Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming.

Area of Science:

• Robotics
• Artificial Intelligence
• Control Theory

Background:

• Probabilistic sampling-based motion planning algorithms efficiently generate collision-free robot paths.
• These methods, while probabilistically complete, often produce trajectories with redundant or jerky motions.
• Optimizing trajectories for shortness, smoothness, and obstacle avoidance is crucial for robotics applications.

Purpose of the Study:

• To propose a new trajectory optimization technique for refining robot motion planning paths.
• To transform polygon collision-free paths into smooth, optimized trajectories.
• To handle trajectories with task constraints and hard kinematic limitations.

Main Methods:

• Trajectory optimization using quadratic programming in the parameter space.
• Conversion of collision avoidance to linear constraints for guaranteed safety.
• Application of a projection operator for optimizing trajectories with hard kinematic constraints.

Main Results:

• The proposed technique effectively removes redundant motions from robot trajectories.
• Collision avoidance conditions are converted to linear constraints, ensuring absolute safety.
• Optimization of trajectories with hard kinematic constraints, such as maintaining stability or dual-robot coordination, is achieved.

Conclusions:

• The developed trajectory optimization method is feasible and effective.
• Experimental results demonstrate superior performance compared to existing trajectory optimization methods.
• This technique enhances the quality and applicability of robot motion planning.