The virtual model control (VMC) method establishes a direct correlation model between the end-effector and the main body by selecting appropriate virtual mechanical components. This approach facilitates direct force control while circumventing the necessity for complex dynamic modeling. However, the simplification inherent in this modeling can result in inaccuracies in the calculation of joint driving torques, ultimately diminishing control precision. Moreover, VMC typically depends on predefined models for control, which constrains its adaptability in dynamically complex environments and under varying movement conditions. To address these limitations, this paper proposes the BP-VMC method, which is based on a backpropagation neural network (BPNN). Initially, a quadruped robot model was established through kinematic analysis. Subsequently, a decomposed VMC model was developed, and BPNN was introduced to facilitate the adaptive tuning of virtual parameters. This approach resulted in the creation of a virtual mechanical component model with adaptive capabilities, compensating for errors arising from simplified modeling. Finally, a simulation control system was constructed based on the BP-VMC control framework to validate the optimization of control performance. Simulation experiments demonstrated that, in comparison to traditional VMC methods, the BP-VMC method exhibits enhanced control accuracy and stability, achieving a 30% reduction in trajectory tracking error, a 40% reduction in velocity tracking error, and a 20–30% improvement in instability indices across various working conditions. This evidence underscores the BP-VMC method’s robust adaptability in dynamic environments.

Onboard localization for multi-robot systems stands as a critical area of research with wide-ranging applications. This paper introduces an innovative framework for multi-robot localization, uniquely characterized by its onboard capability, thereby negating the dependency on external infrastructure. Our approach harnesses the inherent capabilities of each robot, enabling them to localize and synchronize their movements independently. The integration of cooperative localization algorithms with formation control mechanisms empowers a group of robots to sustain a predefined formation while following a linear trajectory. The efficacy of our framework is substantiated through comprehensive simulations and real-world experimental validations. We rigorously assess the system’s resilience to localization inaccuracies and external disturbances, demonstrating its adaptability and consistency in maintaining formation under diverse conditions. Furthermore, we explore the scalability of our approach, highlighting its potential to manage varying numbers of robots and its applicability in tasks such as collaborative transportation.

In this paper, a cellular robot for space trusses is structured so that it can perform tasks such as moving the truss and assembling the truss. There may be some spatial operating mechanisms on the space truss that cause obstacles to the robot’s movement, especially other mobile mechanical devices that are working, which are dynamic obstacles, so a suitable path planning for the robot is needed. In path planning, A-star algorithm has the advantages of efficient searching speed and good optimization effect, but it can’t deal with the path planning problem with dynamic obstacles, so this paper improves Lifelong Planning A-star (LPA-star) algorithm so that the improved algorithm satisfies the dynamic path planning task. Then a three-dimensional truss mathematical model is established, a dynamic obstacle environment is set up, the improved LPA-star algorithm is used for path planning, and the unimproved LPA-star algorithm and the improved A-star algorithm are used to compare with it. The simulation results show that in the environment set up in this paper, the optimal path length of the improved LPA-star algorithm is shortened by about 25% and the algorithm search time is shortened by about 55% compared with the improved A-star algorithm; while the unimproved LPA-star algorithm is unable to accomplish the dynamic path planning task. Therefore, the improved LPA-star algorithm can reduce the robot’s moving distance and time consumption.

Walking mechanisms offer advantages over wheels or tracks for locomotion but often require complex designs. This paper presents the kinematic design and analysis of a novel overconstrained spatial a single degree-of-freedom leg mechanism for walking robots. The mechanism is generated by combining spherical four-bar linkages into two interconnecting loops, resulting in an overconstrained design with compact scalability. Kinematic analysis is applied using recurrent unit vector methods. Dimensional synthesis is performed using the Firefly optimization algorithm to achieve a near-straight trajectory during the stance phase for efficient walking. Constraints for mobility, singularity avoidance, and transmission angle are also implemented. The optimized design solution is manufactured using 3D printing and experimentally tested. Results verify the kinematic properties including near-straight-line motion during stance. The velocity profile shows low perpendicular vibrations. Advantages of the mechanism include compact scalability allowing variable stride lengths, smooth motion from overconstraint, and simplicity of a single actuator. The proposed overconstrained topology provides an effective option for the leg design of walking robots and mechanisms.

A more intuitive appreciation of spatial compliant behavior can be obtained through analysis and description of the behavior in terms of its centers, specifically the center of stiffness, the center of compliance, and the center of elasticity. This paper investigates the properties of each of these centers. Necessary and sufficient conditions for the coincidence of these centers are identified. A physical appreciation of those compliant behaviors that have coincident centers is obtained in terms of restrictions on the geometry of topologically simple mechanisms that realize those behaviors. The results can be used in the design of compliant mechanisms for robotic manipulation, especially when the compliance is characterized by the location of its center.

The problem of how to effectively track and intercept small aircraft that break into the no-fly zones is now attracting increasing interest in robotics society. Vision-based control has been proved an effective solution to the target tracking problem for unmanned aerial vehicles (UAVs). Due to the limited field of view (FOV) of onboard vision sensors, existing works assume that the target is always detectable during tracking or limit the flight speed of the UAV in practice. In this paper, inspired by the broad FOV of camera network, we are the first to propose an eye-to-hand (i.e., fixed cameras) visual servoing scheme to track and intercept aerial targets by using UAVs and ground visual sensors. Specifically, utilizing rotation matrices, we first present a visual servoing equation to convert the UAV motion in image planes to the inertial frame. Then, an image-based visual servoing controller is designed directly based on image errors of camera nodes in the sensor network, and system stability is proved by means of Lyapunov analysis. Additionally, to achieve the desired translational velocity command, a low-level attitude controller is developed based on the UAV dynamics. Finally, a series of experiments in both simulated and real flight scenarios show the outstanding efficacy of our method.

Adaptation to the dynamic environment and variable task sequence is the critical ability for robot navigation and task execution. The Cyclic Networking Rapidly-exploring Random Tree (CNRRT) method is proposed to obtain the optimal path in real time and realize long-term path planning ability in a complex dynamic environment. The cyclic branch is introduced to the acyclic graph of Rapidly-exploring Random Tree (RRT) method, which forms a decentralized path network in the configuration space. An iterative searching strategy is built to search for the optimal path in the network. The branch prune, reconnection, and regrowth processes enable the decentralized network to efficiently respond to dynamic changes in the environment. The CNRRT can search for the real-time optimal path in the dynamic environment, dealing with the configuration and task changes robustly. Besides, the CNRRT is consistent for scenarios with long-term task sequence without significant performance fluctuation. Simulations and real-world comparative experiments verify the effectiveness of the proposed method.

In this paper, fractional-order (FO), intelligent, and robust sliding mode control (SMC) and stabilization of inherently nonlinear, multi-input, multi-output 6-DOF robot manipulators are investigated. To ensure robust control and better performance of the robot system, significant studies on various control transactions have been explored. First, a sliding proportional-integral-derivative (PID) surface is conceived and then its FO constitute is developed. It is an important fact that in SMC, the reaching phase is fast and the chattering is abated in the sliding phase. In particular, the discontinuity in the SMC is prevented in view of the boundary layer obtained by recommending the sigmoid function together with fuzzy logic to eliminate the chattering phenomenon. A hybrid tuning method consisting of gray wolf optimization and particle swarm optimization (GWO-PSO) algorithms is applied to tune the parameters of PID sliding mode control (PIDSMC), FO PIDSMC (FOPIDSMC), fuzzy PIDSMC (FPIDSMC), and FO fuzzy PIDSMC (FOFPIDSMC) controllers. In simulation results, the tuned FOFPIDSMC controller consistently outperforms PIDSMC, FOPIDSMC, and FPIDSMC controllers tuned by the GWO-PSO in dynamic performance, trajectory tracking, disturbance rejection, and mass uncertainty scenarios. It has been seen through a thorough performance analysis that 91.93% and 44.13% improvement are, respectively, obtained for mean absolute error (MAE) and torques root mean square (RMS) values of the joints when using from the PIDSMC to the FOFPIDSMC. Finally, the simulation outcomes reveal the superior aspects of the designed FOFPIDSMC and also demonstrate that the FOFPIDSMC controller enhances the dynamic performances of the 6-revolute universal robots 5 (6R UR5) robot manipulator under a variety of operating conditions.

A modeling method of multi-objective optimization design for parallel mechanisms (PMs) is proposed, whose implementation is illustrated with 2RPU-RPS mechanism as an example. The orientation of biased output axis on moving platform is depicted by spherical attitude angles, and its kinematic model is deduced through vector method. With screw theory as mathematic tool, a comprehensive evaluation method of kinematic performance for PM is established. On this basis, the expensive constrained multi-objective optimization model of dimensional parameters for the discussed mechanism is constructed. The NSDE-II algorithm, formed by replacing the genetic algorithm operators in non-dominated sorting genetic algorithm II (NSGA-II) with DE operators, is utilized to solve this multi-objective optimization problem, thus obtaining multiple Pareto optimal solutions with engineering application significance, which proves the feasibility and effectiveness of the proposed modeling method and algorithm. Moreover, the normalization coverage space and the minimum adjacent vector angle are proposed to evaluate the computational performance of NSDE-II. Finally, the potential engineering application value for the optimized 2RPU-RPS PM is presented.

This paper introduces two enhanced control approaches to improve the performance of parallel manipulators, addressing their inherent nonlinear dynamics and complex structure. The first approach results in a hybrid control system in joint space, integrating acceleration-based control, sliding mode, and disturbance observer techniques. The control system is designed to correct tracking errors and compensate for generalized disturbances, thus improving accuracy in tracking reference positions. The second approach merges the joint-space and task-space formulations, implementing proportional-derivative controllers in task space to manage the end-effector positions while maintaining safe operational configurations. The stability of the proposed controllers is demonstrated through Lyapunov analysis, while their performance is validated through comprehensive simulations and real-time experiments.

Continuous-time batch state estimation using Gaussian processes is an efficient approach to estimate the trajectories of robots over time. In the past, relatively simple physics-motivated priors have been considered for such approaches, using assumptions such as constant velocity or acceleration. This paper presents an approach to incorporating exogenous control inputs, such as velocity or acceleration commands, into the continuous Gaussian process state estimation framework. It is shown that this approach generalizes across different domains in robotics, making it applicable to both the estimation of continuous-time trajectories for mobile robots and the estimation of quasi-static continuum-robot shapes. Results show that incorporating control inputs leads to more informed priors, potentially requiring less measurements and estimation nodes to obtain accurate estimates. This makes the approach particularly useful in situations in which limited sensing is available. For example, in a mobile robot localization experiment with sparse landmark distance measurements and frequent odometry control inputs, our approach provides accurate trajectory estimates with root-mean-square errors around 3-4 cm and 4-5 degrees, even with time intervals up to five seconds between discrete estimation nodes, which significantly reduces computation time.

Motion primitives play an important role in motion planning for autonomous vehicles, as they effectively address the sampling challenges inherent in nonholonomic motion planning. Employing motion primitives (MPs) is a widely accepted approach in nonholonomic motion planning based on sampling. This study specifically addresses the problem of learning from human-driving data to create human-like trajectories from predefined start-to-end states, which then serve as MP within the sampling-based nonholonomic motion planning framework. In this paper, we propose a deep learning-based method for generating MP that capture human-driving trajectory data features. By processing human-driving trajectory data, we create a Motion Primitive dataset that uniformly covers typical urban driving scenarios. Based on this dataset, a vehicle model long short-term memory neural network model is constructed to learn the features of the human-driving trajectory data. Finally, a framework for the generation of MP for practical applications is given based on this neural network. Our experiments, which focus on the dataset, the MMP generation network, and the generation process, demonstrate that our method significantly improves the training efficacy of the MP generation network. Additionally, the MP generated by our method exhibit higher accuracy compared to traditional methods.

The safety of human-collaborative operations with robots depends on monitoring the external torque of the robot, in which there are toque sensor-based and torque sensor-free methods. Economically, the classic method for estimating joint external torque is the first-order momentum observer (MOB) based on a physic model without torque sensors. However, uncertainties in the dynamic model, which encompasses parameters identification error and joint friction, affect the torque estimation accuracy. To address this issue, this paper proposes using the backpropagation neural network (BPNN) method to estimate joint external torque without the delicate physical model by utilizing the powerful machine learning ability to handle the uncertainties of the MOB method and improve the accuracy of torque estimation. Using data obtained from the torque sensor to train the BPNN to build up a digital torque model, the trained BPNN can perceive force in practical applications without relying on the torque sensor. In the end, by contrast to the classic first-order MOB, the result demonstrates that BPNN achieves higher estimation accuracy compared to the MOB.

A transverse ledge climbing robot inspired by athletic locomotion is a customized robot aiming to travel through horizontal ledges in vertical walls. Due to the safety issue and complex configurations in graspable ledges such as horizontal, inclined ledges, and gaps between ledges, existing well-known vision-based navigation methods suffering from occlusion problems may not be applicable to this special kind of application. This study develops a force feedback-based motion planning strategy for the robot to explore and make feasible grasping actions as it continuously travels through reachable ledges. A contact force detection algorithm developed using a momentum observer approach is implemented to estimate the contact force between the robot’s exploring hand and the ledge. Then, to minimize the detection errors due to dynamic model uncertainties and noises, a time-varying threshold is integrated. When the estimated contact force exceeds the threshold value, the robot control system feeds the estimated force into the admittance controller to revise the joint motion trajectories for a smooth transition. To handle the scenario of gaps between ledges, several ledge-searching algorithms are developed to allow the robot to grasp the next target ledge and safely overcome the gap transition. The effectiveness of the proposed motion planning and searching strategy has been justified by simulation, where the four-link transverse climbing robot successfully navigates through a set of obstacle scenarios modeled to approximate the actual environment. The performance of the developed grasping ledge searching methods for various obstacle characteristics has been evaluated.

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

The two main sources of difficulty for a group of mobile robots employing sensors to find a source are robot collisions and wireless ambient noise, such as light, sound, and other sounds. This paper introduces a novel approach to multi-robot system cooperation and collision avoidance: the new modified source-seeking control with noise cancelation technology. The robot team works together on an incline of a light source field; the team’s mobility is dependent upon following the upward gradient’s direction and forming a particular movement pattern. The proposed program also takes into account each robot’s size, speed limit, obstacles, and noise. The noise cancelation technique has been used to avoid the delay and false decisions to find the target point of the source. When the noise is canceled, all control inputs to the algorithm are accurate, and the feedback decision will be true. In this study, we use the MATLAB simulation tools to test the velocity, position, time delay, and performance of each robot in the used group of robots. The simulation and practical results of the robots in searching for a light source showed very satisfactory performance compared with the results in the literature.

A method is proposed for identifying robot gravity and friction torques based on joint currents. The minimum gravity term parameters are obtained using the Modified Denavit–Hartenberg (MDH) parameters, and the dynamic equations are linearized. The robot’s friction torque is identified using the Stribeck friction model. Additionally, a zero-force drag algorithm is designed to address the issue of excessive start-up torque during dragging. A sinusoidal compensation algorithm is proposed to perform periodic friction compensation for each stationary joint, utilizing the identified maximum static friction torque. Experimental results show that when the robot operates at a uniform low speed, the theoretical current calculated based on the identified gravity and friction fits the actual current well, with a maximum root mean square error within 50 mA, confirming the accuracy of the identification results. The start-up torque compensation algorithm reduces the robot’s start-up torque by an average of $ 60.58\mathrm{\%}$, improving the compliance of the dragging process and demonstrating the effectiveness of the compensation algorithm.

Pouch-type actuators have recently garnered significant interest and are increasingly utilized in diverse fields, including soft wearable robotics and prosthetics. This is largely due to their lightweight, high output force, and low cost. However, the inherent hysteresis behavior markedly affects the stability and force control of pouch-type driven systems. This study proposes a modified generalized Prandtl–Ishlinskii (MGPI) model, which includes generalized play operators, the tangent envelope function, and one-sided dead-zone operators, to describe the asymmetric and non-convex hysteresis characteristics of pouch-type actuators. Compared to a classical Prandtl–Ishlinskii (PI) model incorporating one-sided dead-zone functions, the MGPI model exhibits smaller relative errors at six different air pressures, demonstrating its capability to accurately describe asymmetric and non-convex hysteresis curves. Subsequently, the MGPI hysteresis model is integrated with displacement sensing technology to establish a load compensation control system for maintaining human posture. Four healthy subjects are recruited to conduct a 1 kg load compensation test, achieving efficiencies of 85.84%, 84.92%, 83.63%, and 68.86%, respectively.

Aiming at the problems of many path inflection points, unsmooth paths, and poor local obstacle avoidance in path planning of inspection robots in static-dynamic scenes under complex geological conditions in coal mine roadways, a hybrid path planning method based on the improved A* algorithm and dynamic window approach (DWA) algorithm is proposed. First, the inspection robot platform and system model are constructed. An improved heuristic function that incorporates target weight information is proposed based on the A* global path planning algorithm. Additionally, redundant nodes are eliminated, and the path is smoothed using the Floyd algorithm and B-spline curves. Second, the global path planning A* algorithm and the local path planning DWA algorithm are fused. The dynamic path planning is carried out by setting the key node information of the global path extracted from the improved A* algorithm as the local target point of the DWA algorithm. On this basis, a grid map is established to simulate and analyze the proposed path planning algorithm. Finally, the autonomous path planning and walking experiment of inspection robot in simulated roadway environment are carried out. The results show that the hybrid path planning method based on improved A* algorithm and DWA algorithm proposed in this paper is more efficient and safer, which can meet the motion requirements of inspection robot in coal mine roadway.

This paper introduces a novel bipedal robot model designed for adaptive transition between walking and running gaits solely through changes in locomotion speed. The bipedal robot model comprises two sub-components: a mechanical model for the legs that accommodates both walking and running and a continuous state model that does not explicitly switch states. The mechanical model employs a structure combining a linear cylinder with springs, dampers, and stoppers, designed to have mechanistic properties of both the inverted pendulum model used for walking and the spring-loaded inverted pendulum model used for running. The state model utilizes a virtual leg representation to abstractly describe the actual support leg, capable of commonly representing both a double support leg in walking and a single support leg in running. These models enable a simple gait controller to determine the kick force and the foot touchdown point based solely on the parameter of the target speed, thus allowing a robot to walk and run stably. Hence, simulation validation demonstrates the adaptive robot transition to an energy-efficient gait depending on locomotion speed without explicit gait-type instructions and maintaining stable locomotion across a wide range of speeds.