Fast Obstacle Avoidance Based on Real-Time Sensing

TL;DR

This paper presents a real-time obstacle avoidance method for robots that combines high-level commands with reactive sensing, effectively navigating complex environments with minimal computational delay.

Contribution

It introduces a novel control scheme that fuses sparse, asynchronous sensor data with analytical obstacle reconstruction for robust real-time collision avoidance.

Findings

Successfully avoided collisions in indoor and outdoor environments

Achieved 1 millisecond processing time for 30,000 data points

Ensured the robot does not get stuck when feasible paths exist

Abstract

Humans are remarkable at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance robust to partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance. The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. We can ensure that the agent does not get stuck when a feasible path exists between obstacles. The algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in a shared control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller on the onboard computer took 1 millisecond to evaluate over 30000 data points.