| This Paper:Browse 406 Download 0 |
码上扫一扫！ |
| Sailboat navigation control system based on spiking neural networks |
| NelsonSantiagoGiraldo1,SebastiánIsaza1,RicardoAndrésVelásquez1 |
|
|
| (1 Department of Electronics and Telecommunications Engineering, University of Antioquia, 67 st, Medellin 050010, Antioquia, Colombia) |
|
| 摘要: |
| In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks
(SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized
hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a
simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking
neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following
a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the
mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network
controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed
navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally,
we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its
performance. |
| 关键词: Sailboat · Control · Spiking neuron · Reinforcement learning · BindsNet · USVSim |
| DOI：https://doi.org/10.1007/s11768-023-00150-1 |
|
| 基金项目:Open Access funding provided by Colombia Consortium. This work was supported by the University of Antioquia with project PRG2017-16182 and by the Colombia Scientific Program within the framework of the call Ecosistema Científico (Contract No. FP44842-218-018). |
|
| Sailboat navigation control system based on spiking neural networks |
| Nelson Santiago Giraldo1,Sebastián Isaza1,Ricardo Andrés Velásquez1 |
| (1 Department of Electronics and Telecommunications Engineering, University of Antioquia, 67 st, Medellin 050010, Antioquia, Colombia) |
| Abstract: |
| In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks
(SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized
hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a
simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking
neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following
a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the
mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network
controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed
navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally,
we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its
performance. |
| Key words: Sailboat · Control · Spiking neuron · Reinforcement learning · BindsNet · USVSim |
