用可视图与预测控制实现人形机器人实时避障与追击。
Model Predictive Control with Visibility Graphs for Humanoid Path Planning and Tracking Against Adversarial Opponents
- 构建动态可视图，规划全局路径并考虑转向成本。
- 采用改进的模型预测控制，实现实时轨迹跟踪与碰撞规避。
- 在120Hz~400Hz下运行，适合复杂对抗场景的快速响应。
本文介绍了在RoboCup 2024成人尺寸自主人形足球联赛中帮助队伍夺冠的技术：全场比赛未败，6场比赛进45球，决赛以6比1取胜。主要挑战包括双足行走带来的测量噪声和有限视场（FOV），以及障碍物频繁突现于路径中。有时估计器会将机器人置于硬约束内部。因此，我们提出DAVG（动态增强可视图）进行长期路径规划，基于障碍物和目标姿态激活关键区域，并通过状态扩展考虑转向成本，对大尺寸机器人尤为重要。路径由DAVG生成的离散点线性插值而成。采用改进的cf-MPC（无碰撞模型预测控制）实现短期轨迹跟踪，无需切换控制形式即可兼顾机器人动力学与避障约束。非线性版本运行约120 Hz，二次版本达约400 Hz，确保实时重规划与响应。
原文摘要 · Abstract (English)
In this paper we detail the methods used for obstacle avoidance, path planning, and trajectory tracking that helped us win the adult-sized, autonomous humanoid soccer league in RoboCup 2024. Our team was undefeated for all seated matches and scored 45 goals over 6 games, winning the championship game 6 to 1. During the competition, a major challenge for collision avoidance was the measurement noise coming from bipedal locomotion and a limited field of view (FOV). Furthermore, obstacles would sporadically jump in and out of our planned trajectory. At times our estimator would place our robot inside a hard constraint. Any planner in this competition must also be be computationally efficient enough to re-plan and react in real time. This motivated our approach to trajectory generation and tracking. In many scenarios long-term and short-term planning is needed. To efficiently find a long-term general path that avoids all obstacles we developed DAVG (Dynamic Augmented Visibility Graphs). DAVG focuses on essential path planning by setting certain regions to be active based on obstacles and the desired goal pose. By augmenting the states in the graph, turning angles are considered, which is crucial for a large soccer playing robot as turning may be more costly. A trajectory is formed by linearly interpolating between discrete points generated by DAVG. A modified version of model predictive control (MPC) is used to then track this trajectory called cf-MPC (Collision-Free MPC). This ensures short-term planning. Without having to switch formulations cf-MPC takes into account the robot dynamics and collision free constraints. Without a hard switch the control input can smoothly transition in cases where the noise places our robot inside a constraint boundary. The nonlinear formulation runs at approximately 120 Hz, while the quadratic version achieves around 400 Hz.
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