Deep reinforcement learning based mobile robot navigation: A review
Kai Zhu, Tao Zhang
Abstract
Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.
Topics & Concepts
Mobile robot navigationReinforcement learningMobile robotObstacle avoidanceComputer scienceArtificial intelligenceRobotNavigation systemObstacleHuman–computer interactionSocial robotRobot controlGeographyArchaeologyRobotic Path Planning AlgorithmsRobotics and Sensor-Based LocalizationReinforcement Learning in Robotics