Task priority-based redundancy resolution of a 20 DoFs humanoid robot for object pick and place with minimum energy and dynamic balance

Abstract Humanoid robots are highly redundant, and finding whole-body optimal trajectories for various tasks is very complex. This paper proposes a method to find an energy-optimal, dynamically balanced, and collision-free trajectory of the 20 degrees of freedom humanoid robot in pick and place application. The task of pick and place is divided into three subtasks using the Pseudoinverse Jacobian method of redundancy resolution. The three subtasks are end effector trajectory represented by $\mathcal {T}_1$ , hip trajectory represented by $\mathcal {T}_2$ , and maximizing the manipulability represented by $\mathcal {T}_3$ . The Pseudoinverse Jacobian method is coupled with particle swarm optimization (PSO) to find the optimal trajectories. The main contribution of this paper is the decomposition of the whole-body task of the humanoid robot into three distinct subtasks to find energy-optimal, dynamically balanced, and obstacle-free trajectories. The concept of virtual surface is used to avoid dragging objects on the table surface. The problem is optimized with Particle Swarm Optimization. Simulations were conducted to pick up and place objects from a table and constrained spaces like a drawer. The results show that the robot can pick and place objects from defined locations on the table.