Optimal design of robotic work-cell through hierarchical manipulability maximization

The increasing requests for flexible robotic applications involving the rapid relocation of the robot manipulator, possibly mounted on a mobile base, imposes tolerance to imprecise positioning. The high manipulability of the nominally designed poses, i.e., the capacity to change the position and the orientation of a given robot joint configuration's end-effector, is often considered a proxy for robustness to imprecise positioning. This work presents a method for choosing target end-effector poses to manipulate bulky objects in complex environments. The paper proposes a two-layer optimizer connected in cascade to maximize the manipulability and achieve reasonable computational time. First, using a genetic algorithm (GA) allows a global search for a satisfactory solution to the target poses of the task at the same time. Subsequently, the output of the GA becomes the initial guess for the simulated annealing (SA) algorithm, which locally maximizes each pose's manipulability separately. The feasibility of the connecting trajectories and collisions are checked in both layers. Experiments show the method's ability to find excellent solutions within a limited time, considering a complex problem involving manipulating large objects in a cluttered environment. The simulations of three working scenarios allowed testing of the proposed method. The final validation of the algorithm was on two relevant industrial use-cases: the manipulation of sidewalls and the manipulation of cargo panels inside an aircraft fuselage.