Nowadays, population growth and urban development lead to having an efficient waste management system (WMS) based on recent advances and trends. Alongside all functions and procedures in these systems, the waste collection plays a significant role. This study proposes a two-echelon WMS to minimize operational costs and environmental impact by utilizing the industry 4.0 concept. Both models utilize modern traceability Internet of Thing-based devices to compare real-time information of waste level in bins and separation centers with the threshold waste level (TWL) parameter. The first model optimizes the operational cost and CO 2 emission of collecting waste from bins to the separation center by considering the time windows. A capacitated vehicle routing problem is designed as a later model-based to minimize the cost of waste transferring to recycling centers. In addition, to find the optimal solution, recent meta-heuristic algorithms are employed, and several novel heuristics based on the problem's specifications are developed. Furthermore, the developed heuristics methods are utilized to generate the initial feasible solutions in meta-heuristics and compared with random ones. The performance of the proposed algorithms is probed, and Best Worst Method (BWM) is applied to rank the algorithms based on relative percentage deviation, relative deviation index and hitting time.

Upper limb exoskeletons (ULEs) are robotic devices that can mobilize a severely disabled individual's arm and help the user perform some activities of daily living independently. Despite advancement in the mechanical design of ULEs, a versatile control interface that enables full and continuous control of a ULE with multiple degrees of freedom for a user with disability in both arms and legs (tetraplegia) still requires further research and development. In this study, ten individuals with tetraplegia used a tongue-based interface to fully control a five degrees of freedom ULE for a drinking and a snacking task. This required moving the ULE hand from a wheelchair armrest position to grasp an object (bottle or strawberry) placed on a table in front of the participant, moving the object towards them until it touched a face shield, and placing the object on the table. All participants successfully controlled the exoskeleton and completed the tasks. The drinking task lasted 149.6 s on the first day and 122.9 s (median) on the second day of using the exoskeleton. The participants performed the snacking task only on the first day of ULE use and achieved a median task time of 167.0 s. The study showed that the tongue interface could provide effective, efficient, and safe control of the exoskeleton.

Upper-limb exoskeletons (ULEs) have the potential to assist severely paralyzed individuals. Many rehabilitative upper-limb exoskeletons have been developed in the recent years, where the prominent issue of bulkiness and actuator placement has been effectively resolved by the cable-transmission systems, allowing the actuators to be mounted away from the joints. This results in increasing the compactness and wearability of the exoskeleton. Thus, this article summarizes twenty-five cable-driven upper-limb exoskeletons designed for rehabilitating disabled individuals. This paper also presents a comparison based on usability of ULEs by end users considering the design characteristics and user-based experiments for real-time applications of the arm exoskeletons.

Tongue-computer interfaces have shown the potential to control assistive devices developed for individuals with severe disabilities. However, current efficient tongue-computer interfaces require invasive methods for attaching the sensor activation units to the tongue, such as piercing. In this study, we propose a noninvasive tongue-computer interface to avoid the requirement of invasive activation unit attachment methods. We developed the noninvasive tongue-computer interface by integrating an activation unit on a frame, and mounting the frame on an inductive tongue-computer interface (ITCI). Thus, the users are able to activate the inductive sensors on the interface by positioning the activation unit with their tongue. They also do not need to remount the activation unit before each use. We performed pointing tests for controlling a computer cursor and number typing tests with two able-bodied participants, where one of them was experienced with using invasive tongue-computer interfaces and other one had no experience. We measured throughput and movement error for pointing tasks, and speed and accuracy for number typing tasks for the evaluation of the feasibility and performance of the developed noninvasive system. Results show that the inexperienced participant achieved similar results with the developed noninvasive tongue-computer interface compared to the current invasive version of the ITCI, while the experienced participant performed better with the invasive tongue-computer interface.

Complete tetraplegia causes paralysis from the neck down, resulting in a need for constant care and lack of doing activities of daily living by yourself. Exoskeletons have enabled some rehabilitation and movement of paralyzed limbs but are often prohibitively large and heavy. This project aimed to redesign the wrist of the tongue controlled EXOTIC exoskeleton, making it compact and light. The new design used tendon-based technology, removing the motor from the forearm as well as employing 3D print. A proof-of-concept for the new design was obtained through a case study with three subjects.

People with complete tetraplegia who are paralyzed from the neck down require alternative computer and robot interfaces based on the remaining functionalities. The inductive tongue-computer interface (ITCI) consists of two small touchpads that are manipulated by the tongue and can provide individuals with tetraplegia the possibility to control computers, robots, and other electronic devices. The tongue gesture can be used to expand the vocabulary of the ITCI and provide more control commands. In this study for the first time, a tongue gesture recognition method was introduced that can identify a set of six gestures with 94.3% accuracy and a set of 23 gestures with 72.3% accuracy. Furthermore, a robot interface was developed using the tongue gestures and it was compared with a previously tested method based on soft buttons on the ITCI. Four able-bodied participants performed a pouring water task with the two control methods when visual feedback from the interface was presented on a screen and then without the visual feedback. Task completion time for the gesture-based control and the button-based control did not differ. However, gesture-based control provides more control commands that can be used for interfacing other systems.

In this paper, a method based on vector fields for the navigation of autonomous cars is developed. Vector fields-used to generate the desired heading angle of a vehicle toward a specified road lane—attract the car to the desired path and prevent the car from colliding with obstacles. Also, a control law is developed to define the velocity direction and the desired steering angle based on the angle between the car and the vector field. The efficacy of the proposed approach is investigated through several simulations and lab experimental tests.

In this paper we lay the foundation of the first heterogeneous multi-robot system of the Multiple Aerial-Ground Manipulator System (MAGMaS) type. A MAGMaS consists of a ground manipulator and a team of aerial robots equipped with a simple gripper manipulator the same object. The idea is to benefit from the advantages of both kinds of platforms, i.e., physical strength versus large workspace. The dynamic model of such robotic systems is derived, and its characteristic structure exhibited. Based on the dynamical structure of the system a nonlinear control scheme, augmented with a disturbance observer is proposed to perform trajectory tracking tasks in presence of model inaccuracies and external disturbances. The system redundancy is exploited by solving an optimal force/torque allocation problem that takes into account the heterogeneous system constraints and maximizes the force manipulability ellipsoid. Simulation results validated the proposed control scheme for this novel heterogeneous robotic system. We finally present a prototypical mechanical design and preliminary experimental evaluation of a MAGMaS composed by a kuka LWR4 and quadrotor based aerial robot.

The human hand represents a complex fascinating system with highly sensitive sensory capabilities and dexterous grasping and manipulation functionalities. As a consequence, estimating the hand pose and at the same time having the capability to provide haptic feedback in a wearable way may benefit areas such as rehabilitation, human-robot interaction, gaming, and many more. Existing solutions allow us to accurately measure the hand configuration and provide effective force feedback to the user. However, they have limited wearability/portability. In this paper, we present the wearable sensing/actuation system glove for enhanced sensing and touching (GESTO). It is based on inertial and magnetic sensors for hand tracking, coupled with cutaneous devices for the force feedback rendering. Unlike vision-based tracking systems, the sensing glove does not suffer from occlusion problems and lighting conditions. We properly designed the cutaneous devices in order to reduce possible interferences with the magnetic sensors and performed an experimental validation on ten healthy subjects. In order to measure the estimation accuracy of GESTO, we used a high-precision optical tracker. A comparison between using the glove with and without the haptic devices shows that the presence of them does not induce a statistically significant increase in the estimation error. Experimental results revealed the effectiveness of the proposed approach. The accuracy of our system, 3.32° mean estimation error in the worst case, is comparable with the human ability of discriminating finger joint angle.

In this paper, we propose a bilateral tele-operation scheme for cooperative aerial manipulation in which a human operator drives a team of Vertical Take-Off and Landing (VTOL) aerial vehicles, that grasped an object beforehand, and receives a force feedback depending on the states of the system. For application scenarios in which dexterous manipulation by each robot is not necessary, we propose using a rigid tool attached to the vehicle through a passive spherical joint, equipped with a simple adhesive mechanism at the tool-tip that can stick to the grasped object. Having more than two robots, we use the extra degrees of freedom to find the optimal force allocation in term of minimum power and forces smoothness. The human operator commands a desired trajectory for the robot team through a haptic interface to a pose controller, and the output of the pose controller along with system constraints, e.g., VTOL limited forces and contact maintenance, defines the feasible set of forces. Then, an on-line optimization allocates forces by minimizing a cost function of forces and their variation. Finally, propeller thrusts are computed by a dedicated attitude and thrust controller in a decentralized fashion. Human/Hardware in the loop simulation study shows efficiency of the proposed scheme, and the importance of haptic feedback to achieve a better performance.

In the last years, wearable haptic technologies became very promising since they provide the users with tactile force feedback via small and wearable interfaces. However, they have no position sensing thus additional technologies like motion capture systems or expensive gloves are required. Recently, low cost hand tracking systems based on RGB-D cameras have been developed, however they suffer from lighting conditions and occlusions. In this paper we present a sensing glove based on inertial and magnetic sensors for hand tracking which can be combined with cutaneous devices for the rendering of the force feedback, thus producing a wearable sensing/actuation system. The sensing glove does not suffer from occlusion problems, it is wearable and cost effective however, since the employed sensors use the magnetometer to compensate the drift, they are sensitive to variations in the magnetic field. This property makes it challenging to interface the sensing glove with wearable devices since their motors generate variations in the magnetic field. Preliminary experiments showed the effectiveness of using inertial and magnetic sensors for hand tracking. A comparison between using the glove with and without the haptic devices was presented in order to compare the tracking performance when cutaneous devices are used.

In this paper a novel teleoperation framework for aerial robots that physically interact with the environment is presented. This framework allows to teleoperate the robot both in contact-free flight and in physical contact with the environment in order, e.g., to apply desired forces on objects of the environment. The framework is build upon an impedance-like indirect interaction force controller that allows to use standard underactuated aerial robots as force effectors. Haptic feedback from the master side enables the user to feel the contact forces exerted by the robot. An automatic potential field-based slowing-down policy is used by the robot to ensure a smooth transition between the contact-free motion phase and the force interaction phase. The effectiveness of the approach has been shown in extensive human-in-the-loop simulations including remote pressing of buttons on a surface and pushing a cart until it touches a wall.