Human awareness in robot motion planning is crucial for seamless interaction with humans. Many existing techniques slow down, stop, or change the robot's trajectory locally to avoid collisions with humans. Although using the information on the human's state in the path planning phase could reduce future interference with the human's movements and make safety stops less frequent, such an approach is less widespread. This paper proposes a novel approach to embedding a human model in the robot's path planner. The method explicitly addresses the problem of minimizing the path execution time, including slowdowns and stops owed to the proximity of humans. For this purpose, it converts safety speed limits into configuration-space cost functions that drive the path's optimization. The costmap can be updated based on the observed or predicted state of the human. The method can handle deterministic and probabilistic representations of the human state and is independent of the prediction algorithm. Numerical and experimental results on an industrial collaborative cell demonstrate that the proposed approach consistently reduces the robot's execution time and avoids unnecessary safety speed reductions.

翻译：人类在机器人运动规划中的认识对于与人类的无缝互动至关重要。 许多现有技术在本地减缓、停止或改变机器人轨道以避免与人类碰撞。 虽然在路径规划阶段使用人类状态信息可以减少未来对人体运动的干扰,降低安全中断的频率, 但这种方法并不那么普遍。 本文提出了将人类模型嵌入机器人路径规划器的新颖方法。 该方法明确解决了将路径执行时间最小化的问题, 包括减速和因人类接近而停止。 为此, 它将安全速度限制转换为驱动路径优化的配置空间成本函数。 成本图可以根据人类的观察或预测状态更新。 该方法可以处理人类状态的确定性和概率性表现,并且独立于预测算法。 工业协作细胞的数值和实验结果表明, 拟议的方法会持续减少机器人执行时间, 避免不必要的安全速度降低 。