Global-State-Free Obstacle Avoidance for Quadrotor Control in Air-Ground Cooperation

2025-10-28

AI Summary

To address the real-time collision avoidance challenge in UAV–UGV collaborative operations under conditions of absent global state estimation and obstacle motion prediction, this paper proposes a lightweight, global-localization-free avoidance control framework. The method operates directly in a non-inertial frame, constructing a velocity modulation matrix from a single-frame LiDAR point cloud—thereby eliminating explicit obstacle modeling and explicit motion forecasting. It integrates the relative-state-based CoNi-OA algorithm to close the perception–decision–control loop for UAVs. The framework is compatible with both static and dynamic environments, achieves sub-5-ms per-iteration latency, and generates safe, smooth, low-latency, collision-free trajectories in complex scenarios. Experimental results demonstrate significant improvements in robustness and real-time performance of heterogeneous platform collaboration.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

CoNi-MPC provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, Cooperative Non-inertial frame-based Obstacle Avoidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: (1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states; (2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction; and (3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios.

Problem

• Enables obstacle avoidance without global state estimation in UAV-UGV cooperation
• Uses single LiDAR frame for real-time collision-free trajectory generation
• Adapts to dynamic environments without obstacle modeling or prediction

Innovation

• Uses single-frame LiDAR data for obstacle avoidance
• Generates modulation matrix to adjust quadrotor velocity
• Operates without global states or obstacle prediction