使用深度强化学习的基于实体的避障机器人导航
Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning
AI总结：
针对动态环境中机器人与多类实体交互的安全导航问题，提出利用实体类型信息的深度强化学习避障方法及新型奖励函数，并通过优化算法加速训练，实验表明其性能优于现有方法。
摘要：
在动态环境中高效导航对于与移动智能体和静态障碍物交互的自主机器人至关重要。我们提出了一种新颖的深度强化学习方法，该方法基于特定安全要求改进机器人导航以及与不同类型智能体和障碍物的交互。我们的方法利用有关实体类型的信息，改进避障并确保更安全的导航。我们引入了一种新的奖励函数，该函数因机器人靠近或碰撞不同实体（例如成人、骑自行车者、儿童和静态障碍物）而对其进行惩罚，同时也鼓励机器人向目标前进。我们提出了一种优化算法，可显著加快训练、验证和测试阶段，从而在复杂环境中实现高效学习。综合实验表明，我们的方法始终优于最先进的导航和避障方法。
英文摘要：
Efficient navigation in dynamic environments is crucial for autonomous robots interacting with moving agents and static obstacles. We present a novel deep reinforcement learning approach that improves robot navigation and interaction with different types of agents and obstacles based on specific safety requirements. Our approach uses information about the entity types, improving collision avoidance and ensuring safer navigation. We introduce a new reward function that penalizes the robot for being close to or colliding with different entities such as adults, bicyclists, children, and static obstacles, while also encouraging the robot's progress toward the goal. We propose an optimized algorithm that significantly accelerates the training, validation, and testing phases, enabling efficient learning in complex environments. Comprehensive experiments demonstrate that our approach consistently outperforms state-of-the-art navigation and collision avoidance methods.
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