Conference Paper

An experimental evaluation of a novel minimum-jerk cartesian controller for humanoid robots.

Dept. of Robot., Brain & Cognitive Sci. (RBCS), Italian Inst. of Technol. (IIT), Genova, Italy
DOI: 10.1109/IROS.2010.5650851 Conference: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 18-22, 2010, Taipei, Taiwan
Source: IEEE Xplore

ABSTRACT In this paper we describe the design of a Cartesian Controller for a generic robot manipulator. We address some of the challenges that are typically encountered in the field of humanoid robotics. The solution we propose deals with a large number of degrees of freedom, produce smooth, human-like motion and is able to compute the trajectory on-line. In this paper we support the idea that to produce significant advancements in the field of robotics it is important to compare different approaches not only at the theoretical level but also at the implementation level. For this reason we test our software on the iCub platform and compare its performance against other available solutions.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new technique to control highly redundant mechanical systems, such as humanoid robots. We take inspiration from two approaches. Prioritized control is a widespread multi-task technique in robotics and animation: tasks have strict priorities and they are satisfied only as long as they do not conflict with any higher-priority task. Optimal control instead formulates an optimization problem whose solution is either a feedback control policy or a feedforward trajectory of control inputs. We introduce strict priorities in multi-task optimal control problems, as an alternative to weighting task errors proportionally to their importance. This ensures the respect of the specified priorities, while avoiding numerical conditioning issues. We compared our approach with both prioritized control and optimal control with tests on a simulated robot with 11 degrees of freedom.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a new framework for prioritized multi-task motion/force control of fully-actuated robots. This work is established on a careful review and comparison of the state of the art. Some control frameworks are not optimal, that is they do not find the optimal solution for the secondary tasks. Other frameworks are optimal, but they tackle the control problem at kinematic level, hence they neglect the robot dynamics and they do not allow for force control. Still other frameworks are optimal and consider force control, but they are computationally less efficient than ours. Our final claim is that, for fully-actuated robots, computing the operational-space inverse dynamics is equivalent to computing the inverse kinematics (at acceleration level) and then the joint-space inverse dynamics. Thanks to this fact, our control framework can efficiently compute the optimal solution by decoupling kinematics and dynamics of the robot. We take into account: motion and force control, soft and rigid contacts, free and constrained robots. Tests in simulation validate our control framework, comparing it with other state-of-the-art equivalent frameworks and showing remarkable improvements in optimality and efficiency.
    Robotics and Autonomous Systems 10/2014; · 1.11 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper deals with the problem of 3D stereo estimation and eye-hand calibration in humanoid robots. We first show how to implement a complete 3D stereo vision pipeline, enabling online and real-time eye calibration. We then introduce a new formulation for the problem of eye-hand coordination. We developed a fully automated procedure that does not require human supervision. The end-effector of the humanoid robot is automatically detected in the stereo images, providing large amounts of training data for learning the vision-to-kinematics mapping. We report exhaustive experiments using different machine learning techniques; we show that a mixture of linear transformations can achieve the highest accuracy in the shortest amount of time, while guaranteeing real-time performance. We demonstrate the application of the proposed system in two typical robotic scenarios: (1) object grasping and tool use; (2) 3D scene reconstruction. The platform of choice is the iCub humanoid robot.
    IEE-RAS International Conference on Humanoid Robots; 11/2014

Full-text (3 Sources)

Available from
Jul 3, 2014