This paper addresses the problem of sensor selection in a sensor network for tracking a moving target. By considering network uncertainties and unpredictable movements of the target, reliable sensor selection approaches such as sigma points probability and target trajectory are proposed. An updated unscented Kalman filter is proposed to achieve effective tracking of the target through the sensor selection. A multialgorithm genetically adaptive multiobjective is utilized to have a selection strategy without knowing the number of sensors to be selected. Extensive experiments are conducted to evaluate the effectiveness of the proposed approach both in simulation and practical experimentation. The proposed algorithm is also tested in the industrial setting where providing safety is of great importance for a human worker who walks in a potentially dangerous workplace. The results confirm the effectiveness and utility of the proposed scheme.

Human-robot collaboration in industrial settings calls for implementing safety measures to ensure there is no risk to humans working in such an environment. In human-robot physical collaboration, an object or a load is handled by both human and the robot. Developing a safety framework for the robot is a requirement for preventing collisions during performing a task. In this paper, force myography (FMG) data are used to develop a control scheme for the robot such that it can work with the human worker while avoiding collisions. Force myography quantifies the activities of human muscles when applying forces to handle an object. A neural network-based approach is then used to select the most informative features of the FMG signal. The developed control scheme incorporates the FMG data and the robot dynamics to obtain a prediction about the next step of the cooperation task and to plan the robot motion accordingly. The proposed approach is evaluated experimentally in real time in a moving objects task which requires appropriate complementary actions from the robot and the human user. The results of this study show that the proposed scheme can successfully plan the robot motion based on the actions of the human user.

By applying robots while collaborating with a human in an industrial setting to provide more flexible and productive industries, safe interaction and collision detection have become an indispensable element of the collaborative robots. In such a dynamic environment, safe collaboration scenarios are needed to be designed using reliable methods to monitor collision-related signals and avoid a dangerous collision. Since human's hand is the most exposed limb to collision during cooperation with a robot, new flexible methods should be conducted to use in industries by considering hand safety. In this study, collision monitoring is developed using force myography of a worker forearm and robot dynamic parameters. A method based on deep neural network is proposed to distinguish any occurrence of a collision between a worker's hand and robot's arm during the collaboration. The proposed approach can be applied to provide a reliable interaction with no unnecessary robot stop during working by classifying unintended collision. Various experiments have been conducted to evaluate the proposed method. The results show that the proposed scheme can successfully detect a collision and classify human intention to provide safe and reliable cooperation with a robot in an industrial environment.

This paper presents a novel supervised clustering technique including different clustering algorithms which cooperate together to span the decision space in a supervised manner. It uses a variety of clustering methods for an efficient partitioning. An evolutionary algorithm is used to tune the key parameters of the cooperative scheme which minimizes an error-based objective function on the training dataset. The proposed supervised scheme is developed for diagnosing faults in the Tennessee Eastman process, which is a standard benchmark for fault detection and diagnosis. Experimental results show that the proposed technique can efficiently diagnose the process faults.