Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments

2020/02/12 by Jiahao Lin, Lin, Jiahao, Hai Zhu +3 · 4 citations
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization

paper · pdf · doi:10.48550/arxiv.2002.04920

openalex publication_date 2020/02/12 · openalex created_date 2020/02/24 · openalex updated_date 2026/07/28

Abstract

In this paper, we present an on-board vision-based approach for avoidance of moving obstacles in dynamic environments. Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides the estimated position, velocity and size of the obstacles. Robust collision avoidance is achieved by formulating a chance-constrained model predictive controller (CC-MPC) to ensure that the collision probability between the micro aerial vehicle (MAV) and each moving obstacle is below a specified threshold. The method takes into account MAV dynamics, state estimation and obstacle sensing uncertainties. The proposed approach is implemented on a quadrotor equipped with a stereo camera and is tested in a variety of environments, showing effective on-line collision avoidance of moving obstacles.

Cited by

• Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter
• Threat-Aware UAV Dodging of Human-Thrown Projectiles with an RGB-D Camera
• MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints
• Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots

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