I'm working on a motion planning project. In this post I placed the first video of the Motion Planner which I'm working on. The motion Planner uses a fuzzy control system to avoid obstacles and reach a specified goal. 37 fuzzy rules are defined on three different parameters to control the speed and direction of the agent.
The system is still immature. It's going to be combined with some machine learning techniques to enhance its performance but for now it just contains a fuzzy motion planner and the animations are few.
There are sets of parametric animations which their parameters are changing based on the commands come from the motion planner. For now the motion planner just controls the speed and direction of the agent.
As you can see in the video the agent is not always find the best and shortest way through the goal because it has no previous knowledge about the environment and it is exploring the environment while going through the goal. This technique has some pros and some cons.
The agent with the same fuzzy rulebase can avoid obstacles and reach the goal in different environments with different arrayed obstacles and no preprocessing phase is needed as you can see in the video.
The agent doesn't have any previous info about the environment and it can't find the best and shortest path through the goal.
The system performance should be improved after I add more animations to it and integrate it with some machine learning techniques. In my next posts I will update some other videos and share the progress of the work here.
Here is the video: