Path planning and moving obstacle avoidance with neuromorphic computing

Motoki Sakurai, Yosuke Ueno, Masaaki Kondo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Neuromorphic computing has been getting attention because of its potential for fast and low-power computation, robustness, and learning capability. Though traditional machine learning applications are main target of neuromorphic computing, its characteristic of parallel and distributed processing with simple spike-based signals is useful for other types of applications such as a shortest path finding problem (SPFP) on a graph. Prior work discussed approaches for mapping SPFP to a spiking neural network (SNN). In this paper, we propose an SNN algorithm for path planning with moving obstacles. In real world situation, there are many moving obstacles (such as other cars for an autonomous driving car and human for a moving robot) around a target agent which tries to optimize its own path to the goal. Finding an effective path in such an environment is not an easy task since behavior of obstacles is sometimes unknown and there must be a huge number of candidate paths to go. Traditional methods for SPFP with a general CPU may not be effective since it should compare candidate paths and select the most suitable one every time step. We consider two agents with SNN which tries to achieve two goals: 'reaching its destination promptly' and 'avoiding moving obstacles properly'. Thanks to SNN properties, the agent can learn and estimate how the obstacles move. We compare the proposal approaches with an existing method on a 2D grid graph and the result shows that the proposal agents can select proper paths depending on obstacles' movement.

Original languageEnglish
Title of host publicationISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-215
Number of pages7
ISBN (Electronic)9781665438629
DOIs
Publication statusPublished - 2021 Mar 4
Externally publishedYes
Event2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021 - Virtual, Nagoya, Japan
Duration: 2021 Mar 42021 Mar 6

Publication series

NameISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics

Conference

Conference2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021
Country/TerritoryJapan
CityVirtual, Nagoya
Period21/3/421/3/6

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Safety Research

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