Hybrid Feedback Control for Global Navigation with Locally Optimal Obstacle Avoidance in n-Dimensional Spaces

2024-12-29

AI Summary

Autonomous navigation in n-dimensional spaces with spherical obstacles poses challenges in ensuring collision-free, smooth, and globally stable motion under uncertain environments. Method: This paper proposes a dual-mode hybrid feedback control strategy that dynamically switches between “moving toward the goal” and “locally optimal obstacle avoidance,” guaranteeing global asymptotic stability, collision avoidance, and velocity continuity. The approach employs piecewise-continuous feedback laws and hybrid dynamical modeling, with a geometric-distance-based real-time mode-switching mechanism compatible with range sensors—enabling operation in prior-unknown environments. Contribution/Results: To the best of our knowledge, this is the first such framework integrating sensor-compatible switching with provable convergence and locally optimal avoidance trajectories. Implemented in ROS and validated on TurtleBot 4 and 2D/3D simulations, the method achieves shorter paths, smoother trajectories, and low computational latency—demonstrating real-time performance and practical deployability.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Agentic search

Abstract

We present a hybrid feedback control framework for autonomous robot navigation in n-dimensional Euclidean spaces cluttered with spherical obstacles. The proposed approach ensures safe navigation and global asymptotic stability (GAS) of the target location by dynamically switching between two operational modes: motion-to-destination and locally optimal obstacle-avoidance. It produces continuous velocity inputs, ensures collision-free trajectories and generates locally optimal obstacle avoidance maneuvers. Unlike existing methods, the proposed framework is compatible with range sensors, enabling navigation in both a priori known and unknown environments. Extensive simulations in 2D and 3D settings, complemented by experimental validation on a TurtleBot 4 platform, confirm the efficacy and robustness of the approach. Our results demonstrate shorter paths and smoother trajectories compared to state-of-the-art methods, while maintaining computational efficiency and real-world feasibility.

Problem

• Robot Navigation
• Optimal Obstacle Avoidance
• Multi-dimensional Space Control

Innovation

• Intelligent Path Planning
• Adaptive Speed Control
• Sensor Compatibility