In this paper, a Pythagorean-Hodograph (PH) curve-based pick-and-place operation trajectory planning method for Delta parallel robots is proposed, which realizes the flexible control of pick-and-place operations to meet the requirements of various practical scenarios. First, according to the geometric relationship of pick-and-place operation path, different pick-and-place operations are classified. Then trajectory planning is carried out for different situations, respectively, and in each case, the different polynomial motion laws adopted by the linear motion segment and the curved motion segment are solved. Trajectory optimization is performed with the motion period as optimization objective. The proposed method is easier to implement, and at the same time satisfies the safety, optimization, mobility, and stability of the robot; that is, the proposed method realizes obstacle avoidance, optimal time, flexible control of the robot trajectory, and stable motion. Simulations and experiments verify the effectiveness of the method proposed in this paper. The proposed method can not only realize the fast, accurate, and safe operation in intelligent manufacturing fields such as rapid classification, palletizing, grasping, warehousing, etc., but its research route can also provide a reference for trajectory planning of intelligent vehicles in logistics system.

Owing to inherent flexibility, compliant actuator with physical elastic elements can implement compliant interactions between robot and environment. Nonlinear stiffness elastic actuators (NSEAs) use one motor to adjust position and stiffness, which improve compactness of variable stiffness actuators, and consider high torque/force resolution and high bandwidth. The primary challenge of designing a NSEA is designing a reliable compliant mechanism with a given torque-deflection profile. In this study, a general design method of torsional compliant mechanism through given discrete torque-deflection points was proposed, where a chain algorithm based on 2R pseudo-rigid-body model was used to describe the large deformation in elastic elements. Moreover, the theoretical model of stiffness of a compliant mechanism based on series flexible beams was developed. Based on the proposed model, the dimensional parameters of proposed compliant mechanism were designed satisfying the condition of the given torque-deflection points. Finally, simulation and physical experiment of the designed compliant mechanism through three given torque-deflection points are conducted to show the effectiveness of the proposed method. The designed compliant mechanism was tested and evaluated in the self-developed NSEA.

Craniovertebral junction (CVJ) is one of the more complex parts of the spinal column. It provides mobility to the cranium and houses the spinal cord. In a healthy subject, the CVJ contributes 25% of the flexion–extension motion and 50% of the axial rotation of the neck. This work reports instrumentation development and results for evaluating implant performance in the stabilized CVJ after surgical procedures. Typically, some bony parts of the vertebrae causing compression to the spinal cord are removed and subsequently stabilized by the instrumenting implant in the CVJ. Pose estimation of the Cadaveric CVJ region is estimated using a monocular vision-based setup. The cervical spine’s first three vertebrae surround the CVJ area, where most cervical spine mobility originates. We aim to evaluate the performance of vision-based intervertebral motion estimation of the Cadaver’s CVJ in the Indian population, particularly in older people. A series of tests were performed on the Cadaver’s CVJ to evaluate the vision system-based motion estimation performance.

With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.

Path-following control of wheeled mobile robots has been a crucial research topic in robotic control theory and applications. In path-following control with obstacles, the path-following control and collision avoidance goals might be conflicting, making it challenging to obtain near-optimal solutions for path-following control and obstacle avoidance with low tracking error and input energy consumption. To address this problem, we propose a potential field-based dual heuristic programming (P-DHP) algorithm with an actor–critic (AC) structure for path-following control of mobile robots with obstacle avoidance. In the proposed P-DHP, the path-following control and collision avoidance problems are decoupled into two ones to resolve the control conflict. Firstly, a neural network-based AC is constructed to approximate the near-optimal path-following control policy in a no-obstacle environment. Then, with the trained path-following control policy fixed, a potential field-based control policy structure is constructed by another AC network to generate opposite control forces as the robot moves toward the obstacle, which can guarantee the robot’s control safety and reduce the tracking error and input energy consumption in obstacle avoidance. The simulated and experimental results show that P-DHP can realize near-optimal path-following control with the satisfaction of safety constraints and outperforms state-of-the-art approaches in control performance.

Underwater archaeology is of great significance for historical and cultural transmission and preservation of underwater heritage, but it is also a challenging task. Underwater heritage is located in an environment with high sediment content, objects are mostly buried, and the water is turbid, resulting in some of the features of objects missing or blurred, making it difficult to accurately identify and understand the semantics of various objects in the scene. To tackle these issues, this paper proposes a global enhancement network (GENet) underwater scene parsing method. We introduce adaptive dilated convolution by adding an extra regression layer, which can automatically deduce adaptive dilated coefficients according to the different scene objects. In addition, considering the easy confusion in the process of fuzzy feature classification, an enhancement classification network is proposed to increase the difference between various types of probabilities by reducing the loss function. We verified the validity of the proposed model by conducting numerous experiments on the Underwater Shipwreck Scenes (USS) dataset. We achieve state-of-the-art performance compared to the current state-of-the-art algorithm under three different conditions: conventional, relic semi-buried, and turbidified water quality. The experimental results show that the proposed algorithm performs best in different situations. To verify the generalizability of the proposed algorithm, we conducted comparative experiments on the current publicly available Cityscapes, ADE20K, and the underwater dataset SUIM. The experimental results show that this paper achieves good performance on the public dataset, indicating that the proposed algorithm is generalizable.

Ankle rehabilitation robots are widely used due to nerve injuries and sports injuries leading to decreased mobility of the ankle joint. However, the motion of most ankle rehabilitation robots has distinctions with human ankle physiological structure. In order to achieve more accurate rehabilitation training of the ankle joint, this paper proposes a novel 3-UPU parallel rehabilitation mechanism. In a certain range, the mechanism can perform rotation around any axis within the midplane, which means that the mechanism can achieve non-fixed-point rotation around the instantaneous axis of the ankle joint. The mechanism has three degrees of freedom and can perform ankle pronation/supination and inversion/eversion movements. Taking into account the structural differences of different human bodies, the rotating axis of the mechanism can be adjusted in both height and angle. Then, the workspace of the mechanism was solved, and the size parameters of the mechanism are analyzed based on the characteristics of the size parameters of the mechanism and the motion range of the ankle. A genetic algorithm was employed to optimize the mechanism’s parameters. Next, the motion trajectory of the mechanism was planned, and the length change of the mechanism driving pair during the motion planning of the angle was obtained through kinematics simulation. Finally, experimental verification of the above rehabilitation training methods indicates that the mechanism meets the requirements of ankle rehabilitation.

In this paper, we present a broad learning control method for a two-link flexible manipulator with prescribed performance (PP) and actuator faults. The trajectory tracking errors are processed through two consecutive error transformations to achieve the constraints in terms of the overshoot, transient error, and steady-state error. And the barrier Lyapunov function is employed to implement constraints on the transition state variable. Then, the improved radial basis function neural networks combined with broad learning theory are constructed to approximate the unknown model dynamics of flexible robotic manipulator. The proposed fault-tolerant PP control cannot only ensure tracking errors converge into a small region near zero within the preset finite time but also address the problem caused by actuator faults. All the closed-loop error signals are uniformly ultimately bounded via the Lyapunov stability theory. Finally, the feasibility of the proposed control is verified by the simulation results.

This work addresses the gravity balancing of a 2R1T (2 rotations – 1 translation) mechanism with remote center of motion. A previously developed balancing solution is modified and applied to a prototype, and test results are presented. The mechanism is an endoscope holder for minimally invasive transnasal pituitary gland surgery. In this surgery, the endoscope is inserted through a nostril of the patient through a natural path to the pituitary gland. During the surgery, it is vital for the manipulator to be statically balanced so that in case of a motor failure, the patient is protected against any harmful motion of the endoscope. Additionally, static balancing takes the gravitational load from the actuators and hence facilitates the control of the mechanism. The mechanism is a 2URRR-URR type parallel manipulator with three legs. The payload mass is distributed to the legs on the sides. By using counter-masses for two links of each leg, the center of mass of each leg is lumped on the proximal link which simplifies the problem of balancing of a two degree-of-freedom inverted pendulum. The two proximal links with the lumped mass are statically balanced via springs. Dynamic simulations indicate that when the mechanism is statically balanced, generated actuator torques are reduced by 93.5%. Finally, the balancing solution is implemented on the prototype of the manipulator. The tests indicate that the manipulator is statically balanced within its task space when the actuators are disconnected. When the actuators are connected, the torque requirements decrease by about 37.8% with balancing.

This Special Issue of the Robotica is on recent advances in field and service robotics with a focus on the use of robotic and autonomous technologies to handle tasks in harsh environments and tasks that involve the multirobot cooperation and human–robot interactions.

This paper presents a comprehensive study of the forward and inverse kinematics of a six-degrees-of-freedom (DoF) spatial manipulator with a novel architecture. Developed by Systemantics India Pvt. Ltd., Bangalore, and designated as the H6A (i.e., Hybrid 6-Axis), this manipulator consists of two arm-like branches, which are attached to a rigid waist at the proximal end and are coupled together via a wrist assembly at the other. Kinematics of the manipulator is challenging due to the presence of two multi-DoF passive joints: a spherical joint in the right arm and a universal in the left. The forward kinematic problem has eight solutions, which are derived analytically in the closed form. The inverse kinematic problem leads to $160$ solutions and involves the derivation of a $40$-degree polynomial equation, whose coefficients are obtained as closed-form symbolic expressions of the pose parameters of the end-effector, thus ensuring the generality of the results over all possible inputs. Furthermore, the analyses performed lead naturally to the conditions for various singularities involved, including certain non-trivial architecture singularities. The results are illustrated via numerical examples which are validated extensively.

Robotic rovers equipped with articulated rocker-bogie suspension have aroused great interest after the explorations on Mars; this interest has also shifted to different types of terrestrial applications such as in the agriculture, military, and rescue fields. The suspension can be designed so that, when the rover is on flat and horizontal ground, the weight is evenly distributed among the wheels; in this way, all wheels have the same traction capability and offer the same rolling resistance. As the operating conditions vary due to sloping ground, uneven ground surface, or different payload position, the weight distribution can undergo considerable variations. This type of suspension is statically determined with respect to weight, but it is indeterminate with respect to traction forces; the traction control system aims to avoid the wheels slippage. In this paper, the traction contribution that each wheel can provide, to overcome a step obstacle, is shown. Furthermore, the possibility of regulating the distribution of vertical loads among the wheels adopting a torsion spring, with adjustable preload, arranged between rocker and bogie, is evaluated. A suitable spring preload facilitates the initial phase of the obstacle overcoming if the rover advances with the bogie forward. Numerical simulations show that to increase the possibility of overcoming an obstacle it is sufficient for the spring preload to reduce the vertical load on the front wheel; in any case, a higher load variation would not be advisable as it could involves an excessive load difference among the wheels.

Due to the ever-increasing demand for food commodities and issues arising in their transport from rural to urban areas, commercial agricultural practices with the help of vertical farming are being taken up near urban regions. For the realization of agricultural practices on high-rise vertical farms, where human intervention is quite laborious, robotic assistance would be an effective solution to perform agricultural processes like seeding, transplanting, harvesting, health monitoring, nutrient-water supply, etc. The requirements and complexities of these tasks to be performed are different such as end-effector requirement, payload capacity required, amount of clutter while performing the task, etc. In such cases, an individual robotic configuration would not serve all the purposes and each task may require a different configuration. Purchasing a large number of configurations, as per requirement, is not economical and will also increase the cost of maintenance. Thus, the design of a reconfigurable robot manipulator is proposed in this work which can cater to modular layouts. A thorough study of the processes involved in the farming of leafy vegetables is done and the tasks to be performed by the manipulator are identified. Constrained optimization is performed based on reachability, while minimizing DoF, for the tasks of transplanting, plant heath monitoring, and harvesting to find the optimal configurations which can perform the given tasks. The study resulted in 5-DoF, 4-DoF, and 6-DoF configurations for transplanting, plant heath monitoring, and harvesting, respectively, thus emphasizing the need of a reconfigurable solution. The configurations are realized using modular library and verified to satisfy reachability to provide a complete solution.

The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.

Exoskeleton robot can assist people and reduce energy consumption when they walk with heavy weight, so as to protect their health and travel longer distances. This work analyzes the cross gait during walking and designs a dual-drive four joint time-sharing assistance exoskeleton system, which controls the four joints through two motors to realize the assistance to the wearer’s movement process. The control curve and adaptive control algorithm are designed to help different people with various walking gaits and speeds, the effectiveness of exoskeleton system is proved by testing metabolism. When the exoskeleton wearer carries 25 kg weight (load equal to 36% of body mass) and travels at the average speed of 5 km/h, the metabolic rate of the exoskeleton wearers decreases by an average of 7.79%, the reduction magnitude is comparable to the effect of taking off 7.33 kg during walking.

In order to decrease the influence of system parameters and load on the dynamic performance of permanent magnet synchronous motor (PMSM) in cooperative robot joint modules, a practical model-based robust control method was proposed. It inherits the traditional proportional-integral-derivative (PID) control and robust control based on error and model-based control. We first set up the nominal controller using the dynamics model. In order to limit the influence of uncertainty on dynamic performance, a robust controller is established based on Lyapunov method. The control can be regarded as an improved PID control or a redesigned robust control. Compared with the traditional control method, it is simple to implement and has practical effects. It is proved by theoretical analysis that the controller can guarantee the uniform boundedness and uniform final boundedness of the system. In addition, the prototype of fast controller cSPACE is built on the experiment platform, which averts long-time programming and debugging. It offers immense convenience for practical operation. Finally, numerical simulation and real-time experiment results are presented. Based on cSPACE and a PMSM in the joint module of a practical cooperative robot, the availability of the control design and the achievable control performance are verified.

Gripper is one of the most important parts of robot because of contacting with workpieces directly and has attracted lots of research interests. However, the existing grippers are either simple in function or complex in structure. In this paper, we will propose a one-DOF gripper based on a compliant mechanism with four identical twofold-symmetric Bricard linkages. A mobile network with four identical twofold-symmetric Bricard linkages with particular design parameters is constructed at first. Kinematics, such as mobility, singularity, and folding performance, is then analyzed to show the potential of realizing the function of grasping. The result is demonstrated with a physical prototype. To simplify the fabricating process, a compliant mechanism of the network is designed and fabricated with a single polypropylene board, and the grasping function is realized by a cable-driven scheme. Some grasping experiments are carried out on different types of objects which shows that the proposed and fabricated four-figure gripper is simple in structure and has a great grasping function. The work provides a new idea for the design of grippers with low cost, simple structure, and rich functions.

This paper presents a new approach for geometrically constrained path planning applied to the field of robotic grasping. The method proposed in this paper is based on the Fast Marching Square (FM $\, ^2$ ) and a path calculation approach based on an optimization evolutionary filter named Differential Evolution (DE). The geometric restrictions caused by the link lengths of the kinematic chain composed by the robot arm and hand are introduced in the path calculation phase. This phase uses both the funnel potential of the surroundings created with FM $\, ^2$ and the kinematic constraints of the robot as cost functions to be minimized by the evolutionary filter. The use of an optimization filter allows for a near-optimal solution that satisfies the kinematic restrictions, while preserving the characteristics of a path computed with FM $\, ^2$ . The proposed method is tested in a simulation using a robot composed by a mobile base with two arms.

Today, robots can be found helping humans with their daily tasks. Some tasks require the robot to visit a set of locations in the environment efficiently, like in the Traveling Salesman Problem. As indoor environments are maze-like areas, feasible paths connecting locations must be computed beforehand, so they can be combined during the scheduling, which can be impracticable for real-time applications. This work presents an on-line Route Scheduling supported by a Fast Path Planning Method able to adjust pre-built paths. Experiments were carried out with virtual and real robots to evaluate time and quality of tours.