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In the present paper, a neural network (NN) is proposed for detecting the human-manipulator collisions. Because of that purpose, three types of NNs are designed and trained named as; multilayer feedforward, cascaded forward, and recurrent NNs. The NN is designed based on the joints’ dynamics of the manipulator. In addition, the NN is trained using a dataset with and without the collisions using the algorithm of Levenberg-Marquardt to detect the collisions happened with the manipulator. During the design of the NN, only the intrinsic joint position sensor of the robot is used which enable the proposed method to be applied to any robot. The three designed neural networks are compared quantitatively and qualitatively to each other. Furthermore, the proposed method was evaluated with the KUKA LWR manipulator using one joint motion. The results of the designed systems achieve high effectiveness in detecting the collisions between human and robot.

In this manuscript, a recurrent neural network is proposed for variable admittance control in human–robot cooperation tasks. The virtual damping and the virtual inertia of the designed robot’s admittance controller are adjusted online and simultaneously. A Jordan recurrent neural network is designed and trained for this purpose. The network is indirectly trained using the real-time recurrent learning algorithm and based on the velocity error between the reference velocity of the minimum jerk trajectory model and the actual velocity of the robot. The performance of the proposed variable admittance controller is presented in terms of the human required effort, the task completion time, the achieved accuracy at the target, and the oscillations during the movement. Its generalization ability is evaluated experimentally by conducting cooperative tasks along numerous straight-line segments using the KUKA LWR robot and by ten subjects. Finally, a comparison with previous developed variable admittance controllers, where only the variable damping or only the virtual inertia is adjusted, is presented.

In this paper, an approach for the evaluation of human-robot collaboration towards high performance is introduced and implemented. The human arm and the manipulator are modelled as a closed kinematic chain and the proposed task performance criterion is used based on the manipulability index of this chain. The selected task is a straight motion in which the robot end-effector is guided by the human operator via an admittance controller. The best location of the selected task is determined by the maximization of the minimal manipulability along the path. Evaluation criteria for the performance are adopted considering the ergonomics literature. In the experimental set-up with a KUKA LWR manipulator, multiple subjects repeat the specified motion to evaluate the introduced approach experimentally.

In this paper, a multilayer feedforward neural network-based approach is proposed for human–robot collision detection taking safety standards into consideration. One multi-output neural network is designed and trained using data from the coupled dynamics of the manipulator with and without external contacts to detect unwanted collisions and to identify the collided link using only the intrinsic joint position and torque sensors of the manipulator. The proposed method is applied to the collaborative robots, which will be very popular in the near future, and is implemented and evaluated in 3D space motion taking into account the effect of the gravity. KUKA LWR manipulator is an example of the collaborative robots, and it is used for doing the experiments. The experimental results prove that the developed system is considerably efficient and very fast in detecting the collisions in the safe region and identifying the collided link along the entire workspace of the three-joint motion of the manipulator. Separate/uncoupled neural networks, one for each joint, are also designed and trained using the same data, and their performance is compared with the coupled one.

SUMMARYIn this paper, a multilayer feedforward neural network (NN) is designed and trained, for human–robot collisions detection, using only the intrinsic joint position sensors of a manipulator. The topology of one NN is designed considering the coupled dynamics of the robot and trained, with and without external contacts, by Levenberg–Marquardt algorithm to detect unwanted collisions of the human operator with the manipulator and the link that is collided. The proposed approach could be applied to any industrial robot, where only the joint position signals are available. The designed NN is compared quantitatively and qualitatively with an NN, where both the intrinsic joint position and the torque sensors of the manipulator are used. The proposed method is evaluated experimentally with the KUKA LWR manipulator, which is considered as an example of the collaborative robots, using two of its joints in a planar horizontal motion. The results illustrate that the developed system is efficient and fast to detect the collisions and identify the collided link.

In this paper, a method for variable admittance control in human-robot cooperation is proposed. A multilayer feedforward neural network is designed using the Cartesian velocity of the robot and the applied force by the operator as its inputs to modify online the virtual damping of the admittance controller. The neural network is trained online using the error backpropagation algorithm based on the error between the velocity of the minimum jerk trajectory model and the measured velocity of the robot. The performance of the proposed controller and the NN generalization ability are evaluated by conducting a point-to-point cooperative motion with multiple subjects using the KUKA LWR robot.

In this paper, a multilayer neural network based approach is proposed for the human-robot collisions detection during the motions of a 2-DoF robot. One neural network is designed and trained by Levenberg-Marquardt algorithm to the coupled dynamics of the manipulator joints with and without external contacts to detect unwanted collisions of the human operator with the robot and the link that collided using only the proprietary joint position and joint torque sensors of the manipulator. The proposed method is evaluated experimentally with the KUKA LWR manipulator using two joints in planar horizontal motion and the results illustrate that the developed system is efficient and very fast in detecting the collisions as well as the collided link.