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.

This paper addresses the problem of providing feedback to the operator about the manipulator's performance during human-robot physical interaction. A method is proposed that implements virtual constraints in Cartesian admittance control in order to prevent the operator from guiding the manipulator to low-performance configurations. The constraints are forces expressed in the Cartesian frame, which restrict the translation of the end-effector when the operator guides the robot below a certain performance threshold. These forces are calculated online by numerically approximating the gradient of the performance index with respect to the Cartesian frame attached to the end-effector. An experimental evaluation is conducted involving human-robot interaction with a 7-DOF LWR serial manipulator under Cartesian admittance control, using the kinematic manipulability index of the manipulator as the performance measure for singularity avoidance.

In this paper, the co-activation level of the arm muscles is used as an indication of the end-point stiffness for improving human-robot cooperation. A variable admittance controller is proposed to adjust the virtual damping in real time by measuring the operator's muscle activation by means of surface EMG. An experimental user study is conducted that simulates both high accuracy and fast transition movements, involving human-robot interaction with a 7-DOF LWR serial manipulator. The proposed method is compared to constant admittance and is evaluated in terms of movement accuracy, execution time, and the operator's energy consumption. The results demonstrate that there is a significant reduction of the operator's effort and an improvement of the cooperative motion accuracy.

The emerging field of physical human-robot interaction raises the need to distinguish collisions over intended contacts in order to guarantee safe and seamless interaction. In this paper, a novel contact distinction method is proposed that monitors the externally applied forces/torques and is able to distinguish unexpected collisions from intended contacts during cooperative tasks. The method is based on a frequency domain analysis of the externally applied forces using the Fast Fourier Transform. Moreover, a tuning method is proposed to adjust the thresholds for the detection, according to the desired dynamic behavior of the admittance controller. The collision distinction method is evaluated experimentally in a human-robot cooperation task with multiple subjects using a 7DOF LWR manipulator.

In this paper, a variable admittance controller based on reinforcement learning is proposed for human-robot co-manipulation tasks. Setting as the goal of the reinforcement learning algorithm the minimisation of the jerk throughout a point-to-point movement, the proposed controller can learn the appropriate damping for effective cooperation without any prior knowledge of the target position or other task characteristics. The performance of the proposed variable admittance controller is investigated on a co-manipulation task with a number of subjects using a KUKA LWR robot, demonstrating considerable reduction both in the effort required by the operator and in the completion time of the task.

In this paper a dictionary of primitives is proposed that define critical features for the representation of a cloth’s configuration so as to unfold it using a general manipulator. The primitives are defined, detected and classified according to geometrical criteria. Experimental results demonstrate the effectiveness of the detection and classification approach. Finally, applications are presented where the dictionary of the proposed primitives can be used, such as the definition of different types of folds of an unfolded fabric that can facilitate the unfolding task. Conclusion and suggestions for future work completes this paper.

This paper presents a method for variable admittance control in human-robot cooperation tasks, that combines a human-like decision making process and an adaptation algorithm. A Fuzzy Inference System is designed that relies on the measured velocity and the force applied by the operator to modify on-line the damping of the robot admittance, based on expert knowledge for intuitive cooperation. A Fuzzy Model Reference Learning Controller is used to adapt the Fuzzy Inference System according to the minimum jerk trajectory model. To evaluate the performance of the proposed controller a point-to-point cooperation task is conducted with multiple subjects using a KUKA LWR robot.

In this paper, we propose a method that allows a human to perform complex manipulation tasks jointly with a robotic partner. To that end, the robot has a library of assistances that it can provide for helping the human partner during a priori unknown collaborative tasks. According to the haptic cues naturally transmitted by the human partner, the robot selects on-line the suitable assistance for the current intended collaborative motion. Based on the naive bayes classifier and the Matthew Correlation Coefficient, the parameters of the decision-making are automatically tuned. An experiment on a real arm manipulator is provided to validate the proposed approach.

This article considers the following manufacturing/logistics problem: an autonomous vehicle (AV) is requested to serve a set of pickup and delivery (P/D) stations in the shop floor of a modern factory providing P/D tasks while moving safely (i.e., avoiding any collision with obstacles) in its environment. A two-sided time window associated with each P/D station brackets the service time to within a specified interval. A tour for AV is considered legal if it (a) is collision free, (b) passes through each P/D station exactly once, and (c) satisfies the service time restrictions imposed by the P/D stations. A legal tour always starts and ends at a depot. The problem is thereby dual NP-hard because it combines the characteristics of path planning and those of vehicle routing and scheduling problems. The objective is to determine the shortest possible legal tour for AV. A new method is introduced to solve the problem accomplished in two successive phases: first, AV's environment is mapped into a 2D B-spline surface embedded in 3D Euclidean space using a robust geometric model. Then, the generated surface is searched using a genetic algorithm for an optimum legal tour that satisfies the requirements of the vehicle's mission. The performance of the proposed method is investigated and discussed through characteristic simulated experiments.

Abstract This paper presents a new approach for the transformation and normal vector calculation algorithms of parametrically defined surfaces via dual vectors and line transformations. The surface is defined via dual points, the transformation is performed by rotations and translations based on screw theory while normal vector calculation is utilized for shading based on Phong's illumination model. The main benefit of this approach lies into the compactness of the surface's representation since geometrical characteristics, such as tangent vectors, that are necessary for shading algorithms, are included within its definition. An extensive comparison is performed between the proposed approach and the traditional homogeneous model, presenting the merits of our approach. Analytical and experimental determination of the computational cost via computer implementation of 3D surface transformation and shading is presented. Point‐based methods for the representation, transformation and shading of parametrically defined surfaces are compared to the introduced line‐based methods (dual quaternions and dual orthogonal matrices). It is shown that the simplified rendering procedure of 3D objects, is considerably faster using screw theory over the traditional point‐based structures.

Three methods for the formulation of the kinematic equations of robots with rigid links are presented in this paper. The first and most common method in the robotics community is based on 4x4 homogeneous matrix transformation, the second one is based on Lie algebra, and the third one on screw theory expressed via dual quaternions algebra. These three methods are compared in this paper for their use in the kinematic analysis of robot arms. The basic theory and the transformation operators, upon which every method is based, are referenced. Three analytic algorithms are presented for the solution of the direct kinematic problem corresponding to each method, and the geometric significance of the transformation operators and parameters is explained. Finally, a comparative study on the computation and storage requirements for the three methods is worked out.