VPH+ and MPC Combined Collision Avoidance for Unmanned Ground Vehicle in Unknown Environment

TL;DR

This paper introduces a robust collision avoidance method for unmanned ground vehicles in unknown environments by combining VPH+ for perception and MPC for trajectory planning, validated through simulations.

Contribution

It presents a novel integration of VPH+ and MPC for real-time obstacle avoidance and target tracking in partially observable environments.

Findings

Effective collision avoidance demonstrated in VREP simulations.

Real-time trajectory generation with constrained model predictive control.

Enhanced perception and planning efficiency in unknown environments.

Abstract

There are many situations for which an unmanned ground vehicle has to work with only partial observability of the environment. Therefore, a feasible nonholonomic obstacle avoidance and target tracking action must be generated immediately based on the real-time perceptual information. This paper presents a robust approach to integrating VPH+ (enhanced vector polar histogram) and MPC (model predictive control). VPH+ is applied to calculate the desired direction for its environment perception ability and computational efficiency, while MPC is explored to perform a constrained model-predictive trajectory generation. This approach can be implemented in a reactive controller. Simulation experiments are performed in VREP to validate the proposed approach.