Robot Motion Planning

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Overview

Robot motion planning determines how a robot can move from a start state to a goal while respecting obstacles, its own geometry and dynamics, and task requirements. Classical approaches include graph search, sampling-based planners such as PRM and RRT, potential fields, and trajectory optimization or model predictive control; newer methods use learned policies and generative models to propose or refine motions. Planning is also combined with perception, feedback control, temporal-logic specifications, and task-level reasoning for settings such as manipulation, navigation, and multi-robot coordination. A central challenge is balancing speed and generalization in uncertain, dynamic environments with reliable execution and safety guarantees, especially when learned plans must transfer from simulation to physical robots.

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Mar 22, 2025

From Kinematic Motion Planners to Dynamic Autonomous Navigation with Obstacle Avoidance (Extended version)

Kinematic motion planners are among the most widely used control approaches in robotic applications. By modeling the robot as a first-order system, they generate a feedback-based desired velocity field that guides the robot toward a target while avoiding obstacles. However, extending such feedback planners to systems with higher-order dynamics, while preserving safety and stability properties, is not a straightforward task. In the present work, we propose an approach that adapts existing feedback-based kinematic motion planners to second-order autonomous systems while retaining their safety and almost global asymptotic stability guarantees. We consider two general classes of kinematic motion planners: those derived from navigation functions and those defined directly through desired velocity fields without relying on an underlying navigation function. To validate the proposed methodology, two feedback-based kinematic motion planners are adapted to second-order systems and evaluated both in simulations and experimentally.

Mayur Sawant, Abdelhamid Tayebi

Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada

Robotic ControlKinodynamic Motion PlanningSafe Motion PlanningMotion PlanningRobot Motion Planning
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