Robotics, Volume 11, Issue 2 (April 2022) – 24 articles

This study aims to investigate the effects of the degree of freedom (DOF) and weight of the hand exoskeleton (HE) on hand joint mobility function (ease of movement, movement range) in fine hand use activities. A three-digit passive HE prototype was built to fit each of the 12 participants. Two DOF setups (three DOF, two DOF), two digits’ weight levels (70 g, 140 g), and barehand conditions were tested. A productivity task (performed with Standardized-Nine Hole Peg Test) and motion tasks, both performing the tip pinch and tripod pinch, were conducted to measure the task completion time and the range of motion (ROM) of the digit joints, respectively, using a motion capture system. The perceived ease rating was also measured. The results showed that DOF reduction and weight addition caused a significant task completion time increase and rating drop (p < 0.05). Meanwhile, the DOF reduction increased the ROM reduction of the proximal interphalangeal joints; however, the weight addition caused a correction of the ROM reduction of several joints (p < 0.05) at the tripod pinch. In conclusion, wearing an HE reduces hand joint mobility, especially in lower DOF. However, a certain weight addition may improve joint mobility in terms of the fingers’ movement range. Full article

Particle swarm optimisation (PSO) is a swarm intelligence algorithm used for controlling robotic swarms in applications such as source localisation. However, conventional PSO algorithms consider only the intensity of the received signal. Wavefield signals, such as propagating underwater acoustic waves, permit the measurement of higher order statistics that can be used to provide additional information about the location of the source and thus improve overall swarm performance. Wavefield correlation techniques that make use of such information are already used in multi-element hydrophone array systems for the localisation of underwater marine sources. Additionally, the simplest model of a multi-element array (a two-element array) is characterised by operational simplicity and low-cost, which matches the ethos of robotic swarms. Thus, in this paper, three novel approaches are introduced that enable PSO to consider the higher order statistics available in wavefield measurements. In simulations, they are shown to outperform the standard intensity-based PSO in terms of robustness to low signal-to-noise ratio (SNR) and convergence speed. The best performing approach, cross-correlation bearing PSO (XB-PSO), is capable of converging to the source from as low as −5 dB initial SNR. The original PSO algorithm only manages to converge at 10 dB and at this SNR, XB-PSO converges 4 times faster. Full article

This study proposes a performance index for the dimensional optimization of parallel manipulators with specific tasks. In particular, the index evaluates the dexterity of the mechanism to be designed and compares it with that of the required specific task, e.g., rehabilitation tasks. The proposed index is implemented to design a 3UPS + RPU parallel manipulator for performing physical rehabilitation treatments on lower limbs. First, the condition numbers of both the mechanism and the lower limb are determined. Subsequently, the indexes are compared such that both systems exhibit similar dexterity. As a case study, the approach is implemented in the dimensional synthesis of the 3UPS + RPU parallel manipulator. The optimization approach enables obtaining a dexterity space of the mechanism that best matches that of the lower limb. The results are graphically presented, showing the matching areas of both workspaces, verifying the effectiveness of the proposed index. Full article

In a factory with different kinds of spatial atmosphere (warehouses, corridors, small or large workshops with varying sizes of obstacles and distribution patterns), the robot’s generated paths for navigation tasks mainly depend on the representation of that environment. Hence, finding the best representation for each particular environment is necessary to forge a compromise between length, safety, and complexity of path planning. This paper aims to scrutinize the impact of environment model representation on the performance of an automated guided vehicle (AGV). To do so, a multi-objective cost function, considering the length of the path, its complexity, and minimum distance to obstacles, is defined for a perfect circular robot. Unlike other similar studies, three types of representation, namely quadrangle, irregular triangle, and varying-size irregular triangle, are then utilized to model the environment while applying an inflation layer to the discretized view. Finally, a navigation scenario is tested for different cell decomposition methods and an inflation layer size. The obtained results indicate that a nearly constant coarse size triangular mesh is a good candidate for a fixed-size robot in a non-changing environment. Moreover, the varying size of the triangular mesh and grid cell representations are better choices for factories with changing plans and multi-robot sizes due to the effect of the inflation layer. Based on the definition of a metric, which is a criterion for quantifying the performance of path planning on a representation type, constant or variable size triangle shapes are the only and best candidate for discretization in about 59% of industrial environments. In other cases, both cell types, the square and the triangle, can together be the best representation. Full article

Straight-line needle insertion is a prevalent tool in surgical interventions in the brain, such as Deep Brain Stimulation and Convection-Enhanced Delivery, that treat a range of conditions from Alzheimer’s disease to brain cancer. Using a steerable needle to execute curved trajectories and correct positional deviation could enable more intervention possibilities, while reducing the risk of complication in these procedures. This paper experimentally identifies model parameters using an expectation-maximization (EM) algorithm for two different steerable needle models. The results compared a physically motivated model to the established bicycle needle model and found the former to be preferred for modeling soft brain tissue needle insertion. The results also supported the experimentally parameterized models’ use in future applications such as needle steering control. Full article

This paper aims to improve the positioning accuracy of serial industrial manipulators using force feedback in manufacturing processes by implementing an elasto-geometrical model-based control. Initially, the real-time position control strategy using a force feedback to elastically correct the Tool Center Point (TCP) pose of serial industrial manipulators is detailed. To continue, an efficient model structure identification and calibration is proposed to shorten the elasto-geometrical modeling process. The Virtual Joint Method (VJM) is chosen to iterate and complete the robot stiffness modeling. This method considers that the elastic deformations are only localized at the joints of the robot. An appropriate and original test-model approach allows a minimum of optimization iterations to find the best compromise between complexity and accuracy of the modeling. The proposed approach is illustrated in detail by the Stäubli TX200 robot modeling. Finally, the reliability and responsiveness of the developed control framework is then evaluated through experimental tests in an Incremental Sheet Forming (ISF) context. An average improvement of 70% in trajectory-tracking accuracy is achieved during these tests. Overall, the high accuracy and responsiveness of the developed system demonstrate a promising potential for deploying industrial manipulators to a cost-effective manufacturing processes in industry $4.0$. Full article

Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s). Full article

Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks. Full article

COVID-19 patients are strongly affected in terms of limb motion when imbedded during the acute phase of the infection, but also during the course of recovery therapies. Peculiarities are investigated for design requirements for medical devices in limb motion assistance for those patients. Solutions are analyzed from existing medical devices to outline open issues to provide guidelines for the proper adaption or for new designs supporting patients against COVID-19 effects. Examples are reported from authors’ activities with cable driven assisting devices. Full article

The foundation and emphasis of robotic manipulator control is Inverse Kinematics (IK). Due to the complexity of derivation, difficulty of computation, and redundancy, traditional IK solutions pose numerous challenges to the operation of a variety of robotic manipulators. This paper develops a Deep Reinforcement Learning (RL) approach for solving the IK problem of a 7-Degree of Freedom (DOF) robotic manipulator using Product of Exponentials (PoE) as a Forward Kinematics (FK) computation tool and the Deep Q-Network (DQN) as an IK solver. The selected approach is architecturally simpler, making it faster and easier to implement, as well as more stable, because it is less sensitive to hyperparameters than continuous action spaces algorithms. The algorithm is designed to produce joint-space trajectories from a given end-effector trajectory. Different network architectures were explored and the output of the DQN was implemented experimentally on a Sawyer robotic arm. The DQN was able to find different trajectories corresponding to a specified Cartesian path of the end-effector. The network agent was able to learn random Bézier and straight-line end-effector trajectories in a reasonable time frame with good accuracy, demonstrating that even though DQN is mainly used in discrete solution spaces, it could be applied to generate joint space trajectories. Full article

The complexity of forward kinematic modelling increases with the increase in the degrees of freedom for a manipulator. To reduce the computational weight and time lag for desired output transformation, this paper proposes a forward kinematic model mapped with the help of the Radial Basis Function Neural Network (RBFNN) architecture tuned by a novel meta-heuristic algorithm, namely, the Cooperative Search Optimisation Algorithm (CSOA). The architecture presented is able to automatically learn the kinematic properties of the manipulator. Learning is accomplished iteratively based only on the observation of the input–output relationship. Related simulations are carried out on a 3-Degrees of Freedom (DOF) manipulator on the Robot Operating System (ROS). The dataset created from the simulation is divided 65–35 for training–testing of the proposed model. The metrics used for model validation include spread value, cost and runtime for the training dataset, and Mean Relative Error, Normal Mean Square Error, and Mean Absolute Error for the testing dataset. A comparative analysis of the CSOA-RBFNN model is performed with an artificial neural network, support vector regression model, and with with other meta-heuristic RBFNN models, i.e., PSO-RBFNN and GWO-RBFNN, that show the effectiveness and superiority of the proposed technique. Full article

The term perception-driven obstacle-aided locomotion (POAL) was proposed to describe locomotion in which a snake robot leverages a sensory-perceptual system to exploit the surrounding operational environment and to identify walls, obstacles, or other structures as a means of propulsion. To attain POAL from a control standpoint, the accurate identification of push-points and reliable determination of feasible contact reaction forces are required. This is difficult to achieve with rigidly actuated robots because of the lack of compliance. As a possible solution to this challenge, our research group recently presented Serpens, a low-cost, open-source, and highly compliant multi-purpose modular snake robot with a series elastic actuator (SEA). In this paper, we propose a new prototyping iteration for our snake robot to achieve a more dependable design. The following three contributions are outlined in this work as a whole: the remodelling of the elastic joint with the addition of a damper element; a refreshed design for the screw-less assembly mechanism that can now withstand higher transverse forces; the re-design of the joint module with an improved reorganisation of the internal hardware components to facilitate heat dissipation and to accommodate a larger battery with easier access. The Robot Operating System (ROS) serves as the foundation for the software architecture. The possibility of applying machine learning approaches is considered. The results of preliminary simulations are provided. Full article

As a result of several governments closing educational facilities in reaction to the COVID-19 pandemic in 2020, almost 80% of the world’s students were not in school for several weeks. Schools and universities are thus increasing their efforts to leverage educational resources and provide possibilities for remote learning. A variety of educational programs, platforms, and technologies are now accessible to support student learning; while these tools are important for society, they are primarily concerned with the dissemination of theoretical material. There is a lack of support for hands-on laboratory work and practical experience. This is particularly important for all disciplines related to science, technology, engineering, and mathematics (STEM), where labs and pedagogical assets must be continuously enhanced in order to provide effective study programs. In this study, we describe a unique perspective to achieving multi-sensory learning through the integration of virtual and augmented reality (VR/AR) with haptic wearables in STEM education. We address the implications of a novel viewpoint on established pedagogical notions. We want to encourage worldwide efforts to make fully immersive, open, and remote laboratory learning a reality. Full article

Positioning a camera during laparoscopic and robotic procedures is challenging and essential for successful operations. During surgery, if the camera view is not optimal, surgery becomes more complex and potentially error-prone. To address this need, we have developed a voice interface to an autonomous camera system that can trigger behavioral changes and be more of a partner to the surgeon. Similarly to a human operator, the camera can take cues from the surgeon to help create optimized surgical camera views. It has the advantage of nominal behavior that is helpful in most general cases and has a natural language interface that makes it dynamically customizable and on-demand. It permits the control of a camera with a higher level of abstraction. This paper shows the implementation details and usability of a voice-activated autonomous camera system. A voice activation test on a limited set of practiced key phrases was performed using both online and offline voice recognition systems. The results show an on-average greater than 94% recognition accuracy for the online system and 86% accuracy for the offline system. However, the response time of the online system was greater than 1.5 s, whereas the local system was 0.6 s. This work is a step towards cooperative surgical robots that will effectively partner with human operators to enable more robust surgeries. A video link of the system in operation is provided in this paper. Full article

In this work, the concept and mechanical design of a novel compact, lightweight, omnidirectional three-legged robot, featuring a hybrid serial–parallel topology including leg compliance is proposed. The proposal focusses deeply on the design aspects of the mechanical realisation of the robot based on its 3D-CAD assembly, while also discussing the results of multi-body simulations, exploring the characteristic properties of the mechanical system, regarding the locomotion feasibility of the robot model. Finally, a real-world prototype depicting a single robot leg is presented, which was built by highly leaning into a composite design, combining complex 3D-printed parts with stiff aluminium and polycarbonate parts, allowing for a mechanically dense and slim construction. Eventually, experiments on the prototype leg are demonstrated, showing the mechanical model operating in the real world. Full article

By combining stochastic variational inference with message passing algorithms, we show how to solve the highly complex problem of navigation and avoidance in distributed multi-robot systems in a computationally tractable manner, allowing online implementation. Subsequently, the proposed variational method lends itself to more flexible solutions than prior methodologies. Furthermore, the derived method is verified both through simulations with multiple mobile robots and a real world experiment with two mobile robots. In both cases, the robots share the operating space and need to cross each other’s paths multiple times without colliding. Full article

Scoliosis is a spinal disorder that is conventionally treated using rigid or soft braces. Computational methods such as finite element-based models are used to investigate the mechanics of the spine and the effect of braces. Most spinal braces are either passive, static, or rigid and do not allow mobility to the spine, resulting in muscle atrophy, skin deterioration and other spine complexities. Lack of control over the amount of force being exerted by braces on the human spine could have adverse effects. Therefore, developing an active soft brace which allows mobility to the spine while applying controlled corrective forces could be a promising solution. This study presents finite element analysis (FEA) of an active soft brace that applies corrective forces using elastic bands. The pressure exerted by the brace on the spine can be controlled by varying the tensions in the elastic bands. The elastic band tensions are controlled using low-power, lightweight, and twisted string actuators (TSAs). This study aims to demonstrate the immediate corrections induced by the soft active brace using a scoliotic spine finite element (FE) model. A FE model of the patient’s trunk was created and validated with in vitro study. The brace model was installed on the simulated trunk to evaluate in-brace correction in both sagittal and coronal planes. The brace was evaluated under various load cases by simulating the actuator action. Full article

This paper presents dynamics modeling of a Delta robot with three revolute legs and a telescopic rod. Firstly, two generalized coordinate systems are established to describe the relationship between the movement of the telescopic rod and the position of the moving platform, and the telescopic rod system kinematics are established through singularity analysis. Secondly, taking the telescopic rod as the research object, the corresponding dynamics model is established using the Euler–Lagrange method. Moreover, this paper proposes a method to convert the force exerted by the telescopic rod motion on the moving platform into actuator torques. Thirdly, the dynamics model of the Delta robot with a telescopic rod is established, and numerical simulations are performed to demonstrate this approach. Finally, the influence of the telescopic rod on the actuator torques is verified using an experiment. A comparison is drawn between the two dynamics models used in torque feedforward control to validate the proposed dynamics model. Full article

Path planning is a key technology for the autonomous mobility of intelligent robots. However, there are few studies on how to carry out path planning in real time under the confrontation environment. Therefore, based on the deep deterministic policy gradient (DDPG) algorithm, this paper designs the reward function and adopts the incremental training and reward compensation method to improve the training efficiency and obtain the penetration strategy. The Monte Carlo experiment results show that the algorithm can effectively avoid static obstacles, break through the interception, and finally reach the target area. Moreover, the algorithm is also validated in the Webots simulator. Full article

The deflection compensation of a hydraulically actuated loader crane is presented. Measurement data from the laboratory are used to design a neural network deflection estimator. Kinematic expressions are derived and used with the deflection estimator in a feedforward topology to compensate for the static deflection. A dynamic deflection compensator is implemented, using pressure feedback and an adaptive bandpass filter. Simulations are conducted to verify the performance of the control system. Experimental results showcase the effectiveness of both the static and dynamic deflection compensator while running closed-loop motion control, with a 90% decrease in static deflection. Full article

A conventional feedback-linearization-based controller, when applied to a tiltrotor (eight inputs), results in extensive changes in tilting angles, which are not expected in practice. To solve this problem, we introduce the novel concept of “UAV gait” to restrict the tilting angles. The gait plan was initially used to solve the control problems in quadruped (four-legged) robots. Applying this approach, accompanied by feedback linearization, to a tiltrotor may give rise to the well-known non-invertible problem in the decoupling matrix. In this study, we explored invertible gait in a tiltrotor, and applied feedback linearization to stabilize the attitude and the altitude. The conditions necessary to achieve a full-rank decoupling matrix were deduced and simplified to near-zero roll and zero pitch. This paper proposes several invertible gaits to conduct an attitude–altitude control test. The accepted gaits within the region of interest were visualized. The simulation was conducted in Simulink, MATLAB. The results show promising responses in stabilizing attitude and altitude. Full article

The manipulability of a robotic arm may be defined based on ease of motion in different directions or ease of applying force/torque. In this study, we use manipulability measures to investigate how the kinematics of robots can be employed to calculate the optimal power required to drive the actuation systems of their arms. We hypothesize that the isotropy measure is related to the power consumption of the robotic arm. In addition to theoretical aspects, we consider practical applications that can minimize power consumption in robotic systems. Since the method is simple to implement and has no assumption on the robot’s work environment or dependence on information on the main power supply, manipulability measures can be used as a tool to predict the power consumption of robotic manipulators. Full article

This paper presents a method of optimizing the design of robotic manipulators using a novel kinematic model pruning technique. The optimization departs from an predefined candidate linkage consisting of a initial topology and geometry. It allows simultaneously optimizing the degree of freedom, the link lengths and other kinematic or dynamic performance criteria, while enabling the manipulator to follow the desired end-effector position and avoid collisions with the environment or itself. Current methods for design optimization rely on dedicated and complex frameworks, and solve the design optimization only as decoupled from each other in separate optimization problems. The proposed method only requires the introduction of a simple function, called a pruning function, as an objective function of an optimization problem. The introduced pruning function transforms a discrete topology optimization problem into a continuous problem that then can be solved simultaneously with other continuous objectives, using readily available optimization schemes. Two applications are presented: the optimization of a manipulator for the inspection of radio frequency cavities and a manipulator for maintenance within the future circular collider (FCC). Full article

The COVID-19 pandemic has placed unprecedented stress on the world healthcare system and demonstrated the need for modern automated robotic solutions for numerous medical applications. Often, robots that provide spherical motion of the end-effector are used in this area. In this paper, we discuss a spherical mechanism with a circular rail and provide several possible variations of the design: spherical robots with three or four legs and 4-DOF robots with an additional translational DOF, including a decoupled mechanism. The screw theory is used to analyze the mobility of the discussed mechanisms, and their advantages and drawbacks are discussed. Full article