This paper proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a sub-goal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move towards the goal. Through a series of 3D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized. Multimedia demonstrations are available at https://www.youtube.com/watch?v=KneELRT8GzU&list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS.
翻译:本文提出了一种新颖的基于学习的控制策略,使移动机器人能够在充满静态障碍物和密集人群的环境中自主导航。该策略使用一种独特的输入数据组合来生成期望的转向角和前向速度:短时间内的激光雷达数据,关于附近行人的动力学数据和一个子目标点。该策略是在强化学习环境中训练的,并使用一种基于速度障碍的新型奖励函数来指导机器人主动避免行人碰撞并朝着目标前进。通过一系列3D模拟实验(最多达55个行人),该控制策略能够实现更好的碰撞避免和速度平衡(即更高的成功率和更快的平均速度),并且在不同行人密度和未知环境中具有更好的泛化能力。大量的硬件实验展示了这种策略在不用重新训练的情况下,可以直接在不同的实际环境中使用,以及在不同机器人平台上在高度受限制的静态环境中使用。最后,总结了若干可以应用于其他机器人学习系统的重要经验教训。多媒体演示可在https://www.youtube.com/watch?v=KneELRT8GzU&list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS处查看。