorithms for optimal motion planning journal, June 2011

Optimal kinodynamic motion planning using incremental sampling-based methods conference, December 2010

A Probabilistically Robust Path Planning Algorithm for UAVs Using Rapidly-Exploring Random Trees journal, September 2012

Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions journal, May 2015

Probabilistic roadmaps for path planning in high-dimensional configuration spaces journal, January 1996

Real-Time Motion Planning for Agile Autonomous Vehicles journal, January 2002

Robust adaptive motion planning in the presence of dynamic obstacles conference, July 2016

A survey of intelligent vehicle applications worldwide conference, January 2000

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The increasing cost tree search for optimal multi-agent pathfinding journal, February 2013

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journal, June 2011

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journal, September 2012

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conference, July 2016