Mahadevuni, Amarnath (2018-05). Autonomous Navigation Using Reinforcement Learning with Spiking Neural Networks. Master's Thesis. Thesis uri icon

abstract

  • 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.

publication date

  • May 2018