An MPC Algorithm with Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles

Abstract

This article presents a model predictive control based obstacle avoidance algorithm for autonomous ground vehicles in unstructured environments. The novelty of the algorithm is the simultaneous optimization of speed and steering without a priori knowledge about the obstacles. Obstacles are detected using a planar LIDAR sensor and a multi-phase optimal control problem is formulated to optimize the speed and steering commands within the detection range. Acceleration capability of the vehicle as a function of speed, and stability and handling concerns such as tire lift-off are taken into account as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Thus, a safe and quick navigation is enabled without the need for a preloaded map of the environment. Simulation results show that the proposed algorithm is capable of navigating the vehicle through obstacle fields that cannot be cleared with steering control alone.

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Document Details

Document Type
Technical Report
Publication Date
Apr 24, 2015
Accession Number
ADA615652

Entities

People

  • Jeffrey L. Stein
  • Jiechao Liu
  • Paramsothy Jayakumar
  • Tulga Ersal

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Collision Avoidance
  • Collisions
  • Detection
  • Detectors
  • Dynamics
  • Engineering
  • Environment
  • Ground Vehicles
  • Laser-Based Detection
  • Mathematical Programming
  • Model Predictive Control
  • Navigation
  • Optimization
  • Sideslip
  • Simulations
  • Steering

Readers

  • Robotics and Automation.
  • Systems Analysis and Design