Teleoperation system for real world robots-adaptive robot navigation based on sensor fusion

The authors propose a teleoperation system with an autonomous robot which is able to solve tasks even without a large load for the operator and the system. Most teleoperation systems require skilled operators and expensive interfaces to solve tasks because they assume that the operator controls a robot completely. For these problems, we propose a teleoperation system which consists of an operation system and an autonomous robot. The operation system has a man-machine interface and allows a user to specify the working space and the tasks to be done. The autonomous robot follows the instruction from the operation system to solve the specific tasks. The paper focuses on navigation problems of the autonomous robot as an essential part of the proposed system. Namely, the autonomous robot should keep on the instructed paths in the real world to achieve a goal of the tasks. Our approach is based on a sensor fusion method based on two learning schemes: self-organizing map (SOM) and reinforcement learning. These learning schemes allow the system to be able to solve the tasks in an unreliable environment such as outdoors. Computational simulations reveal the effectiveness and robustness of the proposed method in the navigation problem