The autonomous navigation of mobile robots is of great interest in mobile robotics. Algorithms such as simultaneous localization and mapping (SLAM) and artificial potential field methods can be applied to known and mapped environments. However, navigating in an unknown, and unmapped environments is still a challenge. In this research, we propose an algorithm for mobile robot navigation in the near-shortest possible time toward a predefined target location in an unknown environment containing obstacles. The algorithm is based on a reinforcement learning paradigm with biologically realistic spiking neural networks. We make use of eligibility traces that are inherent to spiking neural networks to solve the delayed reward problem implicitly present in reinforcement learning. With this algorithm, we achieve a set of movement decisions for the mobile robot to reach the target in the near-shortest time.
The autonomous navigation of mobile robots is of great interest in mobile robotics. Algorithms such as simultaneous localization and mapping (SLAM) and artificial potential field methods can be applied to known and mapped environments. However, navigating in an unknown, and unmapped environments is still a challenge. In this research, we propose an algorithm for mobile robot navigation in the near-shortest possible time toward a predefined target location in an unknown environment containing obstacles. The algorithm is based on a reinforcement learning paradigm with biologically realistic spiking neural networks. We make use of eligibility traces that are inherent to spiking neural networks to solve the delayed reward problem implicitly present in reinforcement learning. With this algorithm, we achieve a set of movement decisions for the mobile robot to reach the target in the near-shortest time.
