The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.

With the development of intelligent manufacturing, more and more nonstandard parts are used in high-precision assembly. The robotic assembly method based on attractive region in environment (ARIE) has been proven to have good performance in the high-precision assembly under the limitation of robot system accuracy or sensing accuracy. However, for the assembly of nonstandard parts, especially nonconvex parts, the existing ARIE-based strategy lacks a targeted design. In the assembly process, the nonconvex structure may cause blocking problems, which will lead to assembly failure when using the strategy. In order to solve this problem, this paper proposes a new assembly method by using the geometric features of constraint region based on the concept of ARIE. Specifically, first, when using the ARIE-based classic strategy, the reasons for the possible blocking problem in the assembly of a class of nonconvex axisymmetric parts are analyzed in detail. Second, a multi-step sliding strategy is proposed based on the theory of ARIE to solve the possible blocking problem in the assembly process. Third, impedance control is used to enable the peg to achieve the desired compliant motion in the proposed strategy. The improvement in the success rate of the proposed method is verified by the comparison experiment of small clearance peg-in-hole assembly, where the structure of the peg is nonconvex and axisymmetric.

Cable-driven parallel robots (CDPRs) are still gaining attention thanks to their interesting characteristics compared to serial or classic parallel manipulators. However, the limited range of rotation of their end-effectors reduces their application fields to predominantly translational movements. In this context, the issue of extending the rotational workspace of a CDPR while maintaining a compact robot structure is addressed in this paper. This work is motivated by the need to find the optimal CDPR for upper limb rehabilitation allowing to assist the patient’s hand along a set of prescribed tasks. Firstly, a reconfigurable robot, where the motors’ locations are movable, is proposed in order to help reaching all the prescribed poses. Although this solution presents promising results compared to classical CDPRs, it involves a sizable robot structure inadequate to rehabilitation application. To improve the obtained solution, another approach is proposed, based on combining the large translational workspace of CDPRs and the large rotational workspace of serial manipulators. The optimal structure of a hybrid robot will be considered for the prototype design.

This paper presents a robust adaptive controller based on the backstepping technique using an extended state observer (ESO), implemented on a 2PUS+RR parallel robot, to minimize the trajectory tracking error. The proposed backstepping-ESO controller scheme is designed to compensate for the robot’s structured (parametric) and unstructured (nonlinear friction, external disturbances, and dynamics) uncertainties. The overall stability of the controller is guaranteed by the Lyapunov theory. Cosimulation in MATLAB-Simulink and ADAMS View is presented to validate the results of the ESO and backstepping controller implemented in the virtual and physical prototype. For the virtual prototype, it was determined that the system is stable in 2 s and presents a maximum absolute error of 3.5 × $10^{-6}$ m for the actuator position and 2.8 × $10^{-5}$ rad for mobile platform orientation. Regarding the physical robot, a maximum absolute error of 5 × $10^{-4}$ m for the actuator position and 0.0575 rad for the orientation of the robot mobile platform values do not represent a problem for ankle rehabilitation movements. Experimental results were also presented and compared with ankle motion to demonstrate that the applied control system meets the motion requirements of the ankle rehabilitator.

In this research, a force tracking smoothing adaptive admittance controller is proposed that grants precise contact forces (performance necessary for many critical interaction tasks such as polishing) for unknown interaction environments (e.g., leather or thin and soft materials). First, an online indirect adaptive update strategy is proposed for generating the reference trajectory required by the desired tracking force, considering the uncertainty of the interaction. The sensor noise amplitude is environment dynamics and the necessity condition for traditional admittance controller to achieve ideal steady-state force tracking. Then, a pre-PD controller is introduced to increase the parameter convergence rate while ensuring the steady-state force tracking accuracy and enhancing the robustness of the system. The robustness boundary is also analyzed to provide assurance for the stability of the system. Finally, we verify the effectiveness of the proposed method in simulations. Simultaneously, an experiment is conducted on the AUBO-i5 serial collaborative robot, and the experimental results proved the excellent comprehensive performance of the control framework.

Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are convolutional neural network (CNN)-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: (a) their high dimension and (b) lack of generalization, leading to low efficiency and poor performance in real robotic applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time. We verify our method in four challenging real-world datasets involving significant illumination changes, and our method is shown to be superior to the state-of-the-art. The code of our work is publicly available at https://github.com/MedlarTea/CAE-VPR.

“Picking out the impurities” is a typical scenario in production line which is both time consuming and laborious. In this article, we propose a target-oriented robotic push-grasping system which is able to actively discover and pick the impurities in dense environments with the synergies between pushing and grasping actions. First, we propose an attention module, which includes target saliency detection and density-based occluded-region inference. Without the necessity of expensive labeling of semantic segmentation, our attention module can quickly locate the targets in the view or predict the candidate regions where the targets are most likely to be occluded. Second, we propose a push–grasp synergy framework to sequentially select proper actions in different situations until all targets are picked out. Moreover, we introduce an active pushing mechanism based on a novel metric, namely Target-Centric Dispersion Degree (TCDD) for better grasping. TCDD describes whether the targets are isolated from the surrounding objects. With this metric, the robot becomes more focused on the actions around the targets and push irrelevant objects away. Experimental results on both simulated environment and real-world environment show that our proposed system outperforms several baseline approaches,which also has the capability to be generalized to new scenarios.

To improve the accessibility of robotics, we propose a design and fabrication strategy to build low-cost electromechanical systems for robotic devices. Our method, based on origami-inspired cut-and-fold and E-textiles techniques, aims at minimizing the resources for robot creation. Specifically, we explore techniques to create robots with the resources restricted to single-layer sheets (e.g., polyester film) and conductive sewing threads. To demonstrate our strategy’s feasibility, these techniques are successfully integrated into an electromechanical oscillator (about 0.40 USD), which can generate electrical oscillation under constant-current power and potentially be used as a simple robot controller in lieu of additional external electronics.

There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.

We propose a computational design tool to enable casual end-users to easily design, fabricate, and assemble flat-pack furniture with guaranteed manufacturability. Using our system, users select parameterized components from a library and constrain their dimensions. Then they abstractly specify connections among components to define the furniture. Once fabrication specifications (e.g., materials) designated, the mechanical implementation of the furniture is automatically handled by leveraging encoded domain expertise. Afterwards, the system outputs three-dimensional models for visualization and mechanical drawings for fabrication. We demonstrate the validity of our approach by designing, fabricating, and assembling a variety of flat-pack (scaled) furniture on demand.

A novel parabolic dish solar concentrator based on the improved 3-RPS parallel manipulator to drive the reflective mirror facet is proposed and designed, which can not only automatically adjust the position and orientation of the reflective mirror facet but also have the advantages of independent drive, high stiffness and no cumulative error. Then, using the coordinate transformation matrixes of the novel parabolic dish solar tracking platform, the kinematics models of the 3-RPS parallel manipulators associated with the solar altitude and azimuth angles are established. The altitude and azimuth angles of the solar movement at the installation location are calculated according to the calculation formula of solar position. To solve the problem of too many telescopic rods of the 3-RPS parallel manipulator, a genetic algorithm is used to optimize the height of the concentrator’s center of mass. Then the ideal trajectory and attitude of each telescopic rod of the 3-RPS parallel manipulators at different times of the day can be obtained with the inverse kinematics. The particle swarm optimization (PSO)-proportional-integral-derivative (PID) controller, which uses PSO algorithm to tune PID parameters, is proposed for solar trajectory tracking of the novel parabolic dish solar concentrator. The visual simulation model of the parabolic dish system is established in Simscape Multibody, and the trajectory tracking control experiment is carried out. The experimental results show that the trajectory tracking error of the novel dish solar tracking platform can be within 2.6 mm by using the PSO-PID controller.

In recent years, autonomous control based on contact inspections in unknown environments is a new hot and difficult point in robotics research. This paper presents a new control law for unmanned aerial manipulator (UAM) to perform contact inspection tasks on vertical surfaces. The selected circular image feature decouples the position and attitude of UAM, so an image-based impedance control is proposed to control the position and track the contact force. The developed controller uses geometric methods to control the attitude. In addition, the designed aerial manipulator decouples the roll and pitch of the UAV from the UAV, which improves the system’s stability. Experiments have been carried out to demonstrate the feasibility of this method.

This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

Human-centered intelligent human–robot interaction can transcend the traditional keyboard and mouse and have the capacity to understand human communicative intentions by actively mining implicit human clues (e.g., identity information and emotional information) to meet individuals’ needs. Gait is a unique biometric feature that can provide reliable information to recognize emotions even when viewed from a distance. However, the insufficient amount and diversity of training data annotated with emotions severely hinder the application of gait emotion recognition. In this paper, we propose an adversarial learning framework for emotional gait dataset augmentation, with which a two-stage model can be trained to generate a number of synthetic emotional samples by separating identity and emotion representations from gait trajectories. To our knowledge, this is the first work to realize the mutual transformation between natural gait and emotional gait. Experimental results reveal that the synthetic gait samples generated by the proposed networks are rich in emotional information. As a result, the emotion classifier trained on the augmented dataset is competitive with state-of-the-art gait emotion recognition works.

In this paper, the Hill Valley (HV) approach is applied to the drifting masses or agents in the basic Gravitational Search Algorithm (GSA) for the path planning of humanoid robots. The drift in lighter masses toward the heavier mass creates a localized area, where the probability of obtaining a globally optimal solution is very high. So, the present work is focused on exploiting the area to tackle local optima and provide the best steering angle for the humanoids to navigate. The HV approach is applied to the basic GSA model, at the later stages, to improve the overall computational time and cost. The robustness of the proposed controller was tested in both simulation and experimental environments and compared with the previous research. The results obtained from the proposed controller showed a significant improvement in the overall path length and time taken. Path smoothness was also given equal importance during path planning to ensure stability. The multi-robot navigational scheme was performed using the Dining Philosopher’s model to avoid dynamic collision among the humanoids. The percentage deviation in the results was within the acceptable limits. To further check the effectiveness of the proposed technique, the proposed approach was compared with the vision-based navigation in danger space.

The collection of facial action data is essential for the accurate evaluation of a patient’s condition in the intensive care unit, such as pain evaluation. An automatic face-tracking system is demanded to reduce the burden of data collection on the medical staff. However, many previous studies assume that the optimal trajectory of a robotic tracking system is reachable which is inapplicable for large-amplitude head motions. To tackle this problem, we propose a region-based face-tracking algorithm for large-amplitude head motion with a 7-DOF manipulator. A configuration-based optimization algorithm is proposed to trade-off between theoretical optimal pose and workspace constraints through the assignment of importance weights. To increase the probability of recapturing the face exceeding the reachable workspace of the manipulator, the camera is directed toward the center of the head, named the facial orientation center (FOC) constraint. Furthermore, a region-based tracking approach is designed to stabilize the manipulator for small amplitude head motions and smooth the tracking trajectory by adjusting the joint angle in the null space of the 7-DOF manipulator. Experimental results demonstrate the effectiveness of the proposed algorithm in tracking performance and finding an appropriate configuration for the unreachable theoretical optimal configuration. Moreover, the proposed algorithm with FOC constraint can successfully follow the head motion as losing 33.2% of the face during the tracking.

A force sensorless impedance controller is proposed in this paper for robot manipulators without using force estimators. From the observation of the impedance control law, the force feedback term can be canceled if the inertia matrix in the target impedance is the same as the robot inertia matrix. However, the inertia matrix in the target impedance is almost always a constant matrix, while the robot inertia matrix is a function of the robot configuration, and hence, they may not be identical in general. A modification of the coefficient matrix for the contact force term in the target impedance is suggested in this paper to enable cancelation of the force feedback term in the impedance control law so that a force sensorless impedance controller without using force estimators can be obtained. The tracking performance in the free space phase and the motion trajectory in the compliant motion phase of the new design are almost the same as those in the traditional impedance control. Modification of the inertia matrix in the target impedance will result in small variations of the contact force which is acceptable in practical applications. For robot manipulators containing uncertainties, an adaptive version of the new controller is also developed in this paper to give satisfactory performance without the need for force sensors. Rigorous mathematical justification in closed-loop stability is given in detail, and computer simulations are performed to verify the efficacy of the proposed design.

In today’s world, in order to increase the movement abilities of amputees, different types of passive prostheses are used according to the level of the person’s disability. Although these types of prostheses increase a person’s mobility; however, they still have a limited ability to help the amputee walk normally on uneven terrain, as there is no net stimulus input power for the target joint. These limitations have led to the growth of the use of active prostheses in recent years, which has led to a variety of prosthetic designs. The purpose of this research is, first of all, to provide a suitable design and control of active transtibial prosthesis close to the performance of lost limb of a healthy person, and second and more importantly, to develop a 7-link inverse dynamic model of human gait and use it to analyze of an amputee gait with the designed prosthesis. Winter’s reference data are used in the entire process of design and simulation of prosthesis performance. Also, it is assumed that the amputation occurred in only one leg of the person. Based on the obtained results, when an amputee with 57 kg weight and 1.55 cm height wears an active prosthesis designed with 0.5 kg extra weight, the amount of metabolic cost of amputee in the swing phase increases by about 20%. By using this obtained model, it will be possible to optimize different prosthesis designs for people with different weight and height conditions.

With the use of springs, a method to balance the constant forces in arbitrary directions on a planar serial manipulator is developed in this study. Gravity balancing has been discussed a lot in the past. However, manipulators usually bear forces from various directions rather than only a fixed one as gravity. For instance, an industrial manipulator would bear forces from everywhere during the working process. Therefore, a method to balance these forces in arbitrary directions with springs is proposed. Based on the representation of energy, spring energy is the function of springs’ attachment points. Two spring systems with different attachment angles are needed to balance respectively forces in arbitrary directions and gravity. The spring installations of the above systems on 3-DoF manipulators are proposed. Finally, a resistive force-balanced manipulator with/without gravity balance in the grinding process is shown. In sum, this paper for the first time develops the balancing method for forces in arbitrary directions, expanding the spring balance theory to a broader application.

Although robotic or robot-assisted surgery has been increasingly used by many surgical disciplines, its application in cranial or skull base surgery is still in its infancy. Master-slave teleoperation setting of these robotic systems enables these surgical procedures to be replicated in a virtual reality environment for surgeon training purposes. A variety of teleoperation modes were previously determined with respect to the motion capability of the surgeon’s hand that wears the ring as the surgeon handles a surgical tool inside the surgical workspace. In this surgery training simulator developed for a robot-assisted endoscopic skull base surgery, a new strategy is developed to identify the preferred motion axes of the surgeon. This simulator is designed specifically for tuning the teleoperation system for each surgeon via the identification. This tuning capability brings flexibility to adjust the system operation with respect to the motion characteristics of the surgeon.