Motion Planning in the Presence of Moving Obstacles

Abstract

This paper investigates the computational complexity of planning the motion of a body B in 2-D or 3-D space, so as to avoid collision with moving obstacles of known, easily computed, trajectories. Dynamic movement problems are of fundamental importance to robotics, but their computational complexity has not previously been investigated. We provide evidence that the 3-D dynamic movement problem is intractable even if B has only a constant number of degrees of freedom of movement. In particular, we prove the problem is PSPACE-hard if B is given a velocity modulus bound on its movements and is NP hard even if B has no velocity modulus bound, where in both cases B has 6 degrees of freedom. To prove these results we use a unique method of simulation of a Turing machine which uses time to encode configurations (whereas previous lower bound proofs in robotics used the system position to encode configurations and so required unbounded number of degrees of freedom). We also investigate a natural class of dynamic problems which we call asteroid avoidance problems: B, the object we wish to move, is a convex polyhedron which is free to move by translation with bounded velocity modulus and the polyhedral obstacles have known translational trajectories but cannot rotate. This problem has many applications to robot, automobile, and aircraft collision avoidance.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1985
Accession Number
ADA161374

Entities

People

  • John Reif
  • Micha Sharir

Organizations

  • Harvard University

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Asteroids
  • Automata
  • Automata Theory
  • Collision Avoidance
  • Collisions
  • Computational Complexity
  • Computer Science
  • Geometric Forms
  • Geometry
  • Motion Planning
  • Polynomials
  • Robotics
  • Three Dimensional
  • Trajectories
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Graph Algorithms and Convex Optimization.
  • Mathematical Modeling and Probability Theory.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Space
  • Space - Spacecraft Maneuvers