Manipulator Path Research Articles

A new cubic trigonometric Nu-Spline with shape control parameter and its applications

This study introduces a novel Cubic Trigonometric Nu-Spline (CTNS) with a shape parameter tailored for curve designing, ensuring geometric continuity of order 2. The CTNS possesses fundamental geometric properties such as convex hull, partition of unity, affine invariance, and variation diminishing, which are thoroughly discussed herein. Notably, our spline technique is enriched with Nu-Spline constraints, offering compelling local and global shape control capabilities. These capabilities encompass point tension, interval, or global tensions, enhancing versatility across various shape impacts. Beyond its curve designing prowess, the proposed technique exhibits commendable precision in estimating control points. Furthermore, its utility extends to diverse fields including electronics, medical image interpolation, manipulator path planning, and discrete time signal processing. Through numerical experimentation, we demonstrate the simplicity of implementing the CTNS algorithm alongside its superior accuracy compared to existing techniques.

Precision manipulation by an optically induced dielectrophoresis system based on an improved A-star algorithm

Cell manipulation using optically induced dielectrophoresis (ODEP) in a microfluidic system has drawn much attention due to its simplicity and being damage-free at the cellular level. Additionally, to improve its manipulation efficiency and accuracy, automatic manipulation methods have been applied in the ODEP system. However, the current automatic manipulation methods of ODEP rarely consider the impact of non-manipulated targets on cell manipulation, thereby reducing the operating efficiency and accuracy. Here, we propose a new, to our knowledge, automatic manipulation method of ODEP based on a path planning algorithm of the improved A-star. First, the maximum influence range of ODEP force generated by the virtual electrode was investigated by a numerical simulation, and it was also taken as the limit to expand the scope of the infeasible region in path planning to avoid the impact of the non-operational target on manipulation accuracy. Then, an improved A-star algorithm with target range constraints was proposed to optimize the manipulation path and improve the operation efficiency. Finally, experiments on cell separation were also carried out to validate the feasibility of the proposed automatic manipulation method. This work provides an automated method to improve the accuracy of ODEP manipulation, which is of great significance to further promote the application of ODEP in cell manipulation.

Improving the Performance of Cooling and Entanglement in a Double Cavity Optomechanical System Assisted by the Quantum Coherent Feedback

AbstractA theoretical scheme is proposed for improving the performance of cooling and entanglement, where the physical model is based on a double cavity optomechanical system assisted by the field‐mediated coherent feedback. The cooling performance is evaluated by calculating the final mean phonon number. The steady‐state bipartite entanglement between the optical mode and mechanical mode is measured by the logarithmic negativity. The result manifests that with assistance of the quantum coherent feedback, the mechanical resonator (MR) can be cooled close to its quantum ground state and the steady‐state optomechanical entanglement is simultaneously created, all of which are obtained under the condition beyond the resolved sideband. The presented feedback strategy is measurement‐independent, which can effectively preserve the quantum coherence of system. The scheme is conducive to relaxing the current experimental condition and it may provide a new path for the optomechanical manipulation involving the low‐frequency MR.

3D-AMM: a 3D artificial moment method for path planning of manipulator in multiple obstacles scenario

PurposeIn response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.Design/methodology/approachA new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.FindingsA comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.Originality/valueThis paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

Obstacle avoidance motion planning for space redundant manipulator based on improved RRT algorithm

In addressing the challenge of obstacle-avoidance motion planning for space redundant manipulators operating in environments with obstacles, an improved method based on a rapidly exploring random tree (RRT) algorithm is proposed. The method first adopts a target probabilistic sampling strategy to enhance the target orientation. Secondly, an artificial potential field is constructed to steer the growth of the node tree in joint space. Subsequently, a collision detection model between the manipulator and obstacles is established based on the bounding capsule and bounding sphere in Cartesian space. Finally, the paths are pruned and smoothed with a cubic Bezier curve. The simulation results validate the method’s capacity to effectively plan a collision-free path for the space redundant manipulator and the joint motion trajectory is smooth.

Redundant Space Manipulator Autonomous Guidance for In-Orbit Servicing via Deep Reinforcement Learning

The application of space robotic manipulators and heightened autonomy for In-Orbit Servicing (IOS) represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on Deep Reinforcement Learning (DRL) to solve for space manipulator path planning during the motion-synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-Degrees of Freedom (7-DoF) robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The Proximal Policy Optimization (PPO) DRL algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joint rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an IOS mission.

Parameters identification and trajectory optimization of free-floating space robots

There is a strong dynamic coupling between the base of free-floating space robot and the space manipulator. In order to reduce the interference of the movement of the space manipulator on the robot's base, the trajectory of the space manipulator needs to be optimized. In this paper, the joint path of space manipulators is optimized by using the pseudo-spectral method to reduce the impact of the arm movement on the base. For space robots, parameters identification is the premise of trajectory planning. A parameters identification method is proposed, which only needs to measure the base's angular speed. Numerical simulation results verify the validity of the proposed method. The attitude angle of floating base returns to the initial state after maneuvers of space manipulators.

Kinematic Analysis of 3-PRPPS Spatial Parallel Manipulator with Circularly Guided Base for Singularity-Free Robotic Motions

Robot manipulators are classified as serial manipulators and parallel manipulators. Parallel manipulators are classified into planar and spatial parallel manipulators (SPMs). The parallel manipulators have moved and fixed platforms connected with serial chains. The parallel manipulators have many linkages, which create a singularity problem. The singular positions of SPMs have also gained substantial attention in various industrial applications due to their intrinsic advantages in precision, flexibility, and load-bearing capabilities. The 3-PRPPS SPM has three prismatic joints, one spherical joint, and one revolute joint. This work changed the fixed base with a circular guided base to avoid singularity issues. The manipulator was modeled with direct kinematic relations. The Jacobian matrix for position and orientation was derived. The workspace was taken as the common area of the three circles, whose radius was the maximum arm length. The position and orientation of the end effector were traced. In the form of the end effector traces, no singularities in the mechanism were observed. The path of the robot manipulator was observed in all the possible positions and orientations. The multi-body simulation was also conducted on the 3-PRPPS manipulator, the main findings of which are presented in this article.

FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

AbstractThis paper is concerned with the problem of collision-free path planning for manipulators in multi-obstacle scenarios. Aiming at overcoming the deficiencies of existing algorithms in excessive time consumption and poor expansion quality, a path planning algorithm named Fast Bi-directional Rapidly-exploring Random Tree (FBi-RRT) with novel heuristic node expansion is proposed, which includes a selective-expansion strategy and a vertical-exploration strategy. The selective-expansion strategy is designed to guide the selection of the nearest-neighbor node to avoid the repeated expansion failure, thereby shortening the overall planning time. Also, the vertical-exploration strategy is developed to regulate the expansion direction of the collision nodes to escape from the obstacle space with less blindness, thus improving the expansion quality and further reducing time cost. Compared with previous planning algorithms, FBi-RRT can generate a feasible path for manipulators in a drastically shorter time. To validate the effectiveness of the proposed heuristic node expansion, FBi-RRT is conducted on a 6-DOF manipulator and tested in five scenarios. The experimental results demonstrate that FBi-RRT outperforms the existing methods in time consumption and expansion quality.

Autonomous Trajectory Planning for Spray Painting on Complex Surfaces Based on a Point Cloud Model.

Using teach pendants or offline programming methods can generate tool paths for robot manipulators to carry out production activities, such as spray painting on objects of different geometries. This task, in which the complexity of painting the surface is one of the main challenges, requires highly skilled operators. In addition, the time spent setting up a robot task can be justified for the mass production of the same workpiece. However, it is inconvenient for low-production and high-variation production lines. In order to overcome these challenges, this study presents an algorithm to autonomously generate robot trajectories for a spray-painting process applied to objects with complex surfaces based on input 3D point cloud data. A predefined spherical mesh wraps the object, organizing the geometrical attributes into a structured data set. Subsequently, the region of interest is extracted and isolated from the model, which serves as the basis for the automatic path-planning operation. A user-friendly graphical user interface (GUI) is developed to define input parameters, visualize the point cloud model and the generated trajectory, simulate paint quality using a color map, and ultimately generate the robot's code. A 3D sensor is used to localize the pose of the workpiece ahead of the robot and adjust the robot's trajectory. The efficacy of the proposed approach is validated first by using various workpieces within a simulated environment and second by employing a real robot to execute the motion task.

Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm.

Intelligent manufacturing requires robots to adapt to increasingly complex tasks, and dual-arm cooperative operation can provide a more flexible and effective solution. Motion planning serves as a crucial foundation for dual-arm cooperative operation. The rapidly exploring random tree (RRT) algorithm based on random sampling has been widely used in high-dimensional manipulator path planning due to its probability completeness, handling of high-dimensional problems, scalability, and faster exploration speed compared with other planning methods. As a variant of RRT, the RRT*Smart algorithm introduces asymptotic optimality, improved sampling techniques, and better path optimization. However, existing research does not adequately address the cooperative motion planning requirements for dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. It also cannot handle dual-manipulator collaborative motion planning in dynamic scenarios. Therefore, in this paper, a novel motion planner named RRT*Smart-AD is proposed to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments. This planner is capable of generating smooth motion trajectories that comply with differential constraints and physical collision constraints for a dual-arm robot. The proposed method includes several key components. First, a dynamic A* cost function sampling method, combined with an intelligent beacon sampling method, is introduced for sampling. A path-pruning strategy is employed to improve the computational efficiency. Strategies for dynamic region path repair and regrowth are also proposed to enhance adaptability in dynamic scenarios. Additionally, practical constraints such as maximum velocity, maximum acceleration, and collision constraints in robotic arm applications are analyzed. Particle swarm optimization (PSO) is utilized to optimize the motion trajectories by optimizing the parameters of quintic non-uniform rational B-splines (NURBSs). Static and dynamic simulation experiments verified that the RRT*Smart-AD algorithm for cooperative dynamic path planning of dual robotic arms outperformed biased RRT* and RRT*Smart. This method not only holds significant practical engineering significance for obstacle avoidance in dual-arm manipulators in intelligent factories but also provides a theoretical reference value for the path planning of other types of robots.

A heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN-based position posture map and rapidly-exploring random tree

The environment of tomato bunches is complex, and the fruit volume is relatively large, so manipulator picking-harvest motion planning should consider not only how to pick, but also how to avoid obstacles after picking tomato bunches and harvesting them from the complex environment, which puts forward strict requirements for picking-harvest motion performance. However, existing sampling-based algorithms still have problems of blind expansion and low efficiency. To solve these problems, a heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN (convolution neural network)-based position posture map (PPM) and rapidly-exploring random tree (RRT) is proposed in this paper. This method comprehensively considers the sampling effectiveness of the whole process of picking and harvesting, search speed, path cost and manipulability of the manipulator, combining a sampling-based path planning algorithm and deep learning framework. The method utilizes CNN and FCN(Fully Connected Neural Networks) for generating a heuristic cost map where each voxel has a cost-to-go value toward the target and an optimal target posture value to guide the expansion tree. In addition, the sampling space of obstacle avoidance planning is reduced by a spatial partition method, and the difficulty caused by the increase in the volume of the end-effector clamping fruit is solved by prioritizing the planning of the fruit harvest path and then planning the picking path. On the basis of the above research, a large number of harvest experiments have verified the effectiveness of the proposed algorithm. Research and experimental results show that, based on the PPM-RRT algorithm, the picking-harvest time of a single tomato bunch is 13 s, and the success rate is close to 100%. Compared with state-of-the-art sampling algorithms, the planning time is reduced by more than 70%, the path length is reduced by more than 24%, and the manipulability of the manipulator is increased by 38.9%. The proposed path planning method is effective in complex environments.

Nonreciprocal terahertz wavefront manipulation based on a magneto-optical metasurface with an orthogonal meta-atom pair

Active wavefront manipulation and nonreciprocal transmission give key performances in beam steering, unidirectional isolation, and multichannel multiplexing. In this work, we propose a magneto-optical (MO) Pancharatnam–Berry (PB) metasurface, which consists of a pair of orthogonal anisotropic Si and InSb columns. This orthogonal meta-atom pair (OMP) structure leads to destructive interference in the geometric phase so that the wavefront manipulation is in the OFF state without a biased magnetic field (BMF). When the BMF is applied, the nonreciprocal transmission effect for different spin states in InSb breaks the original destructive interference, so the devices provide different geometric phase distribution for different magnetic field directions and spin states, achieving the ON state of the nonreciprocal wavefront manipulation, where cross-polarization circular dichroism is higher than 0.98 and the polarization conversion rate is over 97%. Furthermore, based on this mechanism, a beam deflector and a vortex beam generator are designed by arranging the OMP with different azimuthal angles. Compared with ordinary reciprocal wavefront manipulation, this work shows that this MO PB metasurface with OMP structure has irreplaceable functions in the isolation, selection, and conversion of the two conjugate spin beams, providing a unique path for active THz wavefront manipulation.

Path planning and collision avoidance based on the RRT*FN framework for a robotic manipulator in various scenarios

AbstractIn this article, we present a new path planning algorithm based on the rapidly exploring random tree-fixed node (RRT*FN) algorithm for manipulators. It addresses the problem that RRT*FN complex environment processing is not fast enough to meet real-time requirements, and RRT*FN is basically impossible to search for an effective manipulator path in a narrow-channel environment. In the new path planning algorithm, a heuristic sampling method is adopted and the leaf nodes outside the ellipsoids are preferentially deleted to address the node removal problem, resulting in better search paths and faster search speeds. When the nodes are expanded, new nodes are generated due to goal gravity and random point gravity, with the weight values dynamically adjusted via dichotomy, and the new nodes are expanded to the goal point twice to more rapidly obtain a tree extension direction that is closer to the goal point. For boundary points, the issues of narrow channels and stepped obstacles can be effectively solved by extending the local environment sampling boundaries. To optimize the paths, the redundant intermediate nodes are simplified based on the triangle inequality. Simulation analyses show that the proposed planner can adapt to a variety of scenarios in real time while satisfying optimal path conditions.

Robotic Manipulation of Deformable Rope-Like Objects Using Differentiable Compliant Position-Based Dynamics

Robot manipulation of rope-like objects is an interesting problem with some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials. While more data-driven approaches have become more popular for finding controllers that learn to do a single task, there is still a strong motivation for a model-based method that could solve many optimization problems. Towards this end, we introduced compliant position-based dynamics (XPBD) to model rope-like objects. Using geometric constraints, the model can represent the coupling of shear/stretch and bend/twist effects. Of crucial importance is that our formulation is differentiable, which can solve parameter estimation problems and improve the matching of rope physics to real-life scenarios (i.e., the real-to-sim problem). For the generality of rope-like objects, two different solvers are proposed to handle the inextensible and extensible effects of varied material stiffness for the rope. We demonstrate our framework's robustness and accuracy on real-to-sim experimental setups using the Baxter robot and the da Vinci research kit (DVRK) [1]. Our work leads to a new path for robotic manipulation of the deformable rope-like object taking advantage of the ready-to-use gradients.

Motion Planning of Mobile Manipulator for Navigation Including Door Traversal

Door traversal is an essential task for a mobile manipulator to achieve autonomous navigation in indoor environment. However, it remains challenging since it requires the coupled motion of the robot and door and is composed of several sub-problems such as approaching, opening, passing through, and closing the door. This paper proposes a planning framework that formulates these sub-problems as a single problem and computes a path for the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area indicator</i> is introduced as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the Inverse Kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a nonholonomic mobile manipulator.

Modeling and analysis of a cable-driven serial-parallel manipulator

Cable-driven serial-parallel (CDSP) manipulators have a small size and large load/mass ratio and represent an ideal scheme for humanoid manipulators. However, owing to the multilevel mapping relationship among the end-effector, joints, cables, and motors, as well as the complex cable transmission path and cable–pulley contact, the modeling and control of such manipulators are highly complex. In this study, the kinematics and dynamics models for a CDSP manipulator were established, and a dynamic controller was proposed. First, the structure and cable transmission path of a CDSP manipulator were described. A kinematics equivalent method was used to equate the cable-driven parallel joint in the manipulator to an Equivalent pair. Using the proposed equivalent method, along with analytical and numerical methods, the kinematic equations were derived. In addition, the dynamic equations including those for the cable–pulley contact, cable tension, and joint variables were derived. Considering the actual control requirements, the cable tensions were optimized to obtain the minimum solution. Moreover, a dynamic control strategy was formulated. Finally, a prototype of the proposed manipulator was fabricated, and the cable tension of its joints and the pose accuracy of its end-effector were tested to verify the models and dynamic control method. The experiment results demonstrated that the kinematic and dynamic models are valid, and the dynamic controller is effective.

Magnetically Actuated Superhydrophilic Robot Sphere Fabricated by a Femtosecond Laser for Droplet Steering.

Droplet steering has important applications in biomedical detection, local chemical reactions, liquid collection, and microfluidic control. Presently, droplet steering methods typically require specific paths and can be challenging to operate, involving complex fabrications for the operating systems. Here, we show a magnetically actuated superhydrophilic robot sphere (MSR) based on femtosecond laser direct writing technology for droplet steering. Through femtosecond laser treatment, uniform micro-/nanostructures are constructed on the surface of a MSR. Additionally, the contactless magnetic actuator makes it possible to remotely steer the MSR to transport droplets. After preliminary exploration of the mechanism by which MSR drives the droplet movement, the ability of MSR to control the droplet movement was systematically tested and analyzed. Moreover, the applications of the MSR in complex path liquid collection and transport, three-dimensional space transport, self-cleaning, etc., are further verified. This strategy provides a novel and reliable path for droplet manipulation and broadens its application.

Grasp Analysis for the Robot-Based Manipulation of Pre-Assembled Cables with Electrical Connectors

The mounting of pre-assembled cables with electrical connectors is mainly carried out manually in industry today. An exemplary application is the interconnection of battery modules. Automation of such assembly tasks offers the potential for increasing efficiency but requires the design of suitable gripper systems. This is challenging as the cable induces state-dependent forces and torques on the gripper system, which must be transmitted via the complex surface geometries of the plugs. Currently, the required grasp force cannot be determined in advance but only after prototypes have been manufactured and with elaborate physical experiments. To overcome these drawbacks, we present a methodology for the grasp analysis of pre-assembled cables with electrical connectors. The novelty of this approach is to combine a physics simulation for deformable linear objects with a contact model for non-planar grasping surfaces. The results indicate that the cable deformation significantly affects the required grasp force. In addition, each combination of contact surface and dynamic cable deformation results in an individual grasp force course. The methodology enables comparison of different electrical connectors and their grasping surfaces, as well as cables and their manipulation paths, efficiently and with little expert knowledge.

It does not need two: Assessing physiological linkage from videos across the valence dimension.

The phenomenon of physiological linkage describes similar fluctuations of two individuals' physiology, for example, the cardiac inter-beat interval (IBI). Physiological linkage is a well-documented occurrence in research settings of interacting dyads but the literature on non-interacting dyads, that is, someone watching a video of another person, is sparse. The current study investigated whether physiological linkage, based on IBI, occurs from watching videos where strangers report about personal (neutral, positive, negative non-traumatic, and negative traumatic) experiences. Videos were produced with six individuals and then presented to observers (N = 26). Time-frequency-domain cross-wavelet analyses supplemented by threshold-free cluster enhancement (TFCE; to account for multiple testing) showed significant physiological linkage between the IBI of observers and persons in the videos for 16 out of the 21 tested videos. Although significant physiological linkage also emerged for neutral videos and positive, negative valence videos led to such associations more reliably. This study shows that physiological linkage can be investigated in highly controlled conditions based on video stimuli paving the path for experimental manipulation in future research. Furthermore, due to the provision of information on time and frequency, the use of cross-wavelet analysis is encouraged to learn more about factors modulating physiological linkage. The current study presents the next step toward identifying psychophysiological causal and modulating factors of physiological linkage.