Synthesis and Control of Whole-body Behaviors in Humanoid Systems

A great challenge for robotic systems is their ability to carry on complex manipulation and locomotion tasks while responding to the changing environment. To allow robots to operate in human environments there is a strong need to develop new control architectures that can provide advanced task capabilities and interactive skills. These architectures must be effective in coordinating whole-body behaviors for various control objectives while complying with balance stability, contact stance, and other dynamic constraints. In addition, to facilitate the integration of robots in human environments, it is desirable for their motions and task behaviors to be compatible with those of humans. In this thesis, we present a control methodology for the synthesis of realtime whole-body control behaviors in humanoid systems. The work is presented in three parts. First, we establish mathematical foundations that characterize the kinematic and dynamic behaviors of task and postural criteria under balance and contact stability constraints. We identify the dynamic behavior of postural tasks operating in the null space of operational tasks and we develop task-oriented controllers in postural space. These controllers are used to accomplish secondary goals or to optimize postural criteria without affecting priority tasks. Based on task and posture control decompositions we define recursive structures with multiple priority levels. These structures allow us to create controllers for all aspects of motion while ensuring that critical tasks are accomplished first. Exploiting prioritization, we address the control of dynamic constraints as priority tasks and we project operational tasks and postural criteria in the null space of all acting constraints. This strategy prevents lower priority tasks from violating the acting constraints. Second, we develop a variety of controllers to address the different aspects of the robot’s motion. We propose position and force controllers to control the various task effectors of the robot. We use potential fields to handle reactively dynamic constraints such as balance stability, joint limits, obstacle avoidance, and self collisions. We develop posture controllers to enhance overall performance in terms of available workspace, resemblance to