IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance

TL;DR

This paper introduces IteraOptiRacing, a unified real-time planning-control framework based on i2LQR, that improves autonomous racing performance by optimizing trajectories considering obstacle avoidance and time efficiency.

Contribution

The paper proposes a novel unified planning-control strategy using i2LQR for real-time autonomous racing, integrating obstacle avoidance and time optimization with low computational load.

Findings

Outperforms existing methods in simulated racing scenarios.

Enables collision-free, time-optimal trajectories.

Operates efficiently in real-time with parallel computing.

Abstract

This paper presents a unified planning-control strategy for competing with other racing cars called IteraOptiRacing in autonomous racing environments. This unified strategy is proposed based on Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which can improve lap time performance in the presence of surrounding racing obstacles. By iteratively using the ego car's historical data, both obstacle avoidance for multiple moving cars and time cost optimization are considered in this unified strategy, resulting in collision-free and time-optimal generated trajectories. The algorithm's constant low computation burden and suitability for parallel computing enable real-time operation in competitive racing scenarios. To validate its performance, simulations in a high-fidelity simulator are conducted with multiple randomly generated dynamic agents on the track. Results show that the proposed strategy outperforms existing methods across all randomly generated autonomous racing scenarios, enabling enhanced maneuvering for the ego racing car.