Visual servo control on a humanoid robot

Includes bibliographical referencesThis thesis deals with the control of a humanoid robot based on visual servoing. It seeks to confer a degree of autonomy to the robot in the achievement of tasks such as reaching a desired position, tracking or/and grasping an object. The autonomy of humanoid robots is considered as crucial for the success of the numerous services that this kind of robots can render with their ability to associate dexterity and mobility in structured, unstructured or even hazardous environments. To achieve this objective, a humanoid robot is fully modeled and the control of its locomotion, conditioned by postural balance and gait stability, is studied. The presented approach is formulated to account for all the joints of the biped robot. As a way to conform the reference commands from visual servoing to the discrete locomotion mode of the robot, this study exploits a reactive omnidirectional walking pattern generator and a visual task Jacobian redefined with respect to a floating base on the humanoid robot, instead of the stance foot. The redundancy problem stemming from the high number of degrees of freedom coupled with the omnidirectional mobility of the robot is handled within the task priority framework, allowing thus to achieve con- figuration dependent sub-objectives such as improving the reachability, the manipulability and avoiding joint limits. Beyond a kinematic formulation of visual servoing, this thesis explores a dynamic visual approach and proposes two new visual servoing laws. Lyapunov theory is used first to prove the stability and convergence of the visual closed loop, then to derive a robust adaptive controller for the combined robot-vision dynamics, yielding thus an ultimate uniform bounded solution. Finally, all proposed schemes are validated in simulation and experimentally on the humanoid robot NAO

Locomotion Trajectory Generation For Legged Robots

This thesis addresses the problem of generating smooth and efficiently executable locomotion trajectories for legged robots under contact constraints. In addition, we want the trajectories to have the property that small changes in the foot position generate small changes in the joint target path. The first part of this thesis explores methods to select poses for a legged robot that maximises the workspace reachability while maintaining stability and contact constraints. It also explores methods to select configurations based on a reduced-dimensional search of the configuration space. The second part analyses time scaling strategy which tries to minimize the execution time while obeying the velocity and acceleration constraints. These two parts effectively result in smooth feasible trajectories for legged robots. Experiments on the RoboSimian robot demonstrate the effectiveness and scalability of the strategies described for walking and climbing on a rock climbing wall

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Control Implementation of Dynamic Locomotion on Compliant, Underactuated, Force-Controlled Legged Robots with Non-Anthropomorphic Design

The control of locomotion on legged robots traditionally involves a robot that takes a standard legged form, such as the anthropomorphic humanoid, the dog-like quadruped, or the bird-like biped. Additionally, these systems will often be actuated with position-controlled servos or series-elastic actuators that are connected through rigid links. This work investigates the control implementation of dynamic, force-controlled locomotion on a family of legged systems that significantly deviate from these classic paradigms by incorporating modern, state-of-the-art proprioceptive actuators on uniquely configured compliant legs that do not closely resemble those found in nature. The results of this work can be used to better inform how to implement controllers on legged systems without stiff, position-controlled actuators, and also provide insight on how intelligently designed mechanical features can potentially simplify the control of complex, nonlinear dynamical systems like legged robots. To this end, this work presents the approach to control for a family of non-anthropomorphic bipedal robotic systems which are developed both in simulation and with physical hardware. The first is the Non-Anthropomorphic Biped, Version 1 (NABi-1) that features position-controlled joints along with a compliant foot element on a minimally actuated leg, and is controlled using simple open-loop trajectories based on the Zero Moment Point. The second system is the second version of the non-anthropomorphic biped (NABi-2) which utilizes the proprioceptive Back-drivable Electromagnetic Actuator for Robotics (BEAR) modules for actuation and fully realizes feedback-based force controlled locomotion. These systems are used to highlight both the strengths and weaknesses of utilizing proprioceptive actuation in systems, and suggest the tradeoffs that are made when using force control for dynamic locomotion. These systems also present case studies for different approaches to system design when it comes to bipedal legged robots

Modification of Gesture-Determined-Dynamic Function with Consideration of Margins for Motion Planning of Humanoid Robots

The gesture-determined-dynamic function (GDDF) offers an effective way to handle the control problems of humanoid robots. Specifically, GDDF is utilized to constrain the movements of dual arms of humanoid robots and steer specific gestures to conduct demanding tasks under certain conditions. However, there is still a deficiency in this scheme. Through experiments, we found that the joints of the dual arms, which can be regarded as the redundant manipulators, could exceed their limits slightly at the joint angle level. The performance straightly depends on the parameters designed beforehand for the GDDF, which causes a lack of adaptability to the practical applications of this method. In this paper, a modified scheme of GDDF with consideration of margins (MGDDF) is proposed. This MGDDF scheme is based on quadratic programming (QP) framework, which is widely applied to solving the redundancy resolution problems of robot arms. Moreover, three margins are introduced in the proposed MGDDF scheme to avoid joint limits. With consideration of these margins, the joints of manipulators of the humanoid robots will not exceed their limits, and the potential damages which might be caused by exceeding limits will be completely avoided. Computer simulations conducted on MATLAB further verify the feasibility and superiority of the proposed MGDDF scheme

A Quadratic Programming Approach to Quasi-Static Whole-Body Manipulation

This paper introduces a local motion planning method for robotic systems with manipulating limbs, moving bases (legged or wheeled), and stance stability constraints arising from the presence of gravity. We formulate the problem of selecting local motions as a linearly constrained quadratic program (QP), that can be solved efficiently. The solution to this QP is a tuple of locally optimal joint velocities. By using these velocities to step towards a goal, both a path and an inverse-kinematic solution to the goal are obtained. This formulation can be used directly for real-time control, or as a local motion planner to connect waypoints. This method is particularly useful for high-degree-of-freedom mobile robotic systems, as the QP solution scales well with the number of joints. We also show how a number of practically important geometric constraints (collision avoidance, mechanism self-collision avoidance, gaze direction, etc.) can be readily incorporated into either the constraint or objective parts of the formulation. Additionally, motion of the base, a particular joint, or a particular link can be encouraged/discouraged as desired. We summarize the important kinematic variables of the formulation, including the stance Jacobian, the reach Jacobian, and a center of mass Jacobian. The method is easily extended to provide sparse solutions, where the fewest number of joints are moved, by iteration using Tibshirani’s method to accommodate an l_1 regularizer. The approach is validated and demonstrated on SURROGATE, a mobile robot with a TALON base, a 7 DOF serial-revolute torso, and two 7 DOF modular arms developed at JPL/Caltech