DForC: A real-time method for reaching, tracking and obstacle avoidance in humanoid robots

We present the Dynamic Force Field Controller (DForC), a reliable and effective framework in the context of humanoid robotics for real-time reaching and tracking in presence of obstacles. It is inspired by well established works based on artificial potential fields, providing a robust basis for sidestepping a number of issues related to inverse kinematics of complex manipulators. DForC is composed of two layers organized in descending order of abstraction: (1) at the highest level potential fields are employed to outline on the fly collision-free trajectories that serve to drive the robot end-effector toward fixed or moving targets while accounting for obstacles; (2) at the bottom level an optimization algorithm is exploited in place of traditional techniques that resort to the Transposed or Pseudo-Inverse Jacobian, in order to deal with constraints specified in the joints space and additional conditions related to the robot structure. As demonstrated by experiments conducted on the iCub robot, our method reveals to be particularly flexible with respect to environmental changes allowing for a safe tracking procedure, and generating reliable paths in practically every situation.