Search Results (14)

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

Generating robot behaviors in dynamic real-world situations generally requires the programming of multiple, often redundant degrees of freedom to meet multiple goals governing the desired motions. In this work, we propose a constraint-based controller specification methodology. A novel declarative language is used to combine semantically specialized building blocks into composite controllers. This description is automatically transformed at runtime into an executable form, which can automatically leverage multiple threads to parallelize computations whenever possible. Enabling runtime definition of controller topologies out of declarative descriptions not only reduces the work required to develop such controllers, but it also allows one to dynamically synthesize new controllers based on higher-level task planners or by user interaction through Graphical User Interfaces (GUIs). Our solution adds new functionality to the Robot Operating System (ROS)/ros_control ecosystem, where robot behaviors are typically achieved by deploying single-objective, off-the-shelf controllers for tasks like following joint trajectories, executing interpolated point-to-point motions in Cartesian space, or for basic compliant behaviors. Our proposed constraint-based framework enhances ros_control by providing the means to easily construct composite controllers from existing primary elements using our design language. Building on top of the ros_control infrastructure facilitates the usage of our controller with a wide range of supported robots and enables quick integration with the existing ROS ecosystem. Full article

Aiming at the challenge of trajectory planning for a continuum manipulator in the confined spaces of gas-insulated switchgear (GIS) chambers during intelligent operation and maintenance of power equipment, this paper proposes a configuration space (C-space) obstacle-avoidance planning method based on an improved RRT-Connect algorithm. By constructing a virtual joint-space obstacle map, the collision-avoidance problem in Cartesian space is transformed into a joint-space path search problem, significantly reducing the computational burden of frequent inverse kinematics solutions inherent in traditional methods. Compared to the RRT-Connect algorithm, improvements in node expansion strategies and greedy optimization mechanisms effectively minimize redundant nodes and enhance path generation efficiency: the number of iterations is reduced by 68% and convergence speed is improved by 35%. Combined with polynomial-driven trajectory planning, the method successfully resolves and smoothens driving cable length variations, achieving efficient and stable control for both the end-effector and arm configuration of a dual-segment continuum manipulator. Simulation and experimental results demonstrate that the proposed algorithm rapidly generates collision-free arm configuration trajectories with high trajectory coincidence in typical GIS chamber scenarios, verifying its comprehensive advantages in real-time performance, safety, and motion smoothness. This work provides theoretical support for the application of continuum manipulator in precision operation and maintenance of power equipment. Full article

With the continuous development of modern robotics technology, in order to overcome the obstacles to the ability to complete tasks due to the fixed structure of the robot itself, to realize the reconfigurable purpose of the manipulator, it can be assembled into different degrees of freedom or configurations according to the needs of different tasks, which has the characteristics of a compact structure, high integrability, and low cost. The overall design scheme of a cable-free modular reconfigurable manipulator is proposed, and based on the target design parameters, the structural design of each module is completed, and the module library is constructed. Each module realizes rapid assembly or disassembly through a new type of docking mechanism module, which improves the flexibility and reliability of the manipulator. Meanwhile, a finite element analysis is carried out on the whole manipulator to optimize the structure that does not meet the strength and stiffness requirements. The wireless energy transmission module is integrated into the joint module to realize the cable-free design of the manipulator in the structure. The kinematic models of each module are established separately, providing a method to quickly construct the kinematics of different configurations of the manipulator, and the dexterity of the workspace is analyzed. Then, two methods, joint space planning and Cartesian space planning, are adopted to generate the corresponding motion paths and kinematic curves, which successfully verifies the reasonableness of the kinematics of the designed manipulator. Finally, combined with the results of the dynamics simulation, the corresponding dynamics curves of the end of each joint are generated to further verify the reliability of its design. It provides a new way of thinking for the research and development of highly intelligent and highly integrated manipulators. Full article

In robotic surface UV printing, the irregular shape of the workpiece and frequent curvature changes require the printing robot to maintain the nozzle’s perpendicular orientation to the surface during path planning, which imposes high demands on trajectory accuracy and path smoothness. To address this challenge, this paper proposes a reinforcement-learning-based path planning method. First, an ideal main path is defined based on the nozzle characteristics, and then a robot motion accuracy model is established and transformed into a Markov Decision Process (MDP) to improve path accuracy and smoothness. Next, a framework combining Generative Adversarial Imitation Learning (GAIL) and Soft Actor–Critic (SAC) methods is proposed to solve the MDP problem and accelerate the convergence of SAC training. Experimental results show that the proposed method outperforms traditional path planning methods, as well as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Specifically, the maximum Cartesian space error in path accuracy is reduced from 1.89 mm with PSO and 2.29 mm with GA to 0.63 mm. In terms of joint space smoothness, the reinforcement learning method achieves the smallest standard deviation, especially with a standard deviation of 0.00795 for joint 2, significantly lower than 0.58 with PSO and 0.729 with GA. Moreover, the proposed method also demonstrates superior training speed compared to the baseline SAC algorithm. The experimental results validate the application potential of this method in intelligent manufacturing, particularly in industries such as automotive manufacturing, aerospace, and medical devices, with significant practical value. Full article

Delta parallel robots have been widely used in precision processing, handling, sorting, and the assembly of parts, and their high efficiency and motion stability are important indexes of their performance.Corners created by small line segments in trajectory planning cause abrupt changes in a tangential discontinuous trajectory, and the vibration and shock caused by such changes seriously affect the robot’s high-speed and high-precision performance. In this study, a trajectory-planning method combining Cartesian space and joint space is proposed. Firstly, the vector method and microelement integration method were used to establish the complete kinematic and dynamic equations of a delta parallel robot, and an inverse kinematic/dynamic model-solving program was written based on the MATLAB software R2020a. Secondly, the end-effector trajectory of the delta parallel robot was planned in Cartesian space, and the data points and inverse control points of the end effector’s trajectory were obtained using the normalization method. Finally, the data points and control points were mapped to the joint space through the inverse kinematic equation, and the fifth-order B-spline curve was adopted for quadratic trajectory planning, which allowed the high-order continuous smoothing of the trajectory planning to be realized. The simulated and experimental results showed that the trajectory-smoothing performance in continuous high-order curvature changes could be improved with the proposed method. The peak trajectory tracking error was reduced by $10.53\%$, $41.18\%$, and $44.44\%$, respectively, and the peak torque change of the three joints was reduced by $3.5\%$, $11.6\%$, and $1.6\%$, respectively. Full article

For autonomous legged robots to be deployed in practical scenarios, they need to perform perception, motion planning, and locomotion control. Since robots have limited computing capabilities, it is important to realize locomotion control with simple controllers that have modest calculations. The goal of this paper is to create computational simple controllers for locomotion control that can free up computational resources for more demanding computational tasks, such as perception and motion planning. The controller consists of a leg scheduler for sequencing a trot gait with a fixed step time; a reference trajectory generator for the feet in the Cartesian space, which is then mapped to the joint space using an analytical inverse; and a joint controller using a combination of feedforward torques based on static equilibrium and feedback torque. The resulting controller enables velocity command following in the forward, sideways, and turning directions. With these three velocity command following-modes, a waypoint tracking controller is developed that can track a curve in global coordinates using feedback linearization. The command following and waypoint tracking controllers are demonstrated in simulation and on hardware. Full article

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels. Full article

The hybrid kinematic mechanism (HKM) as an alternative remote handling subsystem of the Demonstration Fusion Power Plant (DEMO) breeding blanket (BB) is undergoing extensive theoretical analysis and feasibility verification. In this paper, the forward and inverse kinematic models of the HKM are derived by combining the Newtonian iterative method and the analytical method. Cartesian space trajectory planning is designed based on the trajectories of the HKM lifting of inboard and outboard BBs. The continuous smooth inverse kinematic solutions in the HKM joint space are obtained based on the polynomial interpolation method. For the characteristics of the HKM piston thread driving, the end-effector position error caused by the degradation of the spherical joint into a universal joint is analyzed and calculated. During the lifting of the left inboard BB, there is a maximum absolute error ∆P = 3.1 mm, and as the error continues to expand to the bottom of the BB it causes a risk of collision. Combining the overall effects of driving control, rigid–flexible coupling, etc., on position accuracy, an open-loop variable parameter error compensation plan based on the Levenberg–Marquardt (LM) nonlinear damping least-squares algorithm is proposed and validated in this paper. The simulation results show that the maximum absolute error after compensation is less than 1 mm as the mesh density increases, and the absolute position accuracy can be further improved by local mesh encryption. This study verifies the feasibility of the HKM as a BB remote handling subsystem and provides an option for high-precision control of the HKM. Full article

This paper presents a universal approach to shaping the mechanical properties of the interaction between a collaborative robot and its environment through an end-effector Cartesian compliance shaping. More specifically, the focus is on the class of kinematically redundant robots, for which a novel redundancy reconfiguration scheme for online optimization of the Cartesian compliance of the end-effector is presented. The null-space reconfiguration aims to enable the more efficient and versatile use of collaborative robots, including robots with passive compliant joints. The proposed approach is model-based and gradient-based to enable real-time computation and reconfiguration of the robot for Cartesian compliance while ensuring accurate position tracking. The optimization algorithm combines two coordinate frames: the global (world) coordinate frame commonly used for end-effector trajectory tracking; and the coordinate frame fixed to the end-effector in which optimization is computed. Another attractive feature of the approach is the bound on the magnitude of the interaction force in contact tasks. The results are validated on a torque-controlled 7-DOF KUKA LWR robot emulating joint compliance in a quasi-static experiment (the robot exerts a force on an external object) and a peg-in-hole experiment emulating an assembly task. Full article

This paper presents the reverse priority impedance control of manipulators with reference to redundant robots of a given task. The reverse priority kinematic control of redundant manipulators is first expressed in detail. The motion in the joint space is derived following the opposite order compared with the classical task priority–based solution. Then the Cartesian impedance control is combined with the reverse priority impedance control to solve the reverse hierarchical impedance controlled, so that the Cartesian impedance behavior can be divided into the primary priority impedance control and the secondary priority impedance control. Furthermore, the secondary impedance control task will not disturb the primary impedance control task. The motion in the joint space is affected following the opposite order and working in the corresponding projection operators. The primary impedance control tasks are implemented at the end, so as to avoid the possible deformations caused by the singularities occurring in the secondary impedance control tasks. Hence, the proposed reverse priority impedance control of manipulator can achieve the desired impedance control tasks with proper hierarchy. In this paper, the simulation experiments of the manipulator will verify the proposed reverse priority control algorithm. Full article

Hyper-redundant manipulators with multiple degrees of freedom have special application prospects in narrow spaces, such as detection in small spaces in aerospace, rescue on-site disaster relief, etc. In order to solve the problems of complex obstacle avoidance planning and inverse solution selection of a hyper-redundant robot in a narrow space, a cubic B-spline curve based on collision-free trajectory using environmental edge information is planned. Firstly, a hyper-redundant robot composed of four pairs of double UCR (Universal-Cylindrical-Revolute) parallel mechanisms (2R1T, 2 Rotational DOFs and 1 Translation DOF) in series to realize flexible obstacle avoidance motion in narrow space is designed. The trajectory point envelope of a single UCR and the workspace of a single pair of UCR in Cartesian space based on the motion constraint boundaries of each joint are obtained. Then, the constraint control points according to the edge information of the obstacle are obtained, and the obstacle avoidance trajectory in the constrained space is planned by combining the A* algorithm and cubic B-spline algorithm. Finally, a variety of test scenarios are built to verify the obstacle avoidance planning algorithm. The results show that the proposed algorithm reduces the computational complexity of the obstacle avoidance process and enables the robot to complete flexible obstacle avoidance movement in the complex narrow space. Full article

In this paper, the real-time joint stiffness configuration strategy of a series parallel hybrid 7-DOF (degree of freedom) humanoid manipulator with flexible joints in continuous motion is studied. Firstly, considering the potential human robot accidental collision, combined with the manipulator safety index (MSI) and human body injury thresholds, the motion speed and joint stiffness of the robot are optimized in advance. Secondly, using hyperbolic tangent function for reference, the relationship between joint torques and passive joint deflection angles of the robot is given, which is beneficial for the real-time calculation of joint stiffness and obtain reasonable joint stiffness. Then, the structural model of the selected humanoid manipulator is described, on this basis, the relationship between the joint space stiffness and the Cartesian space stiffness of the humanoid manipulator is analyzed through Jacobian matrix, and the results show that the posture and joint space stiffness of the humanoid manipulator directly affect the Cartesian space stiffness of the humanoid manipulator. Finally, according to whether the humanoid manipulator works in the human-robot interaction environment, the real-time joint stiffness configuration of the humanoid manipulator in continuous motion is simulated and analyzed. The research shows that the humanoid manipulator with flexible joints can adjust the joint stiffness in real-time during continuous motion, and the joint stiffness configuration strategy can effectively improve the safety of human body in human-robot collision. In addition, in application, when the joint space stiffness of the robot is lower, the position accuracy can be improved by trajectory compensation. Full article

This paper presents a novel method for point-to-point path control for a hydraulic knuckle boom crane. The developed path control algorithm differs from previous solutions by operating in the actuator space instead of the joint space or Cartesian space of the crane. By operating in actuator space, almost all the parameters and constraints of the system become either linear or constant, which greatly reduces the complexity of both the control algorithm and path generator. For a given starting point and endpoint, the motion for each actuator is minimized compared to other methods. This ensures that any change in direction of motion is avoided, thereby greatly minimizing fatigue, jerky motion, and energy consumption. However, where other methods may move the tool-point in a straight line from start to end, the method in actuator space will not. In addition, when working in actuator space in combination with pressure-compensated control valves, there is no need for linearization of the system or feedback linearization due to the linear relationship between the control signal and the actuator velocities. The proposed solution has been tested on a physical system and shows good setpoint tracking and minimal oscillations. Full article