Real-Time Motion Planning for Agile Autonomous Vehicles

TLDR

Planning paths for agile autonomous vehicles in dynamic environments is highly complex, but randomized algorithms have shown promising potential for future platforms. The paper proposes a randomized path‑planning architecture for dynamical systems navigating fixed and moving obstacles. The architecture satisfies dynamic constraints, decouples control from planning, preserves convergence, ensures safety under limited computation, and works for ODE or hybrid vehicle models. Simulations with a ground robot and a small autonomous helicopter demonstrate the approach’s effectiveness.

Abstract

Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of finite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.