Redundancy Resolution Research Articles

Inverse kinematics solution and posture optimization of a new redundant hybrid automatic fastening system for aircraft assembly

PurposeThe purpose of this paper is to use the redundancy of a new hybrid automatic fastening system (HAFS) for aircraft assembly in the best way.Design/methodology/approachFirst, the kinematic model of HAFS is divided into three sub-models, which are the upper/lower tool and parallel robot. With the geometric coordination relationship, a comprehensive kinematic model of the HAFS is built by mathematically assembling the sub-models based on the DH method. Then, a novel master-slave decoupling strategy for inverse kinematics solution is proposed. With the combination of the minimum energy consumption and the comfortable configuration, a multi-objective redundancy resolution method is developed to optimize the fastening configuration of the HAFS, which keep the HAFS away from the joint-limits and collision avoiding in the aircraft panel assembly process.FindingsAn efficient multi-objective posture optimization algorithm to use the redundancy in the best way is obtained. Simulation and an experiment are used to demonstrate the correctness of the proposed method. Moreover, the position and orientation errors of the drilling holes are within 0.222 mm and 0.356°, which are accurate enough for the automatic fastening in aircraft manufacturing.Practical implicationsThis method has been used in the HAFS control system, and the practical results show the aircraft components can be fastened automatically through this method with high efficiency and high quality.Originality/valueThis paper proposes a comprehensive kinematic model and a novel decoupling strategy for inverse kinematic solution of the HAFS, which provides a reference to utilize the redundancy in the best way for a hybrid machine with redundant function.

Evolution Strategies Learning With Variable Impedance Control for Grasping Under Uncertainty

During a robot's interaction with the environment, it is necessary to ensure the safety and robustness of the robot's movements. To improve the safety and adaptiveness of robots in performing complex movement tasks, a novel method called covariance matrix adaptation-evolution strategies (CMA-ES) for learning complex and high-dimensional motor skills is presented. Considering the complex motion model of trajectories, dynamic movement primitives (DMPs), which is a generic method for trajectories modeling in attractor landscape based on differential dynamic systems, is used to represent the robot's trajectories. CMA-ES offers a theoretical rule for updating the parameters of DMPs and a variable impedance controller, which can reduce the impact of noisy environment on the robot's movement. In this paper, we propose two hierarchies for controlling the robot: the high-level neural-dynamic network optimization for redundancy resolution in task space and the low-level CMA-ES fusing with DMPs for learning trajectories in joint space. In this paper, CMA-ES method is explored to learn variable impedance control and the performance of the proposed method in learning the robot's movements is also tested.

Motor-Imagery-Based Teleoperation of a Dual-Arm Robot Performing Manipulation Tasks

This paper proposes a brain–computer interface (BCI)-based teleoperation strategy for a dual-arm robot carrying a common object by multifingered hands. The BCI is based on motor imagery of the human brain, which utilizes common spatial pattern method to analyze the filtered electroencephalograph signals. Human intentions can be recognized and classified into the corresponding reference commands in task space for the robot according to phenomena of event-related synchronization/desynchronization, such that the object manipulation tasks guided by human user’s mind can be achieved. Subsequently, a concise dynamics consisting of the dynamics of the robotic arms and the geometrical constraints between the end-effectors and the object is formulated for the coordinated dual arm. To achieve optimization motion in the task space, a redundancy resolution at velocity level has been implemented through neural-dynamics optimization. Extensive experiments have been made by a number of subjects, and the results were provided to demonstrate the effectiveness of the proposed control strategy.

Trajectory Planning of the Humanoid Manipulator

The paper presents a method of planning a collision-free trajectory for a humanoid manipulator mounted on a rail system. The task of the robot is to move its end-effectors from the current position to the given final location in the workspace. The method is based on a redundancy resolution at the velocity level. In addition to this primary task, secondary objectives are also taken into account. The motion of the robot is planned in order to maximize a manipulability measure in purpose of avoiding manipulator singularities. State inequality constraints resulting from collision avoidance conditions are also considered. A computer example involving a humanoid manipulator operating in a three dimensional task space is also presented.

TURBot: A System for Robot-Assisted Transurethral Bladder Tumor Resection

Staging and treatment of nonmuscle-invasive bladder tumors using transurethral resection of bladder tumors (TURBT) is challenging due to limitations in intravesicular tool dexterity, visualization and risk of bladder wall perforation. Currently, TURBT is achieved via a rigid resectoscope. This dexterity limitation is exacerbated when tumors are located in the bladder neck region. To address these limitations, an endoscopic robotic system called TURBot was developed. The paper presents the design considerations, modeling, and control challenges that were addressed to enable the first robot-assisted in vivo TURBT. The design and control of a slave robot comprised of a multisegment continuum robot capable of deploying a micro snake-like continuum robot for control of an ablation laser, a grasper, and a fiberscope is presented. A strategy for constrained telemanipulation is presented based on the redundancy resolution with varying task dimension that is commensurate with the level of constraint experienced by the robot during transurethral deployment. In addition to evaluation in animals, TURBot resection is compared against manual resection in a mockup user study using a human bladder phantom. The contributions of this paper present key steps that pave the way toward successful clinical robot-assisted TURBT.

Directional Stiffness Modulation of Parallel Robots With Kinematic Redundancy and Variable Stiffness Joints

Parallel robots have been primarily investigated as potential mechanisms with stiffness modulation capabilities through the use of actuation redundancy to change internal preload. This paper investigates real-time stiffness modulation through the combined use of kinematic redundancy and variable stiffness actuators. A known notion of directional stiffness is used to guide the real-time geometric reconfiguration of a parallel robot and command changes in joint-level stiffness. A weighted gradient-projection redundancy resolution approach is demonstrated for resolving kinematic redundancy while satisfying the desired directional stiffness and avoiding singularity and collision between the legs of a Gough/Stewart parallel robot with movable anchor points at its base and with variable stiffness actuators. A simulation study is carried out to delineate the effects of using kinematic redundancy with or without the use of variable stiffness actuators. In addition, modulation of the entire stiffness matrix is demonstrated as an extension of the approach for modulating directional stiffness.

Discretized Mid-Value CLVI-PDNN Based Redundancy Resolution for Single Leg of Quadruped Robot

The two most important performance indicators of quadruped robot are load capacity and walking speed, and these performance indicators of the whole robot finally reflect on the joint torques and angular velocities. To satisfy different requirements of walking speed and load capacity when quadruped robots implement different tasks, the joint torques and angular velocities need to be balanced with physical constraints of the joints. A single leg with redundant DOF (degree of freedom) could optimize the distribution of joint torques or angular velocities based on different performance requirements. This paper presents a kind of new recurrent neural networks taking joint torques and angular velocities simultaneously into consideration and proposes mid-value CLVI-PDNN to achieve the optimal joint torques and angular velocities with physical constraints of the mechanism as described in our previous paper. Because the continuous mid-value CLVI-PDNN has difficulty in real-time operation because of too much calculation workload, two kinds of methods are proposed to discretize the mid-value CLVI-PDNN for application on computer or digital circuit. The simulation results demonstrate the efficacy of the algorithm proposed in this paper.

A Dynamic Neural Network Approach for Efficient Control of Manipulators

Redundancy resolution is a critical problem in the control of manipulators. The dual neural network, as a special type of recurrent neural networks that are inherently parallel processing models, is widely investigated in past decades to control manipulators. However, to the best of our knowledge, existing dual neural networks require a full knowledge about manipulator parameters for efficient control. We make progress along this direction in this paper by proposing a novel model-free dual neural network, which is able to address the learning and control of manipulators simultaneously in a unified framework. Different from pure learning problems, the interplay of the control part and the learning part allows us to inject an additive noise into the control channel to increase the richness of signals for the purpose of efficient learning. Due to a deliberate design, the learning error is guaranteed for convergence to zero despite the existence of additive noise for stimulation. Theoretical analysis reveals the global stability of the proposed neural network control system. Simulation results verify the effectiveness of the proposed control scheme for redundancy resolution of a PUMA 560 manipulator.

A Finite-Time Convergent and Noise-Rejection Recurrent Neural Network and Its Discretization for Dynamic Nonlinear Equations Solving.

The so-called zeroing neural network (ZNN) is an effective recurrent neural network for solving dynamic problems including the dynamic nonlinear equations. There exist numerous unperturbed ZNN models that can converge to the theoretical solution of solvable nonlinear equations in infinity long or finite time. However, when these ZNN models are perturbed by external disturbances, the convergence performance would be dramatically deteriorated. To overcome this issue, this paper for the first time proposes a finite-time convergent ZNN with the noise-rejection capability to endure disturbances and solve dynamic nonlinear equations in finite time. In theory, the finite-time convergence and noise-rejection properties of the finite-time convergent and noise-rejection ZNN (FTNRZNN) are rigorously proved. For potential digital hardware realization, the discrete form of the FTNRZNN model is established based on a recently developed five-step finite difference rule to guarantee a high computational accuracy. The numerical results demonstrate that the discrete-time FTNRZNN can reject constant external noises. When perturbed by dynamic bounded or unbounded linear noises, the discrete-time FTNRZNN achieves the smallest steady-state errors in comparison with those generated by other discrete-time ZNN models that have no or limited ability to handle these noises. Discrete models of the FTNRZNN and the other ZNNs are comparatively applied to redundancy resolution of a robotic arm with superior positioning accuracy of the FTNRZNN verified.

Stable Torque Optimization for Redundant Robots Using a Short Preview

We consider the known phenomenon of torque oscillations and motion instabilities that occur in redundant robots during the execution of sufficiently long Cartesian trajectories when the joint torque is instantaneously minimized. In the framework of on-line local redundancy resolution methods, we propose basic variations of the minimum torque scheme to address this issue. Either the joint torque norm is minimized over two successive discrete-time samples using a short preview window, or we minimize the norm of the difference with respect to a desired momentum-damping joint torque, or the two schemes are combined together. The resulting local control methods are all formulated as well-posed linear-quadratic problems, and their closed-form solutions also generate low joint velocities while addressing the primary torque optimization objectives. Stable and consistent behaviors are obtained along short or long Cartesian position trajectories, as illustrated with simulations on a 3R planar arm and with experiments on a 7R KUKA LWR robot.

Hybrid Strategy-based Coordinate Controller for an Underwater Vehicle Manipulator System Using Nonlinear Disturbance Observer

SummaryThis paper presents a hybrid strategy-based coordinate controller with a novel nonlinear disturbance observer for autonomous underwater vehicle manipulator systems (UVMSs). This method can reduce the influence from external unknown disturbances, inner coupling effects and model uncertainties by using a modified disturbance observer. Considering the natural redundancy property of the UVMS, the redundancy resolution algorithm is often utilized to give desired trajectories in the vehicle–joint space. However, because of the calibration errors, assembling errors and numerical errors, these desired trajectories may not lead the end-effector to the goal point accurately. To realize accurate motion control even when small errors exist in the planning phase, a hybrid strategy is introduced to transform the controller in the joint–vehicle space to the controller in the task space. Numerical simulations based on a UVMS have been carried out to testify the effectiveness of the proposed coordinate controller and the hybrid strategy. During the simulations, unknown disturbances are exerted upon the system. The trajectory tracking and error fixing performances are discussed in comparative analyses. The controller also maintains robust characteristics in comparison with the passivity-based controller and the proposed controller but without the disturbance observer. Experiments are also carried out to test its performance.

Goal-related feedback guides motor exploration and redundancy resolution in human motor skill acquisition.

The plasticity of the human nervous system allows us to acquire an open-ended repository of sensorimotor skills in adulthood, such as the mastery of tools, musical instruments or sports. How novel sensorimotor skills are learned from scratch is yet largely unknown. In particular, the so-called inverse mapping from goal states to motor states is underdetermined because a goal can often be achieved by many different movements (motor redundancy). How humans learn to resolve motor redundancy and by which principles they explore high-dimensional motor spaces has hardly been investigated. To study this question, we trained human participants in an unfamiliar and redundant visually-guided manual control task. We qualitatively compare the experimental results with simulation results from a population of artificial agents that learned the same task by Goal Babbling, which is an inverse-model learning approach for robotics. In Goal Babbling, goal-related feedback guides motor exploration and thereby enables robots to learn an inverse model directly from scratch, without having to learn a forward model first. In the human experiment, we tested whether different initial conditions (starting positions of the hand) influence the acquisition of motor synergies, which we identified by Principal Component Analysis in the motor space. The results show that the human participants’ solutions are spatially biased towards the different starting positions in motor space and are marked by a gradual co-learning of synergies and task success, similar to the dynamics of motor learning by Goal Babbling. However, there are also differences between human learning and the Goal Babbling simulations, as humans tend to predominantly use Degrees of Freedom that do not have a large effect on the hand position, whereas in Goal Babbling, Degrees of Freedom with a large effect on hand position are used predominantly. We conclude that humans use goal-related feedback to constrain motor exploration and resolve motor redundancy when learning a new sensorimotor mapping, but in a manner that differs from the current implementation of Goal Babbling due to different constraints on motor exploration.

Learning based end effector tracking control of a mobile manipulator for performing tasks on an uneven terrain

A Mobile manipulator (MM) combines the ability to move on an uneven terrain and perform a desired task using the manipulator arm, like drilling, welding, pick and place, etc. However, due to slip, modeling and sensor errors during motion, the MM tends to deviate from the desired end effector path. The MM considered in this paper consists of a 10 DOF vehicle and a 4 DOF arm. As the system is redundant, the standard jacobian based pseudo inverse methods for finding the inverse kinematics solution cannot be used in real time and are very complex. Therefore, a learning based method using a two stage KSOM network has been used to learn the inverse kinematics solution and also track the end effector during motion. In the first stage the KSOM performs the redundancy resolution of the system and in the second stage it corrects the error during the actual motion. The forward kinematics model of the mobile manipulator is used to train the KSOM network, with the manipulability measure of the arm, joint angles of the system and the wheel terrain contact as constraints. Once the network is trained for a particular terrain, the corresponding joint angles and wheel velocities for tracking a desired end effector trajectory can be found in real time. Simulation and experimental results for tracking different end effector trajectories on an uneven terrain, proves the efficiency of the proposed method. The experimental results prove that the proposed method could track the end effector trajectory with an average error of less than 2 cm.

Recurrent-Neural-Network-Based Velocity-Level Redundancy Resolution for Manipulators Subject to a Joint Acceleration Limit

For the safe operation of redundant manipulators, physical constraints such as the joint angle, joint velocity, and joint acceleration limits should be taken into account when designing redundancy resolution schemes. Velocity-level redundancy resolution schemes are widely adopted in the kinematic control of redundant manipulators due to the existence of the well-tuned inner loop regarding the joint velocity control. However, it is difficult to deal with joint acceleration limits for velocity-level redundancy resolution methods. In this paper, a recurrent-neural-network-based velocity-level redundancy resolution method is proposed to deal with the problem, and theoretical results are given to guarantee its performance. By the proposed method, the end-effector position error is asymptotically convergent to zero, and all the joint limits are not violated. The effectiveness and superiority of the proposed scheme are validated via simulation results.

Dynamic Analysis of a 7 DOF Robot Using Fuzzy Logic for Inverse Kinematics Problem

In this paper, a dynamic analysis for a redundant, serial robot, with seven degrees of freedom is presented. The dynamic model of the robot is based on importing a tri-dimensional CAD model of the robot into MATLAB®, Simulink®-Simscape™-SimMechanics™. One can import complete CAD assemblies, including all masses, inertias, joints, constraints, and tri-dimensional geometries, into the model. The first step for executing a dynamic analysis is to resolve the Inverse Kinematics (IK) problem for the redundant robot. Because this type of robot has seven degrees of freedom the IK resolution implies an infinite number of solutions. So, to be able to resolve the IK for this kind of robot a Fuzzy Logic (FL) technique is used for redundancy resolution. The entire IK resolution is based on a fact that one degree of freedom is determined using FL. Solving the inverse kinematics problem is a mandatory step in the dynamic analysis of the robot, this is required to drive the robot on certain user-imposed trajectories. The dynamic model of the robot is necessary to determine and analyse joint torques so one can reconstruct the fuzzy model with new rules. Reconstructing the fuzzy model would lead to joint torques minimization and efficiency in robot operation. Also, the dynamic model of the redundant robot is necessary for the simulation of motion, analysis of the robot’s structure and design of optimal control algorithms. This will be presented in some further research.

Redundancy resolution and control of a novel spatial parallel mechanism with kinematic redundancy

In this paper, a spatial kinematically redundant parallel mechanism consisting of three base prismatic actuators and six lateral prismatic actuators which control the six task space degrees of freedom is proposed. The main objective is to introduce a real-time redundancy resolution to actuate kinematically redundant parallel mechanisms. In the proposed method, the redundant coordinates are determined in such a way to improve the dynamic performance of the mechanism for tracking a given trajectory in order to avoid singular configurations. An inverse dynamic control strategy with a non-linear disturbance observer is designed. Kinematics and dynamics of the proposed mechanism are investigated and the control algorithm is implemented for motion control of the introduced mechanism. The effectiveness of the proposed controller is demonstrated through numerical simulations.

Neural-Dynamic Based Synchronous-Optimization Scheme of Dual Redundant Robot Manipulators.

In order to track complex-path tasks in three dimensional space without joint-drifts, a neural-dynamic based synchronous-optimization (NDSO) scheme of dual redundant robot manipulators is proposed and developed. To do so, an acceleration-level repetitive motion planning optimization criterion is derived by the neural-dynamic method twice. Position and velocity feedbacks are taken into account to decrease the errors. Considering the joint-angle, joint-velocity, and joint-acceleration limits, the redundancy resolution problem of the left and right arms are formulated as two quadratic programming problems subject to equality constraints and three bound constraints. The two quadratic programming schemes of the left and right arms are then integrated into a standard quadratic programming problem constrained by an equality constraint and a bound constraint. As a real-time solver, a linear variational inequalities-based primal-dual neural network (LVI-PDNN) is used to solve the quadratic programming problem. Finally, the simulation section contains experiments of the execution of three complex tasks including a couple task, the comparison with pseudo-inverse method and robustness verification. Simulation results verify the efficacy and accuracy of the proposed NDSO scheme.

Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties

Redundant design can greatly improve the flexibility of robot manipulators, but may suffer from potential limitations such as system complicity, model uncertainties, physical limitations, which make it challenging to achieve accurate tracking. In this paper, we propose a novel kinematic controller based on a recurrent neural network(RNN) which is competent in model adaption. An identifier which is related to joint velocity and tracking error is designed to learn the kinematic parameters online. In the inner loop, the redundancy resolution is formulated as a quadratic optimization problem, and a RNN is built to obtain the optimal solution recurrently, and the minimum norm of joint velocity is derived as the secondary task. Theoretical analysis demonstrates the global convergence of tracking error. Compared with existing methods, uncertain kinematic model of the robot is allowed in this paper, and pseudo-inverse of Jacobian matrix is avoided, with the consideration of physical limitations in a joint framework. Numerical and actual experiments based on a serial robot Kinova JACO2 show the effectiveness of the proposed controller.

Constrained Whole Body Motion Planning in Task Configuration and Time

Our goal in this research was to develop an efficient motion planning algorithm for a humanoid to enable it to perform complex tasks. To perform the tasks, the humanoid sometimes must be able to generate whole body motion trajectory under the end effector constraints as well as collisions; self-collision and unexpected collision with obstacles in the environment. Also, more complex constraints such as joint limit, joint speed limit would make planning whole body motion more difficult. This constrained redundancy resolution makes the motion planning for humanoid difficult. Although the previous motion planning algorithms are still feasible to find solution, they are typically planning in the high dimensional joint configuration space then finally require high computing. This paper proposes that planning on the specified whole body task space such as pelvis height, chest orientation is able to find feasible solution while satisfying constraints and collision free. We also expect this approach that can be applied on various humanoids so we demonstrated on Atlas and Valkyrie robot. In order to demonstrate the feasibility of the proposed approach, we applied our motion planning algorithm humanoid performing tasks that are proposed by DARPA challenge such as opening a door, closing valve, cutting wall, pulling emergency switch.

Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain-Machine Interfaces.

In this paper, a closed-loop control has been developed for the exoskeleton robot system based on brain-machine interface (BMI). Adaptive controllers in joint space, a redundancy resolution method at the velocity level, and commands that generated from BMI in task space have been integrated effectively to make the robot perform manipulation tasks controlled by human operator's electroencephalogram. By extracting the features from neural activity, the proposed intention decoding algorithm can generate the commands to control the exoskeleton robot. To achieve optimal motion, a redundancy resolution at the velocity level has been implemented through neural dynamics optimization. Considering human-robot interaction force as well as coupled dynamics during the exoskeleton operation, an adaptive controller with redundancy resolution has been designed to drive the exoskeleton tracking the planned trajectory in human brain and to offer a convenient method of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiments which employed a few subjects have been carried out. In the experiments, subjects successfully fulfilled the given manipulation tasks with convergence of tracking errors, which verified that the proposed brain-controlled exoskeleton robot system is effective.