It is usually proposed to use a robotic manipulator for performing on-orbit capture of a target satellite in the planned active debris removal and on-orbit servicing missions. Control of the satellite-manipulator system is challenging because motion of the manipulator influences position and orientation of the chaser satellite. Moreover, the trajectory selected for the capture manoeuvre must be collision-free. In this article, we consider the case of a nonredundant manipulator mounted on a free-floating satellite.We propose to use the bi-directional rapidly-exploring random trees (RRT) algorithm to achieve two purposes: to plan a collision-free manipulator trajectory that, at the same time, will result in a desired change of the chaser satellite orientation. Several improvements are introduced in comparison to the previous applications of the RRT method for manipulator mounted on a free-floating satellite. Feasibility of the proposed approach is demonstrated in numerical simulations performed for the planar case in which the chaser satellite is equipped with a 2-DoF (Degree of Freedom) manipulator. The obtained results are analysed and compared with the results obtained from collision-free trajectory planning methods that do not allow to set the desired final orientation of the chaser satellite.

Parallel robots are widely used in the fields of manufacturing, medical science, education, scientific research, etc. Many studies have been conducted on the topic already. However, shortcomings still exist, especially in certain situations. To meet the demand of good speed and load performances at the same time, this work presents a novel 2-degree-of-freedom parallel robot. The structural design, static, stiffness, and reachable workspace analysis of the robot are given in the manuscript. Experiment regarding the accuracy and speed performance is conducted, and the results are provided. In the end, potential applications of the proposed robot are suggested.

This paper introduces a dual-user training system whose design is based on an energetic approach. This kind of system is useful for supervised hands-on training where a trainer interacts with a trainee through two haptic devices, in order to practice on a manual task performed on a virtual or teleoperated robot (e.g., for an Minimally Invasive Surgery (MIS) task in a surgical context). This paper details the proof of stability of an Energy Shared Control (ESC) architecture we previously introduced for one degree of freedom (d.o.f.) devices. An extension to multiple degrees of freedom is proposed, along with an enhanced version of the Adaptive Authority Adjustment function. Experiments are carried out with 3 d.o.f. haptic devices in free motion as well as in contact contexts in order to show the relevance of this architecture.

This paper focuses on robust control of a simplest passive model, which is established on a DCLF (discrete control Lyapunov function) -based control system, and presents gait transition method based on the study of purely passive walker. Firstly, the DCLF is introduced to stabilize walking process between steps exponentially by modulating the length of next step. Next, the swing leg trajectory from mid-stance position to foot-strike can be planned. Then the control law is calculated to resist external disturbance. Besides, an impulse is added just before foot-strike to realize a periodic walking pattern on flat or uphill ground. With walking terrain varying, the robot can transit to an adaptive walking gait in a few steps. With different push or pull disturbances acting on hip joint and the robot gait transiting on a continuously slope-changing downhill, the effectiveness of the presented DCLF-based method is verified using simulation experiments. The ability to walk on a changing environment is also presented by simulation results. The insights of this paper can help to develop a robust control method and adaptive walking of dynamic passive locomotion robots.

Working with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.

In the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.

Cable-driven parallel robots (CDPRs) possess a lot of advantages over conventional parallel manipulators and link-based robot manipulators in terms of acceleration due to their low inertia. This paper deals with under-constrained CDPRs, which manipulate the end-effector to carrying the payload by using a number of cables less than six, often used preferably owing to their simple structures. Since a smaller number of cables than six are used, the end-effector of CDPR has uncontrollable degrees of freedom and that causes swaying motion and oscillations. In this paper, a scheme to curb on the unwanted oscillation of the end-effector of the CDPR with three cables is proposed based on multimode input shaping. The precise dynamic model of the under-constrained CDPR is obtained to find natural frequencies, which depends on the position of the end-effector. The advantage of the proposed method is that it is practicable to generate the trajectories for vibration suppression based on multi-mode input-shaping scheme in spite of the complexity in the dynamics and the difficulty in computing the natural frequencies of the CDPR, which are required in any input-shaping scheme. To prove the effectiveness of the proposed method, computer simulations and experiments were carried out by using 3-D motion for CDPR with three cables.

The flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

We establish an efficient minimum-time cooperative task planning algorithm for robots and operators to serve clients. This problem is an extension of the multiple traveling salesman problem and the vehicle routing problem with synchronization constraints, but it is more difficult due to the cooperative tasks of the robots and operators. To find the exact minimum-time task plan with a small tree size, we propose an efficient branch-and-bound with a good initial tree and keen complementary heuristics. Our algorithm is also effective as an approximate algorithm for the many clients problem within the capacity of the computer used. The efficiency of our algorithm is revealed via case studies: telemedical service robots and planetary exploration robots.

Human–robot interaction (HRI) is one of the most rapidly growing research fields in robotics and promising for the future of robotics technology. Despite the fact that numerous significant research results in HRI have been presented during the last years, there are still challenges in several critical topics of HRI, which could be summarized as: (i) collision and safety, (ii) virtual guides, (iii) cooperative manipulation, (iv) teleoperation and haptic interfaces, and (v) learning by observation or demonstration. In physical HRI research, the complementarity of the human and the robot capabilities is carefully considered for the advancement of their cooperation in a safe manner. New advanced control systems should be developed so the robot will acquire the ability to adapt easily to the human intentions and to the given task. The possible applications requiring co-manipulation are cooperative transportation of bulky and heavy objects, manufacturing processes such as assembly and surgery.

Electrical distribution equipment inspection is crucial for the electric power industry. With the rapid increase in the number of electrical distribution rooms, an unattended inspection method, for example, autonomous inspection robot, is eagerly desired by the industry to make up for the deficiencies of traditional manual inspection in effectiveness and validity. Existing inspection robots designed for indoor substations are generally lack of practicality, due to the factors such as inspection requirements and robot weight. To bridge the gap between prototype and practicality, in this work, we design the first completely autonomous robotic system, LongSword, which provides a satisfying technical solution for equipment inspection with an optical zoom camera, a thermal imaging camera or a partial discharge detector. Firstly, we design a novel and flexible hardware architecture which allows the robot to move, lift, and rotate in the station to reach any desired position. Secondly, we develop an intelligent software framework which consists of several modules to achieve accurate equipment recognition and reliable failure diagnosis. Thirdly, we achieve an apposite integration of the existing technologies to implement an applicable robotic system that can fulfill the requirements of indoor equipment inspection. There are over 200 LongSwords currently serving about 160 electrical distribution rooms, some of which have been working for more than 1 year. The average precision of device status recognition is up to 99.70%, and the average inspection time of a single device is as short as 13.5 s. The feedback from workers shows that LongSword can significantly improve the efficiency and reliability of equipment inspection, which accelerates the process of setting up unmanned stations.

In order to improve the training efficiency and establish a multi-person cooperative training simulation system, including “virtual human,” in the process of virtual reality-based astronaut training, it is necessary to plan the velocity at which astronauts carry the target object. A velocity planning algorithm, combining a traditional six-stage acceleration/deceleration algorithm, based on a time-discrete model with high-order dynamic constraints, considering the elastic damping torque of the space suit, is proposed. The described algorithm is verified on MATLAB to prove its feasibility. Compared to other algorithms, the planning time of the proposed algorithm is significantly reduced.

Dynamic modeling is a fundamental step in analyzing the movement of any mechanical system. Methods for dynamical modeling of constrained systems have been widely developed to improve the accuracy and minimize computational cost during simulations. The necessity to satisfy constraint equations as well as the equations of motion makes it more critical to use numerical techniques that are successful in decreasing the number of computational operations and numerical errors for complex dynamical systems. In this study, performance of a variant of Kane’s method compared to six different techniques based on the Lagrange’s equations is shown. To evaluate the performance of the mentioned methods, snake-like robot dynamics is considered and different aspects such as the number of the most time-consuming computational operations, constraint error, energy error, and CPU time assigned to each method are compared. The simulation results demonstrate the superiority of the variant of Kane’s method concerning the other ones.

The paper presents a comprehensive design process for the development of the minimally actuated Closed Loop Articulated Mechanical (CLAM) hand. Each of the fingers is designed as a planar one degree of freedom eight-bar linkage with an anthropomorphic backbone chain. The fingers movement is based on experimentally obtained physiological precision grasping task, with incorporated second-order task specifications, related to maintaining fingertip–body contact with a minimum number of fingers. Instead of actuating individual joints in each finger, the mechanism generates the desired anthropomorphic grasping trajectory using a single actuator in each finger. The paper offers not only details on multi-loop articulated hands design based on anthropometric data and physiological task with second-order effects for maintaining the object–fingertip contact, but also shows how this class of hands that have been considered mostly for adaptive grasping can be successfully utilized for precision grasping. The minimal number of fingers and actuators can simplify the control, resulting in a robust, lightweight, and cost-effective solution for the precision grasping of a variety of objects with different shapes and geometries.

Path planning represents planning collision-free strategies to move from starting point to ending point. These strategies can be carried out for known and unknown environments. Recently, a novel and reduced CPU-time modeling and simulation methodology for path planning in known environment based on resistive grids (RGs) has been introduced. In this work, a novel modified version of Resistive Grid Path Planning Methodology (RGPPM) methodology is presented with the purpose of exploring collision-free path planning for robotic arms. This extension of the methodology allows to numerically relate positions in the RG with angular values of the robotic systems. In addition, it is possible to include obstacles in the configuration space, and therefore collision detection can be established for RGs. Finally, the variation of links for robotic arms and obstacles for configuration space is explored by simulating different scenarios.

This paper presents a non-model-based collision detection algorithm for robots without external sensors and with closed control architecture. A reference signal of repetitive motion is recorded from the robot operation. To detect collisions, the reference is compared with measurements from the robot. One of the key contributions is a novel approach to optimal matching of compared signals, which is ensured by the newly developed modified Dynamic Time Warping (mDTW) method presented in this paper. One of the main improvements of the mDTW is that it enables comparing a signal with the most similar section of the other signal. Partial matching also enables online application of time warping principles and reduces the time and computation resources needed to perform matching. In addition to mDTW, two complementary decision rules are developed to identify collisions. The first rule, based on the absolute difference between compared matched samples, uses statistically determined thresholds to perform rapid detection of unambiguous collisions. The second rule is based on Eigen values of the covariance matrix of matched samples, and it employs its higher sensitivity to detect collisions with lower intensity. Results from experimental validation of the proposed collision algorithm on two industrial robots are shown.

Image stitching is important for the perception and manipulation of undersea robots. In spite of a well-developed technique, it is still challenging for undersea images because of their inevitable appearance ambiguity caused by the limited light in the undersea environment, and local disturbance caused by moving objects, ocean current, etc. To get a clean and stable background panorama in the undersea environment, this paper proposes an undersea image-stitching method by introducing graph-based registration and blending procedures. Specifically, in the registration procedure, matching the features in each undersea image pair is formulated and solved by graph matching, to incorporate the structural information between features. In the blending procedure, an energy function on the indirect graph Markov random field is proposed, which takes both image consistency and neighboring consistency into consideration. Coincidentally, both graph matching and energy minimization can be mathematically formulated by integer quadratic programming problems with different constraints; the recently proposed graduated nonconvexity and concavity procedure is used to optimize both problems. Experiments on both synthetic images and real-world undersea images witness the effectiveness of the proposed method.

In this paper, the behavioral learning of robots through spiking neural networks is studied in which the architecture of the network is based on the thalamo-cortico-thalamic circuitry of the mammalian brain. According to a variety of neurons, the Izhikevich model of single neuron is used for the representation of neuronal behaviors. One thousand and ninety spiking neurons are considered in the network. The spiking model of the proposed architecture is derived and prepared for the learning problem of robots. The reinforcement learning algorithm is based on spike-timing-dependent plasticity and dopamine release as a reward. It results in strengthening the synaptic weights of the neurons that are involved in the robot’s proper performance. Sensory and motor neurons are placed in the thalamus and cortical module, respectively. The inputs of thalamo-cortico-thalamic circuitry are the signals related to distance of the target from robot, and the outputs are the velocities of actuators. The target attraction task is used as an example to validate the proposed method in which dopamine is released when the robot catches the target. Some simulation studies, as well as experimental implementation, are done on a mobile robot named Tabrizbot. Experimental studies illustrate that after successful learning, the meantime of catching target is decreased by about 36%. These prove that through the proposed method, thalamo-cortical structure could be trained successfully to learn to perform various robotic tasks.

This paper deals with the robust force and position control problems of series elastic actuators (SEAs). It is shown that an SEA’s force control problem can be described by a second-order dynamic model which suffers from only matched disturbances. However, the position control dynamics of an SEA is of fourth order and includes matched and mismatched disturbances. In other words, an SEA’s position control is more complicated than its force control, particularly when disturbances are considered. A novel robust motion controller is proposed for SEAs by using disturbance observer (DOb) and sliding mode control. When the proposed robust motion controller is implemented, an SEA can precisely track desired trajectories and safely contact with an unknown and dynamic environment. The proposed motion controller does not require precise dynamic models of environments and SEAs. Therefore, it can be applied to many different advanced robotic systems such as compliant humanoids, industrial robots and exoskeletons. The validity of the proposed motion controller is experimentally verified.

In this paper, a force and torque controller for aerial manipulation is developed using an unmanned aerial vehicle equipped with a robotic arm to interact near or on a vertical surface such as a wall. Control of aerial manipulators interacting with the environment is a challenging task due to dynamic interactions between aerial vehicles, robotic arms, and environment. To achieve this, modeling of aerial manipulators is first investigated and presented considering interaction with the environment. Nonlinear models of generic aerial manipulators, as well as of a prototype aerial manipulator composed of a hexacopter with a three-joint robotic arm, are established. An equilibrium-based force and torque controller is developed to conduct tasks that require the aerial manipulator to exert forces and torques on a wall. Simulations and experiments validate the performance of the controller that successfully applies desired forces and torques to an object fixed on a wall while flying near the wall.