一种仿生软体机器鱼

发表于 2022-03-25 更新于 2023-02-10 分类于 Research ，学习 Disqus：

前言

水下机器人技术不断发展，应用不断增加，仿生机器鱼作为水下机器人中的一种，获得越来越多的关注。仿生机器鱼同时作为一种仿生学的产物，具有很多先天的优势。例如，运行噪音小，体积小，灵活性高，可以很好的融入水下生态环境。

一、仿生软体机器鱼的机械设计与材料选择

优良的机械设计是仿生机器人的应用基础。

Reinforcement Learning Based Path Planning for Robots

发表于 2021-11-29 更新于 2023-02-10 分类于 Research ，学习 Disqus：

These days I have studied unmanned driving path planning and reinforcement learning, and have gained some preliminary knowledges.

In reinforcement learning, there are some simple models, such as letting the agents find a way out of the maze, which may be similar to unmanned driving path planning. To do this kind of reinforcement learning problems, the most used way is try-and-error. That is a lot of attempts, according to the final return (find the exit or not) to update themselves in a certain state (position). Then the agents select a certain action (moving forward, backward, left, and right), so as to explore an optimal policy.

The advantage of using reinforcement learning to do this is that you only need to tell the agents your purpose (find the way out), without telling them how to do it. As long as you try more times, the agent can always find a more ideal strategy (the value function converges), which is the path of action. The specific algorithms can be Monte Carlo or Q-learning. You also can try the Dyna-Q algorithm.

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Another Formation Control Code

发表于 2021-11-19 更新于 2023-02-10 分类于 Research Disqus：

This is another SOM based formation control code in 2-D and 3-D space. Please refer to Github

Email: lixin850224@163.com

Basic Formation Control using SOM Codes on Github

发表于 2021-11-19 更新于 2023-02-10 分类于 Research Disqus：

This is my preliminary codes using SOM method (Self-organizing Map Neural Network) to help you with your own formation control research. Please refer to Github

Authors: Xin Li

Email: lixin850224@163.com

Shanghai Maritime University