What are the best practices for implementing real-time motion planning algorithms in humanoid robots?

[MonsterBot]

What are the best practices for implementing real-time motion planning algorithms in humanoid robots?

I'm working on a project involving humanoid robots and I'm particularly interested in the software aspects of real-time motion planning. Given the complexity of humanoid robot movements, what are the most effective algorithms and frameworks currently available for this purpose?

Some specific questions I have are:

1. What libraries or tools do you recommend for developing and testing real-time motion planning algorithms?
2. How do you handle dynamic obstacle avoidance while maintaining smooth and natural movements?
3. What strategies are effective for optimizing the computational efficiency of these algorithms to ensure quick response times?
4. Are there any open-source projects or resources that have been particularly helpful in your work?

I'd appreciate any insights, experiences, or resources you can share that might help me and others in the community improve our approach to real-time motion planning in humanoid robots.

 

[artbyrobot]

IMO you'd want a 3d environment containing a model of the robots body in its current position and joint angles as well as any momentum information being considered, and a 3d model of the environment. You'd put in collision boxes in this 3d environment like a videogame has for objects. You'd then chart out pathing in simulation and possibly chart multiple paths to test them and find the best path and plot out possibly seconds ahead to simulate a predicted set of pathing for the humanoid and all objects in the environment based on information about all objects in its environment and their individual movement and projected movement if any as well. Smoothness of motion would be based on observations of humans use of their bodies that the AI can note down to create rules lists for itself in how to move like humans do. This would be done by having the robot observe video footage of humans doing similar tasks during the robot's training phase. The robot would then be able to emulate the most humanlike movement speeds and accelerations within its simulated 3d environment. After all modeling and pathing simulations are done and a best course of action is decided upon, the robot will then calculate all joint angle changes in its body and send out these commands to its various microcontrollers for them to implement these movements to the motor controllers. It would do this repeatedly often updating all of its calculations based on fast feedback from its visual systems and sensors and adjusting for errors in past calculations and planning as well as adjusting for sudden and unexpected changes in its environment and recalculating swiftly based on these factors. All of the above must be done very fast and efficient. I personally and rolling my own 3d rendering pipeline from scratch, physics engine from scratch, vision system from scratch, everything from scratch. It's best that way IMO for maximizing your understanding of everything involved and having everything be efficient and well integrated. To use off the shelf 3rd party stuff I'm avoiding that as much as possible but that's just my approach. I did not like my options for ANYTHING 3rd party for any uses for my humanoid other than the operating system (Windows).