Combined Speed and Steering Control in High-Speed Autonomous Ground Vehicles for Obstacle Avoidance Using Model Predictive Control

This paper presents a model predictive control-based obstacle avoidance algorithm for autonomous ground vehicles at high speed in unstructured environments. The novelty of the algorithm is its capability to control the vehicle to avoid obstacles at high speed taking into account dynamical safety con...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2017-10, Vol.66 (10), p.8746-8763
Main Authors: Jiechao Liu, Jayakumar, Paramsothy, Stein, Jeffrey L., Ersal, Tulga
Format: Article
Language:English
Subjects:
Algorithms
Autonomous ground vehicles
Collision avoidance
Computer simulation
Heuristic algorithms
High speed
Lidar
Mathematical models
model predictive control
Navigation
Obstacle avoidance
Optimal control
Optimization
Performance enhancement
Predictive control
Safety
Steering
Trajectory
Unmanned ground vehicles
Vehicle dynamics
Vehicles
Wheels
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a model predictive control-based obstacle avoidance algorithm for autonomous ground vehicles at high speed in unstructured environments. The novelty of the algorithm is its capability to control the vehicle to avoid obstacles at high speed taking into account dynamical safety constraints through a simultaneous optimization of reference speed and steering angle without a priori knowledge about the environment and without a reference trajectory to follow. Previous work in this specific context optimized only the steering command. In this paper, obstacles are detected using a planar light detection and ranging sensor. A multi-phase optimal control problem is then formulated to simultaneously optimize the reference speed and steering angle within the detection range. Vehicle acceleration capability as a function of speed, as well as stability and handling concerns such as preventing wheel lift-off, are included as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Simulation results show that the proposed algorithm is capable of safely exploiting the dynamic limits of the vehicle while navigating the vehicle through sensed obstacles of different sizes and numbers. It is also shown that the proposed variable speed formulation can significantly improve performance by allowing navigation of obstacle fields that would otherwise not be cleared with steering control alone.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2017.2707076