Why Does IK Need "Multiple Solutions"?

Redundant manipulators have more degrees of freedom than task dimensions, so a single end-effector pose often corresponds to infinitely many joint solutions. Traditional numerical methods typically return only one solution, making it difficult to simultaneously satisfy obstacle avoidance, joint limits, and motion continuity.

My Approach

I used a Conditional Normalizing Flow to learn the mapping from end-effector pose to joint solution distribution:

• Given any target pose, the model can sample multiple feasible solutions in milliseconds;
• A constraint-based strategy then selects the optimal solution satisfying joint limits, obstacle avoidance, and continuity;
• Finally, a refined optimization algorithm tightens the end-effector error to industrial-grade accuracy.

Next Steps

Extend this approach to hyper-redundant manipulators and incorporate physical spatial constraint mechanisms to further improve solution feasibility.

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