MOPRP: Multiobjective optimization on preoperative robot placement for robot-assisted dental implant surgery

In robot-assisted dental implant surgery, properly arranging the robot system are important for performance. Following this, a multiobjective optimization approach for preoperative robot placement (MOPRP) is proposed. Firstly, after kinematics modeling, the 9-dimensional planning of both the joint’s angles and the base’s position is simplified into 3-dimensional planning of merely the base’s position, building the design variables. The simplification is based on a multiple inverse kinematics (IK) selection strategy, which contains a collision-free principle and score-based selection. Secondly, three multiobjective indices, including the error range index, dexterity index, and joint variation index, are derived to evaluate placement quality. Subsequently, a Pareto front is generated through the multiobjective genetic algorithm, offering a group of competitive solutions. Lastly, by selecting a favorable solution from the Pareto front and checking its 3D preview, the placement planning can be done. Both numerical tests and experimental validations are conducted, demonstrating that the result approximately ranks in the 80%–90%, 99%, and 80%–90% percentile respectively for the three indices. The results show that MOPRP can provide enhanced dexterity, better accuracy, and less joint variation, with a pretty system layout, verifying its effectiveness and feasibility as a useful planning tool.