Search Results (98)

The Rapidly-exploring Random Tree Star (RRT*) algorithm is widely applied in robotic manipulator path planning, yet it does not directly consider motion control, where abrupt changes may cause shocks and vibrations, reducing accuracy and stability. To overcome this limitation, this paper proposes the Kinematically Smoothed, dynamically Biased Bidirectional Potential-guided RRT* (KSBB-P-RRT*) algorithm, which unifies path planning and motion control and introduces three main innovations. First, a fast path search strategy on the basis of Bi-RRT* integrates adaptive sampling and steering to accelerate exploration and improve efficiency. Second, a triangle-inequality-based optimization reduces redundant waypoints and lowers path cost. Third, a kinematically constrained smoothing strategy adapts a Jerk-Continuous S-Curve scheme to generate smooth and executable trajectories, thereby integrating path planning with motion control. Simulations in four environments show that KSBB-P-RRT* achieves at least 30% reduction in planning time and at least 3% reduction in path cost, while also requiring fewer iterations compared with Bi-RRT*, confirming its effectiveness and suitability for complex and precision-demanding applications such as agricultural robotics. Full article

Robots with multiple degrees of freedom (DOFs), such as underwater vehicle–manipulator systems (UVMSs), are expected to optimize system performance by exploiting redundancy with various basic tasks while still fulfilling the primary objective. Multiple tasks for robots, which are expected to be carried out simultaneously with prescribed priorities, can be referred to as sets of tasks (SOTs). In this work, a hybrid vision/force control method with continuous task transitions is proposed for a UVMS to simultaneously track the reference vision and force trajectory during manipulation. Several tasks with expected objectives and specific priorities are established and combined as SOTs in hybrid vision/force tracking. At different stages, various SOTs are carried out with different emphases. A hierarchical optimization-based whole-body control framework is constructed to obtain the solution in a strictly hierarchical fashion. A continuous transition method is employed to mitigate oscillations during the task switching phase. Finally, comparative simulation experiments are conducted and the results verify the improved convergence of the proposed tracking controller for UVMSs. Full article

In Aerial Manipulation, the motion of the robotic arm can cause unwanted movements of the flying base affecting the trajectory tracking capability. A possible solution to reduce these disturbances is to use a free revolute joint between the flying base and the manipulator, thus reducing the torque applied to the base from the manipulator. In this paper, a novel approach to solve the inverse kinematics of an aerial manipulator with a free revolute joint is presented. The approach exploits the Generalized Jacobian to deal with the presence of a mobile base, and the dynamics of the system is considered to predict the motion of the non-actuated joint; external forces acting on the system are also included. The method is implemented in MATLAB for a planar case considering the parameters of a real manipulator attached to a real octocopter. The tracking of a trajectory with the end-effector and a load picking task are simulated for a non-redundant and for a redundant manipulator. Simulation results demonstrate the capability of this approach in following the desired trajectories and reducing rotation and horizontal translation of the base. Full article

This paper explores the application of Deep Neural Networks (DNNs) to solve the Inverse Kinematics (IK) problem in robotic manipulators. The IK problem, crucial for ensuring precision in robotic movements, involves determining joint configurations for a manipulator to reach a desired position or orientation. Traditional methods, such as analytical and numerical approaches, have limitations, especially for redundant manipulators, or involve high computational costs. Recent advances in machine learning, particularly with DNNs, have shown promising results and seem fit for addressing these challenges. This study investigates several DNN architectures, namely Feed-Forward Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), for solving the IK problem, using the TIAGo robotic arm with seven Degrees of Freedom (DOFs). Different training datasets, normalization techniques, and orientation representations are tested, and custom metrics are introduced to evaluate position and orientation errors. The performance of these models is compared, with a focus on curriculum learning to optimize training. The results demonstrate the potential of DNNs to efficiently solve the IK problem while avoiding issues such as singularities, competing with traditional methods in precision and speed. Full article

This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framework with a proactive pose optimization mechanism. The robotic system, comprising a mobile base and a manipulator arm, compensates for uncertain human behaviors and internal actuation noise through a two-step iterative process. At each planning horizon, a candidate set of feasible joint configurations is generated using a Conditional Variational Autoencoder (CVAE). From this set, one configuration is selected by minimizing an estimated control cost computed via a disturbance-aware Discrete Algebraic Riccati Equation (DARE), which also provides the optimal control inputs for both the mobile base and the manipulator arm. We derive the disturbance-aware DARE and validate DARC with simulated experiments with a Fetch robot. Evaluations across various trajectories and disturbance levels demonstrate that our proposed DARC framework outperforms baseline algorithms that lack disturbance modeling, pose optimization, or both. Full article

The use of multi-joint arms in a high-spindle environment can solve complex problems, but the singularity problem of the manipulator related to the structure of the serial manipulator is prominent. Therefore, based on the general mathematical model of fruit spatial distribution in high-spindle apple orchards, this study proposes two harvesting system architecture schemes that can meet the constraints of fruit spatial distribution and reduce the singularity of harvesting robot operation, which are four-arm dual-module independent moving scheme (Scheme A) and four-arm single-module parallel moving scheme (Scheme B). Based on the link-joint method, the analytical expression of the singular configuration of the redundant degree of freedom arm group system under the two schemes is obtained. Then, the inverse kinematics solution method of the redundant arm group and the singularity avoidance picking trajectory planning strategy are proposed to realize the judgment and solution of the singular configuration in the complex working environment of the high-spindle. The singularity rate of Scheme A in the simulation environment is 17.098%, and the singularity rate of Scheme B is only 6.74%. In the field experiment, the singularity rate of Scheme A is 26.18%, while the singularity rate of Scheme B is 13.22%. The success rate of Schemes A and B are 80.49% and 72.33%, respectively. Through experimental comparison and analysis, Scheme B is more prominent in solving singular problems but still needs to improve the success rate in future research. This paper can provide a reference for solving the singular problems in the complex working environment of high spindles. Full article

This paper presents a secure control framework enhanced with an event-triggered mechanism to ensure resilient and resource-efficient operation under false data injection (FDI) attacks on sensor measurements. The proposed method integrates a Kalman filter and a neural network (NN) to construct a hybrid observer capable of detecting and compensating for malicious anomalies in sensor measurements in real time. Lyapunov-based update laws are developed for the neural network weights to ensure closed-loop system stability. To efficiently manage system resources and minimize unnecessary control actions, an event-triggered control (ETC) strategy is incorporated, updating the control input only when a predefined triggering condition is violated. A Lyapunov-based stability analysis is conducted, and linear matrix inequality (LMI) conditions are formulated to guarantee the boundedness of estimation and system errors, as well as to determine the triggering threshold used in the event-triggered mechanism. Simulation studies on a two-degree-of-freedom (2-DOF) robot manipulator validate the effectiveness of the proposed scheme in mitigating various FDI attack scenarios while reducing control redundancy and computational overhead. The results demonstrate the framework’s suitability for secure and resource-aware control in safety-critical applications. Full article

Future space missions will include multi-robot systems, with greater autonomy and a large degree of heterogeneity for a wider range of task capabilities and redundancy. It is imperative that both software (learning models, parallelizing capabilities, resource distribution, etc.) and hardware factors must be considered during decentralized task negotiation to lead to better performance of the team. By utilizing the formalism of contextual Markov decision processes, team composition can be incorporated into the learning process and used for more meaningful and reliable evaluation using measures such as total time, overall consumed energy, performance feedback from tasks, or damage incurred. Improved team performance will in turn enhance the overall results of the mission. Planetary exploration tasks often involve time, communication and energy constraints. Such missions are also prone to noisy sensor data (e.g., camera images distorted by dust), as well as wear and tear on hardware (e.g., wheels, manipulators). To ensure that such factors do not jeopardize the mission, they must be taken into account. Therefore, this paper describes a software framework for the reliable execution of tasks in constrained and dynamic environments. Our work leverages the advantages of heterogeneity for more resilient planetary missions by addressing two aspects—first, the integration of hardware parameters into the negotiation process, and second the analysis of how the integration of team performance metrics, particularly adaptability and mutual support, in task negotiation plays a role in the overall mission success. Full article

Task prioritization for inverse kinematics can be a powerful tool for realizing objectives in robot manipulation. This is particularly true for robots with redundant degrees of freedom, but it can also help address a debilitating singularity in six-axis robots. A roll-pitch-roll wrist is especially problematic for any six-axis robot because it produces a “gimbal-lock” singularity in the middle of the wrist workspace when the roll axes align. A task priority methodology can be used to realize only the achievable components of the commanded motion in the reduced operational space of a manipulator near singularities while phasing out the uncontrollable direction. In addition, this approach allows the operator to prioritize translation and rotation in the region of singularities. This methodology overcomes a significant drawback to the damped least-squares method, which can produce tool motion that deviates significantly from the desired path even in directions that are controllable. The approach used here reduces the operational space near the wrist singularity while maintaining full command authority over tool translation. The methodology is demonstrated in simulations conducted on a six degree-of-freedom Motoman MH250 manipulator. Full article

This paper presents an obstacle avoidance strategy for mobile manipulators consisting of a robotic arm and a base with a non-holonomic differential wheel system. The algorithm makes it possible to avoid obstacles in a dynamic environment, without planning the path a priori. A series of examples are proposed in simulation using Matlab and analyzed to show how the algorithm works if the obstacle interferes with the manipulator or the base. In addition, the possibility of prioritizing the movement of certain parts of the system using the weighted pseudo-inverse matrix is introduced. In this way, it is possible to give movement priority to the base if it is necessary to move the robot over long distances while keeping the manipulator as still as possible. The use of null space to keep the end-effector stationary while it avoids obstacles is also explored, exploiting the system’s redundancy and allowing the rest of the kinematic chain and the mobile base to move accordingly. Finally, current standards are analyzed and a solution is shown that allows the robot to vary its behavior to avoid obstacles depending on the distance to the target point. Full article

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation. Full article

Ensuring repeatability in robotic manipulators is critical for industrial applications, particularly in cyclic tasks where precision and consistency are required. This study aims to establish the conditions that are necessary for repeatability in data-driven task-space control, compare repeatability across redundant robotic manipulators, and reduce the computational costs associated with data-based control methods compared to traditional model-based approaches. To achieve this, a large number of cyclic actions are simulated, mimicking real-world industrial routines. The methodology evaluates the estimated Jacobian matrix’s manipulability and its role in facilitating effective task execution within the operational space of the robot. The results demonstrate that the proposed approach achieves consistent repeatability at the full pose level of the end-effector, integrating both position and orientation. In particular, the findings indicate that the proposed controller minimizes variability and ensures reliable motion execution, even in the presence of system redundancies, as observed in the eight-DoF KUKA YouBot manipulator. These insights contribute to advancing data-driven control strategies for redundant robotic systems, enhancing their applicability in industrial settings. Full article

Finding realistic motions for redundant manipulators is essential for complex jobs such as home care and industrial assembly. Motion planning is complex when a task requires standing upright or moving through restricted spaces. This work provides an effective motion-planning strategy for 7-DOF manipulators that improves connections via redundancy. The analytic Cartesian-space-to-joint-space kinematic mapping models for 7-DOF redundant manipulators with diverse configurations are constructed first, and the feasible nodes are determined by sampling the Cartesian space without barriers to satisfy the task requirements. Each Cartesian-space sampling node can provide numerous feasible joint-space nodes because of the redundancy of the robot manipulators. To remove additional valid nodes from a singular position, joint configurations with the same end-effector position orientation are modified iteratively. Finally, we find the nearest nodes in the joint-space constraint manifold and build collision-free smooth pathways. The task constraint levels were varied for a 7-DOF manipulator in simulations and experiments. The proposed planner finds more viable nodes at the same end-position attitude than one-to-one projection. It does not require numerical iterations and achieves high planning efficiency and a high motion-planning success rate. Full article

Inspired by the characteristics of living organisms with soft bodies and flexibility, continuum robots, which bend their robotic bodies and adapt to different shapes, have been widely introduced. Such robots can be used as manipulators to handle objects by wrapping themselves around them, and they are expected to have high grasping performance. However, their infinite degrees of freedom and soft structure make modeling and controlling difficult. In this study, we develop a tendon-driven continuum robot system with color-based posture sensing. The robot is driven by dividing the continuum body into two parts, enabling it to grasp objects by flexible motions. For posture sensing, each joint is painted in a different color, and the 3D coordinates of each joint are detected by a stereo camera for estimating the 3D shape of the robotic body. By taking a video of the robot in actuation and using image processing to detect joint positions, we succeeded in obtaining the posture of the entire robot in experiments. We also robustly demonstrate the grasping manipulation of an object using the redundant structure of the continuum body. Full article

This review investigates the latest advancements in Multi-Robot Systems (MRSs) and soft robotics, with a particular focus on their integration and emerging opportunities. An MRS extends principles from distributed artificial intelligence and coordination frameworks, enabling efficient collaboration in robotic applications such as object manipulation, navigation, and transportation. Soft robotics employs flexible materials and biomimetic designs to improve adaptability in unstructured environments, with applications in manufacturing, sensing, actuation, and modeling. Unlike previous reviews, which often address these fields independently, this work emphasizes their integration, identifying key challenges such as nonlinear dynamics, hyper-redundant configurations, and adaptive control. This review discusses recent advancements in locomotion, coordination, and simulation, offering insights into the development of adaptive and collaborative robotic systems across diverse applications. Full article