Neural Fields for Local Path Planning

In this article we introduce a neural øeld approach for local path planning of an autonomous mobile robot. The robot's heading direction is determined by the localized peak and its velocity by the maximum activation in the øeld. We emphasize the neural øeld's ability to keep the path planning stable even in the case of noisy sensor data or varying environments. The theoretical framework is validated by an implementation on our mobile service robot called 'ARNOLD'. Since its only sensor is an active stereo camera head, we highlight the importance of gaze control and low-level short-term memory for local path planning, particularly in cluttered indoor environments. 1 Introduction Autonomous mobile robots move in a partially unknown and dynamically changing environment. Hence, the path planning for a given target position has to be reliable and stable even in situations where the robot has only incomplete or noisy sensor data. In addition, the robot should be able to autonomously acquir..