Adaptive Dual-Headway Unicycle Pose Control and Motion Prediction for Optimal Sampling-Based Feedback Motion Planning

TL;DR

This paper tackles safe, smooth, and efficient motion planning for nonholonomic unicycle-like robots by introducing an adaptive dual-headway pose control. It uses a headway point in front of the robot and a tailway point behind the goal to achieve asymptotic convergence to a goal pose, and it provides an explicit convex motion-prediction bound based on the convex hull of critical pose points for safety verification. The authors integrate this control into optimal sampling-based feedback motion planning, leveraging dual-headway translation and orientation distances to minimize travel and turning effort, and demonstrate superior performance over standard Euclidean translation and cosine orientation measures. The approach enables reliable, obstacle-aware planning with provable safety bounds and smoother trajectories in numerical experiments, highlighting practical benefits for autonomous logistics, mobility, and service robotics.

Abstract

Safe, smooth, and optimal motion planning for nonholonomically constrained mobile robots and autonomous vehicles is essential for achieving reliable, seamless, and efficient autonomy in logistics, mobility, and service industries. In many such application settings, nonholonomic robots, like unicycles with restricted motion, require precise planning and control of both translational and orientational motion to approach specific locations in a designated orientation, such as for approaching changing, parking, and loading areas. In this paper, we introduce a new dual-headway unicycle pose control method by leveraging an adaptively placed headway point in front of the unicycle pose and a tailway point behind the goal pose. In summary, the unicycle robot continuously follows its headway point, which chases the tailway point of the goal pose and the asymptotic motion of the tailway point towards the goal position guides the unicycle robot to approach the goal location with the correct orientation. The simple and intuitive geometric construction of dual-headway unicycle pose control enables an explicit convex feedback motion prediction bound on the closed-loop unicycle motion trajectory for fast and accurate safety verification. We present an application of dual-headway unicycle control for optimal sampling-based motion planning around obstacles. In numerical simulations, we show that optimal unicycle motion planning using dual-headway translation and orientation distances significantly outperforms Euclidean translation and cosine orientation distances in generating smooth motion with minimal travel and turning effort.

Figures (1)

• Figure 1: Adaptive dual-headway unicycle pose control continuously moves the headway point of the unicycle pose, which is in front of the unicycle, toward the tailway point of the goal pose, located behind the goal. (left) Example closed-loop trajectories of dual-headway unicycle control for a smooth lane-changing scenario with associated motion prediction bounds. (right) Example application of dual-headway unicycle control and motion prediction for optimal sampling-based motion planning around obstacles, minimizing the total dual-headway translation and orientation distance.