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 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.

This article presents modelling, simulation, and development of a wall-climbing robot based on coupled wheel and arm-type locomotion mechanism. The developed robot consists of two mobile modules connected with a robot arm mechanism. The actuation of the robot arm is inspired by inchworm locomotion, particularly during wall-to-wall transition, obstacle avoidance, and uneven surface locomotion. Easiness in the interchanging of wheel to arm and vice versa makes the robot more effective compared to previously developed wall-climbing robots. The kinematic and dynamic model for the proposed coupled wheel and arm locomotion concept has been established. A combination of particle swarm optimization (PSO) and proportional, integral, derivatives (PID) feedback control algorithm has been developed using MATLAB to simulate the different cases of robot motions. The developed prototype of the wall-climbing robot is used to verify the coupled wheel and arm locomotion concept in various wall climbing scenarios. The simulation and experimental findings show good comparisons and validate the model-based design of the wall-climbing robot.

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

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

In this paper, a novel self-adaptive underactuated robot hand with rigid-flexible coupling fingers (SAU-RFC hand) is proposed. The seven degrees of freedom (DOFs) SAU-RFC hand is driven by four servomotors, consists of three fingers, including two side-turning (ST) fingers and one non-side-turning finger. Specially, the ST fingers can perform synchronous reverse rotation laterally with each other. Each finger with three joints and two DOFs introduces a flexible structure, and the inner part of the proximal phalanx that makes most of the contact with the object is replaced by a flexible belt. The fingers can generate flexion/extension under the pull of the flexible belt, and the middle and distal phalanxes are mechanically coupled through a four-bar linkage. In particular, the flexible belt in the inner direction of the finger will deform, while it will not deform in the outer direction since the outer is a rigid structure. The flexible belt not only plays the role of transmitting power but also has the effect of uniformizing the contact force. Due to the rigid-flexible finger structure, the developed robot hand has a higher self-adaptive grasping ability for objects with different shapes, sizes, and hardness. In addition, the kinematic and kinetic analyses of SAU-RFC hand are performed. A contact force distribution model is established for the flexible belt, which demonstrates its effect of promoting uniform force distribution theoretically. In the end, experiments are conducted on different objects to verify the performance of SAU-RFC hand.

In this paper, a method based on neural networks for intelligently extracting weighting matrices of the optimal controllers’ cost function is presented. Despite the optimal and robust performance of controllers with the cost function, adjusting their gains which are the weighting matrices for the system state variables vector and the system inputs vector, is a challenging and time-consuming task that is usually selected by trial and error method for each specific application; and even little changes in the weighting matrices significantly impact problem-solving and system optimization. Therefore, it is necessary to select these gains automatically to improve controller performance and delete human energy to find the best gains. As a linear controller, linear quadratic regulator, and as a nonlinear controller, nonlinear model predictive control have been employed with trained networks to track the path of a wheeled mobile robot. The simulation and experimental results have been extracted and compared to validate the proposed method. These results have been demonstrated that the intelligent controller’s operation has lower error than the conventional method, which works up to 7% optimal in tracking and up to 19% better in angle state error; furthermore, as the most important aim, the required time and effort to find the weighting matrices in various situations has been omitted.

In this paper, a hydraulic soft gripper for underwater applications is designed to provide a solution for improving the gripping force as well as the sensing capability of the soft gripper. The soft gripper is made of silicone and has an integrated semi-circular hydraulic network inside. To enhance the rigidity and grasping performance of the soft gripper, we have integrated a restriction layer consisting of a spring steel plate in the soft gripper. Meanwhile, to enhance the sensing capability of this soft gripper, we have designed a water pressure sensor based on resistance strain gauges and integrated it on the spring steel plate. Before fabrication, we determined the structural parameters of the soft gripper by geometric analysis. Then we experimentally evaluated its pressure-bearing capacity, bending performance, the role of spring steel plates, and the accuracy of the sensor.The experimental results show that the spring steel plate improves the gripping force of the soft gripper, the sensor also has high accuracy, and the built four-finger gripping system has good adaptability to objects of different shapes and weights.Compared with the existing solutions, this solution takes a simpler structural form while improving the gripping force and sensing ability of the soft gripper, and integrates the issues of improving the gripping force and sensing ability. The spring steel plate used in this paper not only improves the gripping force of the soft gripper but also provides a stable and reliable platform for installing sensors.

To integrate the uneven terrain adaptivity of legged robots and the fast capacity of wheeled robots on even terrains, a four wheel-legged robot is addressed and the cooperative control strategy of wheels and legs based on attitude balance is investigated. Firstly, the kinematics of wheel-legged robot is analyzed, which contains the legged and wheeled motion modal. Secondly, the cooperative control strategy of wheel-legged robot based on attitude balance is proposed. The attitude is calculated by using the quaternion method and complementary filtering, and the attitude stability control of the wheel-legged robot is studied. The trajectory planning of leg motion including walk and trot gait is implemented, and the differential control of wheeled motion is deduced. And then, the cooperative motion control of wheels and legs is achieved by keeping the attitude balance of robot body. Finally, a small prototype is set up to validate the feasibility and effectiveness of proposed method. The experimental results show that the established wheel-legged robot can do walk, trot, and wheel-leg compound motion to overcome many complex terrains and environments.

In this paper, the realization of any specified planar Cartesian compliance for an object grasped by a compliant hand is addressed. The hands considered have 2 or more fingers for which each has 3 modulated elastic joints and predetermined link lengths. Geometric construction-based compliance synthesis procedures are developed. Using these procedures, a large set of compliant behaviors can be realized by a single hand simply by adjusting the configuration of each finger and by adjusting the joint stiffness (using variable stiffness actuation) of each finger joint.

The control of industrial robot manipulators presents a difficult problem for control engineers due to the complexity of their nonlinear dynamics models. Nonlinear controls based on feedback linearization are developed to meet control requirements. Model-based nonlinear control is highly sensitive to parameter errors and leads to problems of robustness for tracking trajectories at high speeds, and there is the additional problem of a heavy computational burden to consider in the design of nonlinear controllers. In this paper, a mechatronic design approach is proposed, which aims to facilitate controller design by redesigning the mechanical structure. The problem is approached in two steps: first, the dynamic decoupling conditions of manipulators are described and discussed, involving redistribution of the moving mass, which leads to the decoupling of motion equations. A classical linear control law is then used to track the desired efficient bang-bang profile trajectory. Then, in the presence of parameter uncertainty and external disturbances, the nonlinear controls with simple structures are adopted to stabilize the decoupled system asymptotically. An analysis of the results from a simulation of this approach demonstrates its effectiveness in controller design. The proposed improvement in control performance is illustrated via two spatial manipulators.

Variational Bayesian inference is an important machine learning tool that finds application from statistics to robotics. The goal is to find an approximate probability density function (PDF) from a chosen family that is in some sense “closest” to the full Bayesian posterior. Closeness is typically defined through the selection of an appropriate loss functional such as the Kullback-Leibler (KL) divergence. In this paper, we explore a new formulation of variational inference by exploiting the fact that (most) PDFs are members of a Bayesian Hilbert space under careful definitions of vector addition, scalar multiplication, and an inner product. We show that, under the right conditions, variational inference based on KL divergence can amount to iterative projection, in the Euclidean sense, of the Bayesian posterior onto a subspace corresponding to the selected approximation family. We work through the details of this general framework for the specific case of the Gaussian approximation family and show the equivalence to another Gaussian variational inference approach. We furthermore discuss the implications for systems that exhibit sparsity, which is handled naturally in Bayesian space, and give an example of a high-dimensional robotic state estimation problem that can be handled as a result. We provide some preliminary examples of how the approach could be applied to non-Gaussian inference and discuss the limitations of the approach in detail to encourage follow-on work along these lines.

Collision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

We propose a hierarchical cognitive navigation model (HCNM) to improve the self-learning and self-adaptive ability of mobile robots in unknown and complex environments. The HCNM model adopts the divide and conquers approach by dividing the path planning task into different levels of sub-tasks in complex environments and solves each sub-task in a smaller state subspace to decrease the state space dimensions. The HCNM model imitates animal asymptotic properties through the study of thermodynamic processes and designs a cognitive learning algorithm to achieve online optimum search strategies. We prove that the learning algorithm designed ensures that the cognitive model can converge to the optimal behavior path with probability one. Robot navigation is studied on the basis of the cognitive process. The experimental results show that the HCNM model has strong adaptability in unknown and environment, and the navigation path is clearer and the convergence time is better. Among them, the convergence time of HCNM model is 25 s, which is 86.5% lower than that of HRLM model. The HCNM model studied in this paper adopts a hierarchical structure, which reduces the learning difficulty and accelerates the learning speed in the unknown environment.

This paper focuses on configuration design, dimensional synthesis, and engineering application of a novel asymmetric 2R1T parallel mechanism (PM) with zero-coupling degree. The analytical forward and inverse displacement solutions are deduced by the means of vector method. The mathematical models between Euler angles and the orientational parameters (i.e., azimuth and tilt angles) of the offset output axis are established. Using screw theory as mathematical tool, this paper worked out evaluation indices of motion/force transmissibility and presented the definitions and calculation methods of good transmission orientation workspace and good transmission orientation capacity (GTOC). Furthermore, a comparative example with respect to kinematic performance of asymmetric UPS-RPU-PU PM and planar symmetric 2UPS-PU PM is carried out, and the result demonstrates that UPS-RPU-PU significantly outperforms 2UPS-PU in terms of GTOC. The constrained optimization model is constructed to formulate optimal problem of dimensional parameters on the maximizing GTOC, which is then solved by differential evolution algorithm. Finally, an engineering case demonstrates that the optimized mechanism has a good application prospect in hydraulic support test bed.

Modern approaches for exploration path planning generally do not assume any structural information regarding the operational area. Therefore, they offer good performance when the region of interest is entirely unknown. However, for some applications such as plantation forest surveying, partial information regarding the survey area is known before the exploration process. Because the region of interest consists only of the lower portions of the tree stems themselves, the ground and high-elevation sections of the environment are unimportant and do not need to be observed. Due to these unconventional conditions, existing methods favoring faster survey speeds produce suboptimal surveys as they do not try and ensure even coverage across the entire exploration volume, while methods that favor reconstruction accuracy produce excessively long survey times. This work proposes a structured exploration approach specifically for plantation forests utilizing a lawnmowing pattern to maximize coverage while minimizing re-visited regions, guiding the unmanned aerial vehicle to visit all areas. Experiments are conducted in various environments, with comparisons made to state-of-the-art exploration planners regarding survey time and coverage. Results suggest that the proposed methods produce surveys with significantly more predictable coverage and survey times at the expense of a longer survey.

Joint actuators with a compact size and high power density are necessary for robots with rotating joints, making the hydraulic actuator (HyA) an ideal actuator candidate. This paper presents a joint HyA with the following characteristics: compact installation size of 70 mm × 92.5 mm × 145 mm, low weight of 1.93 kg, high output torque of 742.2 Nm/531.09 Nm in two directions under 210 bar, high torque to weight ratio of 265.5 Nm/kg, low internal leakage of about 9 mL/min, zero external leakage, low starting pressure of 0.26 MPa/0.39 MPa in two directions, and a large rotation angle of 135 degrees. Compared with HyAs that have been applied in robot joints, the HyA proposed in this paper can greatly reduce the joint weight, reduce the joint size, and ensure the control performance of the joint movement.

In addition, a dynamic model of the HyA is established. Based on this, some dynamic design suggestions are given. Furthermore, a simple position and torque control algorithm for the HyA is proposed. Finally, some experiments are carried out to verify the performance of the HyA.

Recently, deep learning methods have achieved considerable performance in gesture recognition using surface electromyography signals. However, improving the recognition accuracy in multi-subject gesture recognition remains a challenging problem. In this study, we aimed to improve recognition performance by adding subject-specific prior knowledge to provide guidance for multi-subject gesture recognition. We proposed a time–frequency feature transform suite (TFFT) that takes the maps generated by continuous wavelet transform (CWT) as input. The TFFT can be connected to a neural network to obtain an end-to-end architecture. Thus, we integrated the suite into traditional neural networks, such as convolutional neural networks and long short-term memory, to adjust the intermediate features. The results of comparative experiments showed that the deep learning models with the TFFT suite based on CWT improved the recognition performance of the original architectures without the TFFT suite in gesture recognition tasks. Our proposed TFFT suite has promising applications in multi-subject gesture recognition and prosthetic control.

Biped robots with dynamic motion control have shown strong robustness in complex environments. However, many motion planning methods rely on models, which have difficulty dynamically modifying the walking cycle, height, and other gait parameters to cope with environmental changes. In this study, a heuristic model-free gait template planning method with dynamic motion control is proposed. The gait trajectory can be generated by inputting the desired speed, walking cycle, and support height without a model. Then, the stable walking of the biped robot can be realized by foothold adjustment and whole-body dynamics model control. The gait template can be changed in real time to achieve gait flexibility of the biped robot. Finally, the effectiveness of the method is verified by simulations and experiments of the biped robot BHR-B2. The research presented here helps improve the gait transition ability of biped robots in dynamic locomotion.