Multi-objective Sensor Planning for Efficient and Accurate Object Reconstruction

A novel approach for sensor planning, which incorporates multi-objective optimization principals into the autonomous design of sensing strategies, is presented. The study addresses planning the behavior of an automated 3D inspection system, consisting of a manipulator robot in an Eye-on-Hand configuration. Task planning in this context is stated as a constrained multi-objective optimization problem, where reconstruction accuracy and robot motion efficiency are the criteria to optimize. An approach based on evolutionary computation is developed and experimental results shown. The obtained convex Pareto front of solutions confirms the conflict among objectives in our planning.