Problem
Research questions and friction points this paper is trying to address.
Ackermann-steered robots
safe local navigation
unmapped environments
obstacle avoidance
local path planning
Safe Local Navigation for Ackermann-Steered Robots in Unmapped Environments
🤖 AI Summary
This work addresses the problem of local safe navigation for Ackermann-steered robots in mapless environments without global goals. The authors propose a real-time, perception-only obstacle avoidance method that identifies the largest open sector ahead to determine a safe heading and constructs left–right boundary constraints. A convex quadratic program is then employed to maximize clearance between the vehicle and surrounding obstacles, and a feedback linearization controller tracks the resulting smooth reference trajectory. The approach achieves high computational efficiency while ensuring both safety and trajectory smoothness. Experimental results demonstrate that, compared to existing exploration-based planners, the proposed method significantly reduces computation time and yields safer, more reliable paths.
📝 Abstract
A control framework is proposed for safe local navigation of mobile robots equipped with Ackermann steering in unmapped environments where a global goal is absent. Based on local obstacle detections, the safest heading angle is determined along the direction of the largest open space ahead of the vehicle. Guided by this direction, bounding lines are constructed on the left and right sides of the vehicle to achieve obstacle separation. These bounding lines are obtained by solving a convex quadratic optimization that maximizes vehicle-to-obstacle clearance. Optionally, conditions are imposed on the bounding lines to preserve parallelism and smooth abrupt changes from prior control steps. A feedback-linearizing controller is then used to regulate the vehicle's distance from one or both bounding lines, effectively enabling tracking of a local reference path that preserves safety through obstacle clearance maximization. Open-source code is included for the application of this control scheme. Experimental results demonstrate that the proposed method produces safer navigation paths with significantly shorter computation times, compared to some existing exploration-based planners.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Ackermann steering
safe local navigation
convex quadratic optimization
obstacle clearance maximization
feedback-linearizing control
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