Autonomous collision avoidance as a markov decision process

Hossein Rastgoftar, Xiangyu Ni, Ella M. Atkins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we apply a Markov Decision Process (MDP) to assure collision avoidance and specify optimal paths for evolution of an agent with nonlinear dynamics. We consider evolution of an autonomous wheeled robot in a motion field where a malicious agent has stochastic transitions over the field. By knowing the history of malicious agent transition as well as initial and goal destinations of the autonomous robot, the optimal path is obtained using the Bellman equation. We propose a novel finite time controller that assures reachability of desired waypoints along the optimal path, where the optimal path is obtained without deriving the Hamiltonian.

Original languageEnglish (US)
Title of host publicationMechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791850701
DOIs
StatePublished - 2016
Externally publishedYes
EventASME 2016 Dynamic Systems and Control Conference, DSCC 2016 - Minneapolis, United States
Duration: Oct 12 2016Oct 14 2016

Publication series

NameASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Volume2

Conference

ConferenceASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Country/TerritoryUnited States
CityMinneapolis
Period10/12/1610/14/16

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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