Adaptive Robot Path Planning Using a Spiking Neuron Algorithm With Axonal Delays

A path planning algorithm for outdoor robots, which is based on neuronal spike timing, is introduced. The algorithm is inspired by recent experimental evidence for experience-dependent plasticity of axonal conductance. Based on this evidence, we developed a novel learning rule that altered axonal de...

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Published inIEEE transactions on cognitive and developmental systems Vol. 10; no. 2; pp. 126 - 137
Main Authors Hwu, Tiffany, Wang, Alexander Y., Oros, Nicolas, Krichmar, Jeffrey L.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Algorithms
Delays
Energy conservation
Heuristic algorithms
Neuromorphic chips
Neurons
Parallel processing
Path planning
Planning
plasticity
Power efficiency
Resistance
Roads
robotics
Robots
Shortest path planning
Spikes
Spiking
spiking neurons
Trajectory planning
Online AccessGet full text
ISSN2379-8920
2379-8939
DOI10.1109/TCDS.2017.2655539

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Abstract A path planning algorithm for outdoor robots, which is based on neuronal spike timing, is introduced. The algorithm is inspired by recent experimental evidence for experience-dependent plasticity of axonal conductance. Based on this evidence, we developed a novel learning rule that altered axonal delays corresponding to cost traversals and demonstrated its effectiveness on real-world environmental maps. We implemented the spiking neuron path planning algorithm on an autonomous robot that can adjust its routes depending on the context of the environment. The robot demonstrates the ability to plan different trajectories that exploit smooth roads when energy conservation is advantageous, or plan the shortest path across a grass field when reducing distance traveled is beneficial. Because the algorithm is suitable for spike-based neuromorphic hardware, it has the potential of realizing orders of magnitude gains in power efficiency and computational gains through parallelization.
AbstractList A path planning algorithm for outdoor robots, which is based on neuronal spike timing, is introduced. The algorithm is inspired by recent experimental evidence for experience-dependent plasticity of axonal conductance. Based on this evidence, we developed a novel learning rule that altered axonal delays corresponding to cost traversals and demonstrated its effectiveness on real-world environmental maps. We implemented the spiking neuron path planning algorithm on an autonomous robot that can adjust its routes depending on the context of the environment. The robot demonstrates the ability to plan different trajectories that exploit smooth roads when energy conservation is advantageous, or plan the shortest path across a grass field when reducing distance traveled is beneficial. Because the algorithm is suitable for spike-based neuromorphic hardware, it has the potential of realizing orders of magnitude gains in power efficiency and computational gains through parallelization.
Author Oros, Nicolas
Hwu, Tiffany
Krichmar, Jeffrey L.
Wang, Alexander Y.
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Snippet A path planning algorithm for outdoor robots, which is based on neuronal spike timing, is introduced. The algorithm is inspired by recent experimental evidence...
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SubjectTerms Algorithm design and analysis
Algorithms
Delays
Energy conservation
Heuristic algorithms
Neuromorphic chips
Neurons
Parallel processing
Path planning
Planning
plasticity
Power efficiency
Resistance
Roads
robotics
Robots
Shortest path planning
Spikes
Spiking
spiking neurons
Trajectory planning
Title Adaptive Robot Path Planning Using a Spiking Neuron Algorithm With Axonal Delays
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