Manipulation Planning Research Articles

A Spatial Biarc Method for Inverse Kinematics and Configuration Planning of Concentric Cable-Driven Manipulators

Superior dexterity and extreme flexibility are typical advantages for concentric cable-driven manipulators working in confined spaces. However, its inverse kinematics and configuration planning are very complicated. In this article, we propose a spatial biarc method for the above problem. The distinguishing feature of this method is that input parameters are two positions and two direction vectors in three-dimensional (3-D) space, and the output is a reasonable spatial biarc for controlling a concentric cable-driven manipulator in 3-D space. This method has the following three advantages. First, the positions and direction vectors of the base and inner distal tip are considered simultaneously. In addition, the length and ratio of the overlapped section and separated section can be adjusted by changing the length of the direction vectors. Furthermore, by judging the angular value of the direction vectors, one can predetermine whether the spatial configuration of the entire arm is C- or S-shaped. The proposed method realizes the parameterization of a concentric cable-driven manipulator, which makes it convenient to intuitively control the manipulator to achieve interference-free motion trajectory planning in confined spaces. Finally, trajectory tracking inspections are simulated and experimentally executed. It can be seen from results that the proposed spatial biarc method can provide reasonable solutions for concentric cable-driven manipulators. The method is especially favorable in terms of 3-D-pose-determination problem and trajectory-planning problem. It can also be applied to other manipulators with similar configurations. Without loss of generality, when the given points and direction vectors are coplanar, the proposed spatial biarc method can be transformed to a planar biarc method.

Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints

Redundancy resolution is a critical issue to achieve accurate kinematic control for manipulators. End-effectors of manipulators can track desired paths well with suitable resolved joint variables. In some manipulation applications such as selecting insertion paths to thrill through a set of points, it requires the distal link of a manipulator to translate along such fixed point and then perform manipulation tasks. The point is known as remote centre of motion (RCM) to constrain motion planning and kinematic control of manipulators. Together with its end-effector finishing path tracking tasks, the redundancy resolution of a manipulators has to maintain RCM to produce reliable resolved joint angles. However, current existing redundancy resolution schemes on manipulators based on recurrent neural networks (RNNs) mainly are focusing on unrestricted motion without RCM constraints considered. In this paper, an RNN-based approach is proposed to solve the redundancy resolution issue with RCM constraints, developing a new general dynamic optimisation formulation containing the RCM constraints. Theoretical analysis shows the theoretical derivation and convergence of the proposed RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on an industrial redundant manipulator system.

Smooth and proximate time-optimal trajectory planning of robotic manipulators

The simultaneous achievement of high accuracy and productivity in robotics has been extensively investigated using the trajectory planning approach. We propose a smooth and proximate time-optimal trajectory planning method. First, we generated the velocity limit curve by expressing joint torque and joint velocity constraints as a function of the path coordinate. Then, we calculated the time-optimal trajectory using a dynamic programming technique and fitted the smooth cubic spline. The smoothing factor of the cubic spline curve was optimized by applying a binary recursive. The proposed method can guarantee the continuity and smoothness of joint torque. The simulation results showed that the proposed method can reduce joint-jerk and improve the tracking accuracy of the controller.

Financial reporting and book-tax conformity: A review of the issues

This work reviews accounting research on book-tax conformity (BTC) with specific reference to financial reporting issues. There is an ongoing debate in the accounting literature about the impact of BTC levels (weak/strong) on accounting quality and on tax avoidance. Policymakers have discussed at length the opportunity to reform BTC as well. Proponents of a strong BTC argue that it can deter both financial reporting ma-nipulation and aggressive tax planning by creating contrasting incentives between book earnings maximisation and taxable income minimisation. Further controls on book earnings assured by taxing authorities will reinforce such beneficial ef-fects. Opponents of a strong BTC suggest that financial accounting decisions should not interfere with tax accounting and vice versa, as financial reporting and tax reporting have different purposes. Furthermore, under a strong BTC, managers will tend to smooth earnings to minimise income taxes, thus reducing earnings in-formativeness. Even if a strand of research based on the Tax Reform Act (TRA 86) in the US corroborates the position of opponents, a large body of literature formed in international settings has not yet reached a consensus over the conse-quences of BTC. This circumstance makes BTC a relevant topic to the current de-bate on financial reporting quality.

Complex In-Hand Manipulation Via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand’s kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different manipulation manifolds. This capability, however, has traditionally been difficult or impossible to practically realize. A finger gaiting system must simultaneously plan for and control forces on the object while maintaining stability during contact switching. This letter alleviates the traditional requirement by taking advantage of system compliance, allowing the hand to more easily switch contacts while maintaining a stable grasp. Our method achieves complete <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula> finger gaiting control of grasped objects against gravity by developing a manipulation planner that operates via orthogonal safe modes of a compliant, underactuated hand absent of tactile sensors or joint encoders. During manipulation, a low-latency 6D pose object tracker provides feedback via vision, allowing the planner to update its plan online so as to adaptively recover from trajectory deviations. The efficacy of this method is showcased by manipulating both convex and non-convex objects on a real robot. Its robustness is evaluated via perturbation rejection and long trajectory goals. To the best of the authors’ knowledge, this is the first work that has autonomously achieved full <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula> control of objects within-hand via finger gaiting and without a support surface, elucidating a valuable step towards realizing true robot in-hand manipulation capabilities.

Robotic Cable Routing with Spatial Representation

Cable routing is a challenging task for robotic automation. To accomplish the task, it requires a high-level path planner to generate a sequence of cable configurations from the initial state to the target state and a low-level manipulation planner to plan the robot motion commands to transit between adjacent states. However, there are yet no proper representations to model the cable with the environment objects, impeding the design of both high-level path planning and low-level manipulation planning. In this letter, we propose a framework for cable routing with spatial representation. For high-level planning, by considering the spatial relations between the cable and the environment objects such as fixtures, the proposed method is able to plan a path from the initial state to the goal state in a graph. For low-level manipulation, multiple manipulation primitives are efficiently learned from human demonstration, to configure the cable to planned intermediate states leveraging the same spatial representation. We also implement a cable state estimator that robustly extracts the spatial representation from raw RGB-D images, thus completing the cable routing framework. We evaluate the proposed framework with various cables and fixture settings, and demonstrate that it outperforms some baselines in terms of reliability and generalizability.

Runge–Kutta Type Discrete Circadian RNN for Resolving Tri-Criteria Optimization Scheme of Noises Perturbed Redundant Robot Manipulators

In order to resist periodic interfere in robot hardware or environment, a Runge&#x2013;Kutta type discrete-time circadian rhythms neural network (RK-DCRNN) model is proposed, and investigated to plan the motion of redundant robot manipulators. To achieve the optimal control, a quadratic programming-based acceleration-level hybrid tri-criteria (ALHT) scheme is first designed, which simultaneously minimize the acceleration norm, torque norm, and joint-angle shift-free indices. Second, according to the neural dynamic design method, a continuous-time circadian rhythms neural network model is exploited, and then based on the Runge&#x2013;Kutta numerical differential method, a discrete-time circadian rhythms neural network model is obtained. Third, the convergence of the proposed RK-DCRNN model is proved by detailed mathematical derivation. Fourth, comparative simulations and physical experiments verify that the proposed RK-DCRNN model can suppress the accumulation of position error in the motion planning of manipulators.

Fault tolerant motion planning and control of space manipulators with free-swinging joint failure based on controllable degree

Space manipulators are usually used to assist or replace the astronaut to execute on-orbit tasks. However, due to the burdensome tasks and harsh space environment, free-swinging joint failure is prone to happen, causing the task to be unable to continue. Once the joint failure occurs, the base and free-swinging joint belong to underactuated units, so their trajectories have to be controlled by the active joint. To execute on-orbit tasks successfully after the joint failure occurs, a fault tolerant motion planning and control method (FTMPC) of the space manipulator based on controllable degree is proposed. First, based on the dynamics model of space manipulators, the dynamics coupling relationship between the active joint and the underactuated unit is established. Second, according to the inertia matrix, Coriolis and centrifugal terms of the dynamics model, a controllable degree index of the active joint over the underactuated unit is constructed. Third, according to the specific task requirement and controlled object, the FTMPC based on the controllable degree is designed. The FTMPC is applied according to the number of active joints and underactuated units and whether there is a dynamic singularity during the task execution. The simulation is carried out with a 7-DOF space manipulator, verifying the correctness and effectiveness of the FTMPC. The method has two advantages: 1) Coriolis and centrifugal terms are both considered for the first time in evaluating the control ability of active joints, making the evaluation result more in line with the motion characteristics of space manipulators; 2) A FTMPC based on the controllable degree is proposed, completing the task without dynamic singularity or huge torque on the active joint.

Path Planning Strategy for a Manipulator Based on a Heuristically Constructed Network

Collision-free path planning of manipulators is becoming indispensable for space exploration and on-orbit operation. Manipulators in these scenarios are restrained in terms of computing resources and storage, so the path planning method used in such tasks is usually limited in its operating time and the amount of data transmission. In this paper, a heuristically constructed network (HCN) construction strategy is proposed. The HCN construction contains three steps: determining the number of hub configurations and selecting and connecting hub configurations. Considering the connection time and connectivity of HCN, the number of hub configurations is determined first. The selection of hub configurations includes the division of work space and the optimization of the hub configurations. The work space can be divided by considering comprehensively the similarity among the various configurations within the same region, the dissimilarity among all regions, and the correlation among adjacent regions. The hub configurations can be selected by establishing and solving the optimization model. Finally, these hub configurations are connected to obtain the HCN. The simulation indicates that the path points number and the planning time is decreased by 45.5% and 48.4%, respectively, which verify the correctness and effectiveness of the proposed path planning strategy based on the HCN.

Time-Optimal Asymmetric S-Curve Trajectory Planning of Redundant Manipulators Under Kinematic Constraints

Time-Optimal Asymmetric S-Curve Trajectory Planning of Redundant Manipulators Under Kinematic Constraints

Manipulation Planning of Wiring a Cable with Connector Considering Shape Transition of Deformable Linear Object

Manipulation Planning of Wiring a Cable with Connector Considering Shape Transition of Deformable Linear Object

Vision-Based Robotic Comanipulation for Deforming Cables

Although deformable linear objects (DLOs), such as cables, are widely used in the majority of life fields and activities, the robotic manipulation of these objects is considerably more complex compared to the rigid-body manipulation and still an open challenge. In this paper, we introduce a new framework using two robotic arms cooperatively manipulating a DLO from an initial shape to a desired one. Based on visual servoing and computer vision techniques, a perception approach is proposed to detect and sample the DLO as a set of virtual feature points. Then a manipulation planning approach is introduced to map between the motion of the manipulators end effectors and the DLO points by a Jacobian matrix. To avoid excessive stretching of the DLO, the planning approach generates a path for each DLO point forming profiles between the initial and desired shapes. It is guaranteed that all these intershape profiles are reachable and maintain the cable length constraint. The framework and the aforementioned approaches are validated in real-life experiments.

Path planning for manipulators based on an improved probabilistic roadmap method

An indispensable feature of an intelligent manipulator is its capability to quickly plan a short and safe path in the presence of obstacles in its workspace. Among the path planning methods, the probabilistic roadmap (PRM) method has been widely applied in path planning for a high-dimensional manipulator to avoid obstacles. However, its efficiency remains disappointing when the free space of manipulators contains narrow passages. To solve this problem, an improved PRM method is proposed in this paper. Based on a virtual force field, a new sampling strategy of PRM is presented to generate configurations more appropriate for practical application in the free space. Correspondingly, in order to interconnect these configurations to form a roadmap, a new connection strategy is designed, which consists of three stages and can gradually improve the connectivity of the roadmap. The contributions of this paper are as follows. The new sampling strategy can increase the sampling density at the narrow passages of the free space and reduce the redundancy of the samples in the wide-open regions of the free space; the three-stage connection strategy for interconnecting samples can ensure a high-connectivity roadmap; through synthesizing the above strategies, the improved PRM method is more suitable for path planning of manipulators to avoid obstacles efficiently in a cluttered environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available for manipulator of any degrees of freedom.

Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network

Redundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator.

Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper

We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilizes a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers, jointly controlled by a single actuator, connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and, thus, the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyze the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.

Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping

Robotic manipulation of a bulky object is challenging due to the limited kinematics and payload of the manipulator. In this study, a robot realizes the manipulation of general-shaped bulky objects utilizing the contact with the environment. We propose a hierarchical manipulation planner that effectively combined three manipulation styles, namely, pivoting, tumbling, and regrasping. In our proposed method, we first generate a set of superimposed planar segments on the object surface to obtain an object pose in stable contact with the table, and a set of points on the object surface for the end-effectors (EEFs) of a dual-arm manipulator to stably grasp the object. Object manipulation can be realized by solving a graph, considering the kinematic constraints of pivoting and tumbling. For pivoting, we consider two supporting styles: stable support (SP) and unstable support (USP). Our proposed method manipulates large and heavy objects by selectively using the two different support styles of pivoting and tumbling according to the conditions on the table area. In addition, it can effectively avoid the limitation arising due to the arm kinematics by regrasping the object. We experimentally demonstrate that a dual-arm manipulator can move an object from the initial to goal position within a limited area on the table, avoiding obstacles placed on the table.

A geometric method based on space arc for pose-configuration simultaneous planning of segmented hyper-redundant manipulators

A geometric method based on space arc for pose-configuration simultaneous planning of segmented hyper-redundant manipulators

Dexterous robotic grasping of delicate fruits aided with a multi-sensory e-glove and manual grasping analysis for damage-free manipulation

Gripping control of the manipulator is still a technical problem in its research field. Accurate, efficient, and lossless crawling remains a challenge due to the difficulty of controlling grip mode, joint movement, and optimal implementation. Human hands can accurately perceive objects and grasp flexibly, so understanding and simulating the gripping behavior of human hands is crucial for controlling manipulators. In this study, an electronic glove was designed and assembled based on manual grasping and mechanical grip perception, combined with force sensors, bending sensors, and inertial sensor (Inertial Measurement Unit, IMU). The mechanism and characteristics of handgrip are analyzed from the four aspects, including force and bending relationship, hand grasping patterns, finger cooperation and correlation, and the law of external interaction acceleration. The pressure generally increases with the increase of bending, so the force can be controlled by controlling the degree of bending. Moreover, mode A shows a more complex grip mechanism compared with mode B. The pose position and orientation of the manipulator can be controlled by analyzing the gripping characteristics between the various modes. Besides, the correlation between the index finger, middle finger, and ring finger is relatively high. By understanding the correlation law between the fingers can control the cooperative movement of the manipulator when grasping. And the angular velocity of the X and Y axes changes significantly during the grasping process. The direction of movement and position of the manipulator can be determined by the changing state of the angular velocity. Thus, the analysis of these aspects could provide theoretical guidance for the structural design, surface material selection, and grasping planning of bionic manipulators.

Multi-Resolution A*

Heuristic search-based planning techniques are commonly used for motion planning on discretized spaces. The performance of these algorithms is heavily affected by the resolution at which the search space is discretized. Typically a fixed resolution is chosen for a given domain. While a finer resolution allows better maneuverability, it exponentially increases the size of the state space, and hence demands more search efforts. On the contrary, a coarser resolution gives a fast exploratory behavior but compromises on maneuverability and the completeness of the search. To effectively leverage the advantages of both high and low resolution discretizations, we propose Multi-Resolution A* (MRA*) algorithm, that runs multiple weighted-A*(WA*) searches with different resolution levels simultaneously and combines the strengths of all of them. In addition to these searches, MRA* uses one anchor search to control expansions of other searches. We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete. We performed experiments on several motion planning domains including 2D, 3D grid planning and 7 DOF manipulation planning and compared our approach with several search-based and sampling-based baselines.

Global motion planning and redundancy resolution for large objects manipulation by dual redundant robots with closed kinematics

AbstractThe multi-arm robotic systems consisting of redundant robots are able to conduct more complex and coordinated tasks, such as manipulating large or heavy objects. The challenges of the motion planning and control for such systems mainly arise from the closed-chain constraint and redundancy resolution problem. The closed-chain constraint reduces the configuration space to lower-dimensional subsets, making it difficult for sampling feasible configurations and planning path connecting them. A global motion planner is proposed in this paper for the closed-chain systems, and motions in different disconnected manifolds are efficiently bridged by two type regrasping moves. The regrasping moves are automatically chosen by the planner based on cost-saving principle, which greatly improve the success rate and efficiency. Furthermore, to obtain the optional inverse kinematic solutions satisfying joint physical limits (e.g., joint position, velocity, acceleration limits) in the planning, the redundancy resolution problem for dual redundant robots is converted into a unified quadratic programming problem based on the combination of two diff erent-level optimizing criteria, i.e. the minimization velocity norm (MVN) and infinity norm torque-minimization (INTM). The Dual-MVN-INTM scheme guarantees smooth velocity, acceleration profiles, and zero final velocity at the end of motion. Finally, the planning results of three complex closed-chain manipulation task using two Franka Emika Panda robots and two Kinova Jaco2 robots in both simulation and experiment demonstrate the effectiveness and efficiency of the proposed method.