Real-Time Motion Planning and Safe Navigation in Dynamic Multi-Robot Environments

Abstract

All mobile robots share the need to navigate, creating the problem of motion planning. In multi-robot domains with agents acting in parallel, highly complex and unpredictable dynamics can arise. This leads to the need for navigation calculations to be carried out within tight time constraints, so that they can be applied before the dynamics of the environment make the calculated answer obsolete. At the same time, we want the robots to navigate robustly and operate safely without collisions. While motion planning has been used for high level robot navigation, or limited to semi-static or single-robot domains, it has often been dismissed for the real-time low-level control of agents due to the limited computational time and the unpredictable dynamics. Many robots now rely on local reactive methods for immediate control of the robot, but if the reason for avoiding motion planning is execution speed, the answer is to find planners that can meet this requirement. Recent advances in traditional path planning algorithms may offer hope in resolving this type of scalability, if they can be adapted to deal with the specific problems and constraints mobile robots face. Also, in order to maintain safety, new scalable methods for maintaining collision avoidance among multiple robots are needed in order to free motion planners from the curse of dimensionality when considering the safety of multiple robots with realistic physical dynamics constraints. This thesis contributes the pairing of real-time motion planning which builds on existing modern path planners, and a novel cooperative dynamics safety algorithm for high speed navigation of multiple agents in dynamic domains. It also explores near real-time kinematically limited motion planning for more complex environments. The thesis algorithms have been fully implemented and tested with success on multiple real robot platforms.

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Document Details

Document Type
Technical Report
Publication Date
Dec 15, 2006
Accession Number
ADA465526

Entities

People

  • James R. Bruce

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence
  • Autonomous Navigation
  • Autonomous Systems
  • Collision Avoidance
  • Computational Science
  • Computer Graphics
  • Computer Vision
  • Control Systems
  • Geometry
  • Heuristic Methods
  • Kalman Filters
  • Motion Planning
  • Robot Navigation
  • Robots
  • Trees (Data Structures)
  • Unmanned Aerial Vehicles

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control