A real-time multi-constraints obstacle avoidance method using LiDAR

TL;DR

This paper introduces a real-time obstacle avoidance method for autonomous robots using LiDAR data, combining multi-constraints planning with an improved Ant Colony Optimization to navigate dynamic environments efficiently.

Contribution

The proposed method uniquely integrates multi-constraints planning with real-time LiDAR data processing and an enhanced ACO algorithm for dynamic obstacle avoidance.

Findings

Effective in dynamic environments with moving obstacles

Achieves real-time performance with low computational requirements

Validated through simulations and real-world experiments

Abstract

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments.