Due to a large number of redundant degrees of freedom (DOFs), the hyper-redundant manipulator shows outstanding dexterity and adaptability in avoiding the obstacles in confined space. In this paper, a hybrid obstacle-avoidance method of spatial hyper-redundant manipulators is proposed, with both efficiency and accuracy considered. The space around an obstacle is classified into safe, warning, and dangerous zones. A two-level protection strategy is then addressed to handle the obstacle-avoidance problem from qualitative (i.e., pseudo-distance based on super-quadric function) and quantitative (i.e., Euclidean distance based on practical geometry function) perspectives, respectively. The only condition for switching between the two-level protections is the value of pseudo-distance. Then, a modified modal method, which is a trajectory planning method, is presented to plan the collision-free trajectory of the manipulator by maximizing the minimum pseudo-distance or Euclidean distance in different zones. Some parameters, including the arm-angle parameters and the equivalent link length parameters, are defined to represent the manipulator configuration. They are adjusted to avoid the obstacle, singularity, and joint limit. The simulations of 12-DOF manipulator and an experiment of 18-DOF manipulator verify the proposed methods.

The hyper-redundant manipulators are suitable for working in the constrained on-orbit servicing environment due to the extreme flexibility. However, its modelling and control are very challenging due to the characteristics of non-linearity and strong coupling. In this paper, considering the multi-level mapping among the motors, cables, joints, and end-effector, a proportional derivative (PD) with dynamic feedforward compensation control system is designed. The corresponding control system is divided into five parts: controller, planner, actuator, manipulator, and sensor. The actual control torque consisting of the desired feedforward torque and the feedback torque is generated by the controller. In order to improve the tracking accuracy and maintain rapid response, the torque, which is calculated by the dynamics model of the traditional joint-driven manipulator, is regarded as the desired feedforward torque. The parameters of interest are the angle and velocity of the universal joint and motors. The planner plans and converts the desired parameters of the universal joint to corresponding motors. Combining with the feedback angles and velocities signals of the corresponding motors, the feedback torque can be calculated by the PD control module. Finally, typical cases of six universal joints (12DOFs) manipulators are simulated and experimented. The results demonstrate that the method is very efficient for controlling spatial cable-driven hyper-redundant manipulators.

This paper aims to provide an optimal design of geometric parameters of a special architecture of the delta parallel mechanism, in order to improve positioning accuracy, workspace size, and kinematic and dynamic performance characteristics. In the studied 3[P2(US)] robot, the radius of both fixed and moving platforms, length of the connecting rods, and installation angle of the actuators of the manipulator are chosen as the decision variables. These parameters are optimized to maximize the weighted objective function, comprising workspace volume, global dexterity, global mass, global error, and global error sensitivity indices. Optimizations are performed employing two distinct algorithms, Genetic and Harmony Search whose results confirm each other. The optimal design of the robot leads to maximum workspace size, high dexterity, and dynamic performance, with a minimum error of the end-effector position in its reachable workspace.

This paper presents a simple and highly visual approach for the type synthesis of a family of overconstrained parallel mechanisms that have one translational and two rotational movement capabilities. It considers, especially, mechanisms offering the accuracy and dynamic response needed for machining applications. This family features a spatial limb plus a member of a class of planar symmetrical linkages, the latter connected by a revolute joint either to the machine frame at its base link or to the platform at its output link. Criteria for selecting suitable structures from among numerous candidates are proposed by considering the realistic practical requirements for reconfigurability, movement capability, rational component design and so on. It concludes that a few can simultaneously fulfil the proposed criteria, even though a variety of structures have been presented in the literature. Exploitation of the proposed structures and evaluation criteria then leads to a novel five degrees of freedom hybrid module named TriMule. A significant potential advantage of the TriMule over the Tricept arises because all the joints connecting the base link and the machine frame can be integrated into one single, compact part, leading to a lightweight, cost effective and flexible design particularly suitable for configuring various robotized manufacturing cells.

The purpose of this study is to determine the dynamic load carrying capacity (DLCC) of a manipulator that moves on the specified path using a new closed loop optimal control method. Solution methods for designing nonlinear optimal controllers in a closed-loop form are usually based on indirect methods, but the proposed method is a combination of direct and indirect methods. Optimal control law is given by solving the nonlinear Hamilton–Jacobi–Bellman (HJB) partial differential equation. This equation is complex to solve exactly for complex dynamics, so it is solved numerically using the Galerkin procedure combined with a nonlinear optimization algorithm. To check the performance of the proposed algorithm, the simulation is performed for a fixed manipulator. The results represent the efficiency of the method for tracking the pre-determined path and determining the DLCC. Finally, an experimental test has been done for a two-link manipulator and compare with simulation results.

This paper presents a unified formulation for the kinematics, singularity and workspace analyses of parallel delta robots with prismatic actuation. Unlike the existing studies, the derivations presented in this paper are made by assuming variable angles and variable link lengths. Thus, the presented scheme can be used for all of the possible linear delta robot configurations including the ones with asymmetric kinematic chains. Referring to a geometry-based derivation, the paper first formulates the position and the velocity kinematics of linear delta robots with non-iterative exact solutions. Then, all of the singular configurations are identified assuming a parametric content for the Jacobian matrix derived in the velocity kinematics section. Furthermore, a benchmark study is carried out to determine the linear delta robot configuration with the maximum cubic workspace among symmetric and semi-symmetric kinematic chains. In order to show the validity of the proposed approach, two sets of experiments are made, respectively, on the horizontal and the Keops type of linear delta robots. The experiment results for the confirmation of the presented kinematic analysis and the simulation results for the determination of the maximum cubic workspace illustrate the efficacy and the flexible applicability of the proposed framework.

Robust positioning and navigation of a mobile robot in an urban environment is implemented by fusing the Global Positioning System (GPS) and Inertial Navigation System (INS) data with the aid of a motion estimator. To select and isolate malicious satellite signals and guarantee the minimum number of GPS signals for the localization, an enhanced fault detection and isolation (FDI) algorithm with a short-term memory has been developed in this research. When there are sufficient satellite signals for positioning, the horizontal dilution of precision (HDOP) has been applied for selecting the best four satellite signals to localize the mobile robot. Then, the GPS data are fused with INS data by a Kalman filter (KF) for a straight path and a curved motion estimator (CME) for a curved path. That is, the INS data are properly fused to the GPS data through the KF or CME process. To verify the effectiveness of the proposed algorithm, experiments using a mobile robot have been carried out on a university campus.

Kinematic calibration plays an important role in the improvement of positioning accuracy for parallel manipulators. Based on the specific geometric constraints of limbs, this paper presents a new kinematic parameter identification method for the widely studied 3-PRS parallel manipulator. In the proposed calibration method, the planes where the PRS limbs exactly located are identified firstly as the geometric characteristics of the studied parallel manipulator. Then, the limbs can be considered as planar PR mechanisms whose kinematic parameters can be determined conveniently according to the limb planes identified in the first step. The main merit of the proposed calibration method is that the system error model which relates the manipulator’s kinematic errors to the output ones is not required for kinematic parameter identification. Instead, only two simple geometric problems need to be established for identification, which can be solved readily using gradient-based searching algorithms. Hence, another advantage of the proposed method is that parameter identification of the manipulator’s limbs can be accomplished individually without interactive impact on each other. In order to validate the effectiveness and efficiency of the proposed method, calibration experiments are conducted on an apparatus of the studied 3-PRS parallel manipulator. The results show that using the proposed two-step calibration method, the kinematic parameters can be identified quickly by means of gradient searching algorithm (converge within five iterations for both steps). The positioning accuracy of the studied 3-PRS parallel manipulator has been significantly improved by compensation according to the identified parameters. The mean position and orientation errors at the validation configurations have been reduced to 1.56 × 10−4 m and 1.13 × 10−3 rad, respectively. Further, the proposed two-step kinematic calibration method can be extended to other limited-degree-of-freedom parallel manipulators, if proper geometric constraints can be characterized for their kinematic limbs.

In this paper, we present a passive lower extremity exoskeleton with a simple structure and a light weight. The exoskeleton does not require any external energy source and can achieve energy transfer only by human body’s own gravity. The exoskeleton is self-adaptive to human gait to achieve basic matching therewith. During walking, pulling forces are generated through Bowden cables by pressing plantar power output devices by feet, and the forces are transmitted to the exoskeleton through a crank-slider mechanism to enable the exoskeleton to provide torques for the ankle and knee joints as required by the human body during the stance phase and the swing phase. Our self-developed gait detection system is used to perform experiments on kinematics, dynamics and metabolic cost during walking of the human body wearing the exoskeleton in different states. The experimental results show that the exoskeleton has the greatest influence on motion of the ankle joint and has the least influence on hip joint. With the increase in elastic coefficient of the spring, the torques generated at the joints by the exoskeleton increase. When walking with wearing k3EF exoskeleton at a speed of 0.5 m/s, it can save the most metabolic cost, reaching 13.63%.

This paper presents a control strategy for solving the cable pseudo-drag problem of cable-driven parallel camera robots at high speeds. The control strategy belongs to a hybrid position/tension control method based on cable tension optimization. The cable catenary model and cable pseudo-drag problem are considered firstly. Then, the dynamic model of the cable-driven parallel camera robot is established. The cable tension optimization is proposed. And then a control strategy is put forward and its stability is proved. Simulation results of a four-cable camera robot are presented and discussed.

The goal of this paper is to present a new hybrid system based on the fusion of gaze data and Steady State Visual Evoked Potentials (SSVEP) not only to command a powered wheelchair, but also to account for users distraction levels (concentrated or distracted). For this purpose, a multi-layer perception neural network was set up in order to combine relevant gazing and blinking features from gaze sequence and brainwave features from occipital and parietal brain regions. The motivation behind this work is the shortages raised from the individual use of gaze-based and SSVEP-based wheelchair command techniques. The proposed framework is based on three main modules: a gaze module to select command and activate the flashing stimuli. An SSVEP module to validate the selected command. In parallel, a distraction level module estimates the intention of the user by mean of behavioral entropy and validates/inhibits the command accordingly. An experimental protocol was set up and the prototype was tested on five paraplegic subjects and compared with standard SSVEP and gaze-based systems. The results showed that the new framework performed better than conventional gaze-based and SSVEP-based systems. Navigation performance was assessed based on navigation time and obstacles collisions.

In this paper, a path planning algorithm for robotic systems with excess degrees of freedom (DOF) for welding of intersecting pipes is presented. At first step, the procedure of solving the inverse kinematics considering system kinematic redundancy is developed. The robotic system consists of a 6 DOF robotic manipulator installed on a railed base with linear motion. Simultaneously, the main pipe is able to rotate about its longitudinal axis. The system redundancy is employed to improve weld quality. Three different simulation studies are performed to show the effect of the robotic system kinematic redundancy to plan a better path for the welding of intersecting pipes. In the first case, it is assumed that robotic manipulator base and main pipe are fixed, and the path is planned only with manipulator joints motion. In the second case, only the robot base is free to move and the main pipe is fixed, and in the third case, the main pipe is free to rotate together with the base of the manipulator. It is seen that kinematic constraints according to the system’s redundancy will help to plan the most efficient path for the welding of complex pipe joints.

We present singularity-free workspace optimization of a class of two-degree-of-freedom (2-DoF) parallel wrists with large rotation range capability. The wrists in consideration are kinematically equivalent to two families of 2-DoF homokinetic couplings. The first family comprises fully parallel wrists with N (N ≥ 3) double-universal ($\mathcal{UU}$) legs. The second family comprises spherical N-$\mathcal{UU}$ parallel wrists with interconnecting revolute ($\mathcal{R}$) joints. Both families belong to the more general class of zero-torsion parallel manipulators, and are, therefore, collectively referred to as zero-torsion wrists (ZTWs). We carry out a unified singularity-free workspace optimization by utilizing geometric properties of zero-torsion motion manifolds. Our work may serve as a conceptual guide to the design of ZTWs for large tilt-angle applications.

A Q-learning approach is often used for navigation in static environments where state space is easy to define. In this paper, a new Q-learning approach is proposed for navigation in dynamic environments by imitating human reasoning. As a model-free method, a Q-learning method does not require the environmental model in advance. The state space and the reward function in the proposed approach are defined according to human perception and evaluation, respectively. Specifically, approximate regions instead of accurate measurements are used to define states. Moreover, due to the limitation of robot dynamics, actions for each state are calculated by introducing a dynamic window that takes robot dynamics into account. The conducted tests show that the obstacle avoidance rate of the proposed approach can reach 90.5% after training, and the robot can always operate below the dynamics limitation.

Autonomous landing on the deck of a boat or an unmanned surface vehicle (USV) is the minimum requirement for increasing the autonomy of water monitoring missions. This paper introduces an end-to-end control technique based on deep reinforcement learning for landing an unmanned aerial vehicle on a visual marker located on the deck of a USV. The solution proposed consists of a hierarchy of Deep Q-Networks (DQNs) used as high-level navigation policies that address the two phases of the flight: the marker detection and the descending manoeuvre. Few technical improvements have been proposed to stabilize the learning process, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Simulated studies proved the robustness of the proposed algorithm against different perturbations acting on the marine vessel. The performances obtained are comparable with a state-of-the-art method based on template matching.

Path planning under 2D map is a key issue in robot applications. However, most related algorithms rely on point-by-point traversal. This causes them usually cannot find the strict shortest path, and their time cost increases dramatically as the map scale increases. So we proposed RimJump to solve the above problem, and it is a new path planning method that generates the strict shortest path for a 2D map. RimJump selects points on the edge of barriers to form the strict shortest path. Simulation and experimentation prove that RimJump meets the expected requirements.

In this work we present NEUROExos, a novel generation of upper-limb exoskeletons developed in recent years at The BioRobotics Institute of Scuola Superiore Sant’Anna (Italy). Specifically, we present our attempts to progressively (i) improve the ergonomics and safety (ii) reduce the encumbrance and weight, and (iii) develop more intuitive human–robot cognitive interfaces. Our latest prototype, described here for the first time, extends the field of application to assistance in activities of daily living, thanks to its compact and portable design. The experimental studies carried out on these devices are summarized, and a perspective on future developments is presented.

This work presents a systematic design selection methodology that utilizes a co-design strategy for system-level optimization of compliantly actuated robots that are known for their advantages over robotic systems driven by rigid actuators. The introduced methodology facilitates a decision-making strategy that is instrumental in making selections among system-optimal robot designs actuated by various degrees of variable or fixed compliance. While the simultaneous co-design method that is utilized throughout guarantees systems performing at their full potential, a homotopy technique is used to maintain integrity via generation of a continuum of robot designs actuated with varying degrees of variable and fixed compliance. Fairness of the selection methodology is ensured via utilization of common underlying (variable) compliant actuation principle and dynamical task requirements throughout the generated system designs. The direct consequence of the developed methodology is that it allows robot designers make informed selections among a variety of systems which are guaranteed to perform at their best. Applicability of the introduced methodology has been validated using a case study for system-optimal design of an active knee prosthesis that is driven by a mechanically adjustable compliance and controllable equilibrium position actuator (MACCEPA) under a periodic/real-life dynamical task.

Designers of robotic groups are faced with the formidable task of creating effective coordination architectures that can deal with collisions due to changing environment conditions and hardware failures. Communication between robots is a mechanism that can at times be helpful in such systems, but can also create a time, energy, or computation overhead that reduces performance. In dealing with this issue, different communication schemes have been proposed ranging from those without any explicit communication, localized algorithms, and centralized or global communicative methods. Finding the optimal communication act is typically an intractable problem in real-world problems. As a result, we argue that at times group designers should use computationally bounded team communication approaches. We propose two such approaches: an algorithm selection approach to communication whereby robots choose between a known group of communication schemes and a parameterized communication framework whereby robots can reason about how large a communication radius is needed for a given problem. Both solutions use a novel coordination cost measure, combined coordination costs, to find the appropriate level of communication within such groups. Robots can then use this measure to create adaptive communication approaches that select between communication approaches as needed during task execution. We validated this approach through conducting extensive experiments in a canonical robotic foraging domain and found that robotic groups using these adaptive methods were able to significantly increase their productivity compared to teams that used only one type of communication scheme.

This paper introduces a novel 2R1T parallel manipulator redundantly actuated by pneumatic muscles for lower-limb rehabilitation. First, the conceptual design of the proposed 3-DOF parallel mechanism is presented. Then, the inverse kinematics and the generalized Jacobian analysis are carried out. Based on the generalized Jacobian and the constraint characteristics of the mechanism, the force/motion transmissibility of the redundantly actuated parallel mechanism is investigated via four individual cases without actuation redundancy, leading to a suitable local transmission index for the evaluation of kinematic performance of the proposed mechanism. Finally, the design variables are optimized by maximizing the mean value of the local transmission index with the aid of genetic algorithm. The numerical result shows that the proposed parallel mechanism can achieve a good kinematic performance in its task workspace.