Curiosity-based Robot Navigation under Uncertainty in Crowded Environments

Mobile robots have become more and more popular in our daily life. In large-scale and crowded environments, how to navigate safely with localization precision is a critical problem. To solve this problem, we proposed a curiosity-based framework that can find an effective path with the consideration of human comfort, localization uncertainty, crowds, and the cost-to-go to the target. Three parts are involved in the proposed framework: the distance assessment module, the curiosity gain of the information-rich area, and the curiosity negative gain of crowded areas. The curiosity gain of the information-rich area was proposed to provoke the robot to approach localization referenced landmarks. To guarantee human comfort while coexisting with robots, we propose curiosity gain of the spacious area to bypass the crowd and maintain an appropriate distance between robots and humans. The evaluation is conducted in an unstructured environment. The results show that our method can find a feasible path, which can consider the localization uncertainty while simultaneously avoiding the crowded area. Curiosity-based Robot Navigation under Uncertainty in Crowded Environments

Mobile Robot Path Planning in Dynamic Environments: A Survey

The environment that the robot operating in is becoming more and more complex, which poses great challenges on robot navigation. This paper gives an overview of the navigation framework for robot running in dense environment. The path planning in the navigation framework of mobile robots is divided into global planning and local planning according to the planning scope and the executability. Robot navigation is a multi-objective problem, which not only needs to complete the given tasks but also needs to simultaneously maintain the social comfort level. Consequently, we focus on the reinforcement learning-based path planning algorithms and analyze the development status, advantages, and disadvantages of the existing algorithms. Besides, path planning in a dynamic environment for robots will be further studied in the future in the areas of the advanced algorithm, hybrid algorithm, mu10.15878/j.cnki.instrumentation.2019.02.010lti-robot collaboration, social model, and artificial intelligence algorithm combination.