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

TL;DR

aUToPath introduces a unified online framework combining hybrid lattice and free-space search for efficient, safe, and smooth autonomous navigation in complex urban environments, validated through simulations and real-world tests.

Contribution

The paper proposes a novel hybrid planner integrating lattice maps with free-space sampling and a unified MPC approach for trajectory generation and control.

Findings

High success rate in obstacle-rich scenarios

Comparable runtimes to lattice-based planners

Successful real-world validation on a Chevrolet Bolt

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.