Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a nonlinear model predictive control (NMPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typically one. For a feasible initialization, the approach is guaranteed to find a feasible solution, i.e. it preserves feasibility. Our experimental evaluation shows that CIAO outperforms state of the art baselines in terms of planning efficiency and path quality. Experiments on a robot with 12 states show that it also scales to high-dimensional systems. Furthermore real-world experiments demonstrate its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments.

中文翻译：

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一种使用凸内近似的 NMPC 方法进行在线运动规划并保证避免碰撞

尽管移动机器人已经存在了几十年，但轨迹优化和连续时间避免碰撞仍然是积极研究的主题。现有方法在路径质量、计算复杂性和运动动力学可行性之间进行权衡。这项工作使用非线性模型预测控制 (NMPC) 框架来解决该问题，该框架基于碰撞避免约束的新型凸内近似。所提出的凸内逼近 (CIAO) 方法在很少的迭代中找到了运动动力学上可行的连续时间无碰撞轨迹，通常是一次。对于可行的初始化，该方法保证找到可行的解决方案，即它保留了可行性。我们的实验评估表明，CIAO 在规划效率和路径质量方面优于最先进的基线。对具有 12 个状态的机器人进行的实验表明，它也可以扩展到高维系统。此外，真实世界的实验证明了其在动态环境中将轨迹优化和跟踪统一起来以进行安全运动规划的能力。