Numerical issues in integrating holonomic kinematic inversion algorithms for redundant manipulators

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A General User-Oriented Framework for Holonomic Redundancy Resolution in Robotic Manipulators Using Task Augmentation

Redundant robotic manipulators under kinematic control may exhibit unpredictable behaviors at the joint level. When the end effector describes a closed trajectory, the joint angles may not return to their initial values. Likewise, final configuration in the joint space may depend on the trajectory that is followed by the end effector. In this paper, a complete parameterization of holonomic redundancy resolution techniques that avoid these problems is proposed. The flexibility of the proposed approach is discussed. In particular, it is shown that the selection of the redundancy resolution criterion is totally decoupled from the implementation of a closed-loop inverse kinematics (CLIK) algorithm. Any user-defined redundancy resolution criterion can, thus, be enforced. Potentialities of this new methodology are experimentally verified on an industrial robot in a case study where functional redundancy occurs and is applied in simulation on a 7-degree-of-freedom (7-DOF) anthropomorphic manipulator

Achieving humanlike motion: resolving redundancy for anthropomorphic industrial manipulators

Research interest in human-robot interaction (HRI) has been increasing in recent decades and has now reached an appealing degree of maturity, which encourages first steps toward a technology transfer from research centers to robot manufacturers. Making robots able to cooperate with humans in a safe and intuitive manner is no longer a futuristic vision but a concrete opportunity that is just around the corner. On the other hand, measuring the quality of this interaction is a novel field in robotics research and will be extremely useful for future assessments of all efforts in the direction of more effective HRI. First results exist, ranging from a simple approach, like the so-called "uncanny valley" proposed in [1], to a more scientific interpretation supported by physiological measurements [2]

A redundancy resolution method for an anthropomorphic dual-arm manipulator based on a musculoskeletal criterion

In order to make humans feeling comfortable when working with robots, it is necessary for robots to be as much as possible “human-like” in both their appearance and movements. In redundant manipulators, it is possible to use the additional degrees of freedom in order to make the robot motion more human-like, thus increasing the quality of the human-robot interaction. In this work, a redundancy resolution method to address this issue is presented. Such a method considers the human musculoskeletal system and a biomechanical model of the human upper limbs in order to define a strategy to solve the redundancy for a dual-arm anthropomorphic manipulator as a human would do, then making the robot able to both perform the prescribed task and to assume human-like postures

Constraint-based Model Predictive Control for holonomic mobile manipulators

In this paper, a controller based on constrained optimization for tracking problems in mobile manipulation is presented. A Model Predictive Control problem is set and solved online, allowing to deal with dynamic scenarios and unforeseen events. Besides acceleration, velocity and position constraints, collision avoidance constraints for the mobile base and the arm and Field-of-View constraints have been enforced and extended over the prediction horizon. Navigation performance has been improved by including an additional goal, derived from the classical vortex field approach, to the MPC problem. An experimental validation on a KUKA youBot mobile manipulator has been carried out, showing the online applicability of the presented approach

Accurate sensorless lead-through programming for lightweight robots in structured environments

Nowadays, programming an industrial manipulator is a complex and time-consuming activity, and this prevents industrial robots from being massively used in companies characterized by high production flexibility and rapidly changing products. The introduction of sensor-based lead-through programming approaches (where the operator manually guides the robot to teach new positions), instead, allows to increase the speed and reduce the complexity of the programming phase, yielding an effective solution to enhance flexibility. Nevertheless, some drawbacks arise, like for instance lack of accuracy, need to ensure the human operator safety, and need for force/torque sensors (the standard devices adopted for lead-through programming) that are expensive, fragile and difficult to integrate in the robot controller. This paper presents a novel approach to lead-through robot programming. The proposed strategy does not rely on dedicated hardware since torques due to operator's forces are estimated using a model-based observer fed with joint position, joint velocity and motor current measures. On the basis of this information, the external forces applied to the manipulator are reconstructed. A voting system identifies the largest Cartesian component of the force/torque applied to the manipulator in order to obtain accurate lead-through programming via admittance control. Finally an optimization stage is introduced in order to track the joint position displacements computed by the admittance filter as much as possible, while enforcing obstacle avoidance constraints, actuation bounds and Tool Center Point (TCP) operational space velocity limits. The proposed approach has been implemented and experimentally tested on an ABB dual-arm concept robot FRIDA