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arXiv · 2505.09475

aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search

Tanmay P. Patel·Connor Wilson·Ellina R. Zhang·Morgan Tran·Chang Keun Paik·Steven L. Waslander·Timothy D. Barfoot
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Abstract

This paper presents aUToPath, a unified online framework for global path-planning and control to address the challenge of autonomous navigation in cluttered urban environments. A key component of our framework is a novel hybrid planner that combines pre-computed lattice maps with dynamic free-space sampling to efficiently generate optimal driveable corridors in cluttered scenarios. Our system also features sequential convex programming (SCP)-based model predictive control (MPC) to refine the corridors into smooth, dynamically consistent trajectories. A single optimization problem is used to both generate a trajectory and its corresponding control commands; this addresses limitations of decoupled approaches by guaranteeing a safe and feasible path. Simulation results of the novel planner on randomly generated obstacle-rich scenarios demonstrate the success rate of a free-space Adaptively Informed Trees* (AIT*)-based planner, and runtimes comparable to a lattice-based planner. Real-world experiments of the full system on a Chevrolet Bolt EUV further validate performance in dense obstacle fields, demonstrating no violations of traffic, kinematic, or vehicle constraints, and a 100% success rate across eight trials.

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Tanmay P. Patel, Connor Wilson, Ellina R. Zhang, Morgan Tran, Chang Keun Paik, Steven L. Waslander, Timothy D. Barfoot. 2025-05-14. aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search. https://arxiv.org/abs/2505.09475

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