Deterministic robot path planning method for obstacle avoidance

A system comprising:

• at least one sensor;
• a multi-joint robotic arm;
• at least one processor;
• and
• a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• obtaining, from the at least one sensor, a current pose of the multi-joint robotic arm;
• calculating a minimum distance permitted between the multi-joint robotic arm and at least one obstacle identified between the current pose and a target;
• calculating a weighting factor, the weighting factor defining a relationship between a joint angle cost of joints within the multi-joint robotic arm and an obstacle-distance cost,
• wherein the weighting factor is iteratively learned over a predetermined number of iterations by minimizing a cost function over a plurality of iterations using, for each iteration, a random desired position of an end-effector of the multi-joint robotic arm;
• computing a trajectory from the current pose to the target by minimizing the cost function using the weighting factor;
• and
• initiating movement of the multi-joint robotic arm along the trajectory.

A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• obtaining, from at least one sensor, a current pose of a multi-joint robotic arm;
• calculating a minimum distance permitted between the multi-joint robotic arm and at least one obstacle identified between the current pose and a target;
• calculating a weighting factor, the weighting factor defining a relationship between a joint angle cost of joints within the multi-joint robotic arm and an obstacle-distance cost,
• wherein the weighting factor is iteratively learned over a predetermined number of iterations by minimizing a cost function over a plurality of iterations using, for each iteration, a random desired position of an end-effector of the multi-joint robotic arm;
• computing a trajectory from the current pose to the target by minimizing the cost function using the weighting factor;
• and
• initiating movement of the multi-joint robotic arm along the trajectory.