Abdelrahem Atawnih

Bio: Abdelrahem Atawnih is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Robot control & Control theory. The author has an hindex of 3, co-authored 6 publications receiving 73 citations.

A passivity based control signal guaranteeing joint limit avoidance in redundant robots

Abstract: In this work we propose a torque control signal that guarantees joint limit avoidance of a redundant arm. Its design is based on the prescribed performance control methodology that enables guarantees on the satisfaction of inequality constraints regarding the system output. It is proved that the proposed signal preserves the passivity of closed loop robot dynamics with respect to joint velocities thus allowing its use with any passive control law designed to attract the robot towards a task goal. Experimental results with a KUKA LWR4+ for a task involving the tip's position motion along a linear path confirm theoretical findings and demonstrate the proposed signal's performance in two scenarios of a feasible and an unfeasible path.

Operational Space Prescribed Tracking Performance and Compliance in Flexible Joint Robots

Abstract: In this work, an admittance control scheme is proposed utilizing a highly robust prescribed performance position tracking controller for flexible joint robots which is designed at the operational space. The proposed control scheme achieves the desired impedance to the external contact force as well as superior position tracking in free motion without any robot model knowledge, as opposed to the torque based impedance controllers. Comparative simulation results on a three degrees-of-freedom (3DOF) flexible joint manipulator, illustrate the efficiency of the approach.

Smooth Reaching and Human-Like Compliance in Physical Interactions for Redundant Arms

Abstract: This work collectively addresses human-like smoothness and compliance to external contact force in reaching tasks of redundant robotic arms, enhancing human safety potential and facilitating physical human-robot interaction. A model based prescribed performance control algorithm is proposed, producing smooth, repeatable reaching movements for the arm and a compliant behavior to an external contact by shaping the reaching target superimposing the position output from a human-like impedance model. Simulation results for a 5dof human-arm like robot demonstrate the performance of the proposed controller.

Prescribed Performance Tracking of a Variable Stiffness Actuated Robot

Abstract: This paper is concerned with the design of a state feedback control scheme for variable stiffness actuated (VSA) robots, which guarantees prescribed performance of the tracking errors despite the low range of mechanical stiffness. The controller does not assume knowledge of the actual system dynamics nor does it utilize approximating structures (e.g., neural networks and fuzzy systems) to acquire such knowledge, leading to a low complexity design. Simulation studies, incorporating a model validated on data from an actual variable stiffness actuator (VSA) at a multi-degrees-of-freedom robot, are performed. Comparison with a gain scheduling solution reveals the superiority of the proposed scheme with respect to performance and robustness.

Tricriteria Optimization-Coordination Motion of Dual-Redundant-Robot Manipulators for Complex Path Planning

Abstract: To remedy the discontinuity phenomenon existing in the infinity-norm velocity minimization (INVM) scheme, prevent the occurrence of high joint velocity, and decrease the joint-angle drift of redundant robot manipulators, a new type of tricriteria optimization-coordination-motion (TCOCM) scheme is proposed and investigated for dual-redundant-robot manipulators to track complex end-effector paths. Besides, the proposed scheme considers joint physical constraints (i.e., joint-angle limits and joint-velocity limits) and guarantees the joint velocity to approach zero at the end of tasks. Such a new-type TCOCM scheme combines the minimum velocity norm (MVN), repetitive motion planning (RMP), and INVM solutions via two weighting factor, and thus is termed MVN-RMP-INVM-TCOCM scheme. The proposed scheme consists of two subschemes, i.e., subscheme for the left robot manipulator and subscheme for the right robot manipulator. To control the dual arms simultaneously and collaboratively, two subschemes are reformulated as two general quadratic programming (QP) problems and further unified into one QP formulation. The unified QP problem is then solved by a simplified linear-variational-inequalities-based primal–dual neural network solver. Computer simulation results based on dual PUMA560 robot manipulators are illustrated to substantiate the advantage, efficacy, and applicability of the proposed MVN-RMP-INVM-TCOCM scheme to resolve the redundancy of dual-robot manipulators.

Recurrent-Neural-Network-Based Velocity-Level Redundancy Resolution for Manipulators Subject to a Joint Acceleration Limit

Abstract: For the safe operation of redundant manipulators, physical constraints such as the joint angle, joint velocity, and joint acceleration limits should be taken into account when designing redundancy resolution schemes. Velocity-level redundancy resolution schemes are widely adopted in the kinematic control of redundant manipulators due to the existence of the well-tuned inner loop regarding the joint velocity control. However, it is difficult to deal with joint acceleration limits for velocity-level redundancy resolution methods. In this paper, a recurrent-neural-network-based velocity-level redundancy resolution method is proposed to deal with the problem, and theoretical results are given to guarantee its performance. By the proposed method, the end-effector position error is asymptotically convergent to zero, and all the joint limits are not violated. The effectiveness and superiority of the proposed scheme are validated via simulation results.

An Incremental Learning Framework for Human-Like Redundancy Optimization of Anthropomorphic Manipulators

Abstract: Recently, the human-like behavior on the anthropomorphic robot manipulator is increasingly accomplished by the kinematic model establishing the relationship of an anthropomorphic manipulator and human arm motions. Notably, the growth and broad availability of advanced data science techniques facilitate the imitation learning process in anthropomorphic robotics. However, the enormous dataset causes the labeling and prediction burden. In this article, the swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy. For the sake of efficient computing, a novel incremental learning framework that combines an incremental learning approach with a deep convolutional neural network is proposed for fast and efficient learning. The algorithm exploits a novel approach to detect changes from human motion data streaming and then evolve its hierarchical representation of features. The incremental learning process can fine-tune the deep network only when model drifts detection mechanisms are triggered. Finally, we experimentally demonstrated this neural network's learning procedure and translated the trained human-like model to manage the redundancy optimization control of an anthropomorphic robot manipulator (LWR4+, KUKA, Germany). This approach can hold the anthropomorphic kinematic structure-based redundant robots. The experimental results showed that our architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.

Predictive Inverse Kinematics for Redundant Manipulators With Task Scaling and Kinematic Constraints

Abstract: The paper presents a fast online predictive method to solve the task-priority differential inverse kinematics of redundant manipulators under kinematic constraints. It implements a task-scaling technique to preserve the desired geometrical task, when the trajectory is infeasible for the robot capabilities. Simulation results demonstrate the effectiveness of the methodology.