基于迭代优化的统一规划控制框架，实现实时竞速中避障与提速兼顾。
IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance
- 利用历史数据迭代优化，融合避障与时间成本
- 仿真中对所有动态障碍场景均超越现有方法
- 计算量低且可并行，适合真实竞速场景
本文提出一种名为 IteraOptiRacing 的统一规划-控制策略，用于在自主竞速环境中与其他赛车竞争。该策略基于针对迭代任务的迭代线性二次调节器（i2LQR），通过利用自车历史数据迭代优化，在存在周围竞速障碍物的情况下提升单圈用时表现。该统一方法同时考虑多辆移动车辆的避障与时间成本最小化，生成无碰撞且时间最优的轨迹。算法具有恒定的低计算开销，适合并行计算，可在竞赛场景中实现实时运行。为验证性能，我们在高保真仿真器中对赛道上随机生成的多个动态智能体进行了测试。结果表明，所提策略在所有随机生成的自主竞速场景中均优于现有方法，显著提升了自车的机动能力。
原文摘要 · Abstract (English)
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.
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