Joint Physical Constraints Research Articles

Force manipulability-oriented manipulation planning for collaborative robot

PurposeCollaborative robots (cobots) are widely used in various manipulation tasks within complex industrial environments. However, the manipulation capabilities of cobot manipulation planning are reduced by task, environment and joint physical constraints, especially in terms of force performance. Existing motion planning methods need to be more effective in addressing these issues. To overcome these challenges, the authors propose a novel method named force manipulability-oriented manipulation planning (FMMP) for cobots.Design/methodology/approachThis method integrates force manipulability into a bidirectional sampling algorithm, thus planning a series of paths with high force manipulability while satisfying constraints. In this paper, the authors use the geometric properties of the force manipulability ellipsoid (FME) to determine appropriate manipulation configurations. First, the authors match the principal axes of FME with the task constraints at the robot’s end effector to determine manipulation poses, ensuring enhanced force generation in the desired direction. Next, the authors use the volume of FME as the cost function for the sampling algorithm, increasing force manipulability and avoiding kinematic singularities.FindingsThrough experimental comparisons with existing algorithms, the authors validate the effectiveness and superiority of the proposed method. The results demonstrate that the FMMP significantly improves the force performance of cobots under task, environmental and joint physical constraints.Originality/valueTo improve the force performance of manipulation planning, the FMMP introduces the FME into sampling-based path planning and comprehensively considers task, environment and joint physical constraints. The proposed method performs satisfactorily in experiments, including assembly and in situ measurement.

New Jerk-Level Configuration Adjustment Schemes Applied to Constrained Redundant Robots

In this article, the new jerk-level configuration adjustment (JLCA) schemes are proposed to achieve the configuration adjustment of constrained redundant robots. Specifically, by applying the zeroing neurodynamics design rule three times, the new JLCA performance index is first derived; then, together with the joint physical constraints incorporated, the new JLCA schemes are obtained for dual-arm and single-arm redundant robots, respectively. For comparison purposes, three other configuration adjustment schemes are also presented. Moreover, the comparative simulative experiments based on a planar dual-arm redundant robot (i.e., five-link dual-arm robot) and a spatial single-arm redundant robot (i.e., Kinova JACO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> robot) are performed to verify the efficacy and superiority of the proposed JLCA schemes, as compared with the three other configuration adjustment schemes. At last, the comparative physical experiments are conducted on the real Kinova JACO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> robot to substantiate the practicability and excellent performance of the proposed JLCA scheme for single-arm redundant robots.

Acceleration-Level Configuration Adjustment Scheme for Robot Manipulators

In this article, configuration adjustment (CA) for robot manipulators at the joint-acceleration level is presented. Specifically, a new acceleration-level performance index for achieving CA is designed by employing the neurodynamic method. Thus, based on this performance index, and by incorporating joint physical constraints (i.e., joint-configuration, -velocity, and -acceleration limits), a novel acceleration-level CA (ALCA) scheme for robot manipulators is proposed and investigated. The proposed ALCA scheme is transformed into a quadratic program and calculated using a neural network solver. Comparative simulation results obtained with a four-link robot manipulator are presented to substantiate the effectiveness and superiority of the proposed ALCA scheme compared with those of the velocity-level CA scheme. Moreover, simulations and experiments are conducted on a practical Epson robot manipulator to demonstrate the physical realization of the proposed ALCA scheme.

Acceleration-level trajectory planning for a dual-arm space robot

In order to achieve disturbance rejection of the base while realizing the pre-capturing process under joint physical constraints (such as limited joint-angle, joint-velocity, joint-acceleration), this paper proposed an acceleration-level trajectory planning method for a dual-arm space robot. Firstly, the acceleration-level kinematic models are derived by using the velocity-level kinematics and linear and angular momentum conservation law. On this basis, the joint physical constraints are reformulated as a quadratic programming (QP) problem which can be converted to a piecewise-linear projection equation. Meanwhile, the improved damped least-squares (DLS) method is applied to design trajectory planning for the balance arm which is employed to keep the base disturbance free. Finally, simulation results verify the effectiveness of the proposed acceleration-level trajectory planning method.

Tricriteria Optimization-Coordination Motion of Dual-Redundant-Robot Manipulators for Complex Path Planning

To remedy the discontinuity phenomenon existing in the infinity-norm velocity minimization (INVM) scheme, prevent the occurrence of high joint velocity, and decrease the joint-angle drift of redundant robot manipulators, a new type of tricriteria optimization-coordination-motion (TCOCM) scheme is proposed and investigated for dual-redundant-robot manipulators to track complex end-effector paths. Besides, the proposed scheme considers joint physical constraints (i.e., joint-angle limits and joint-velocity limits) and guarantees the joint velocity to approach zero at the end of tasks. Such a new-type TCOCM scheme combines the minimum velocity norm (MVN), repetitive motion planning (RMP), and INVM solutions via two weighting factor, and thus is termed MVN-RMP-INVM-TCOCM scheme. The proposed scheme consists of two subschemes, i.e., subscheme for the left robot manipulator and subscheme for the right robot manipulator. To control the dual arms simultaneously and collaboratively, two subschemes are reformulated as two general quadratic programming (QP) problems and further unified into one QP formulation. The unified QP problem is then solved by a simplified linear-variational-inequalities-based primal–dual neural network solver. Computer simulation results based on dual PUMA560 robot manipulators are illustrated to substantiate the advantage, efficacy, and applicability of the proposed MVN-RMP-INVM-TCOCM scheme to resolve the redundancy of dual-robot manipulators.

A Unified Approach for Second-Order Control of the Manipulator With Joint Physical Constraints

Kinematic control of manipulators with joint physical constraints, such as joint limits and joint velocity limits, has received extensive studies. Many studies resolved this problem at the second-order kinematic level, which may suffer from the self-motion instability in the presence of persistent self-motion or unboundedness of joint velocity. In this paper, a unified approach is proposed to control a manipulator with both joint limits and joint velocity limits at the second-order kinematic level. By combining the weighted least-norm (WLN) solution in the revised joint space and the clamping weighted least-norm (CWLN) solution in the real joint space, the unified approach ensures the joint limits and joint velocity limits at the same time. A time-variant clamping factor is incorporated into the unified approach to suppress the self-motion when the joint velocity diverges, or the end-effector stops, which improves the stability of self-motion. The simulations in contrast to the traditional dynamic feedback control scheme and the new minimum-acceleration-norm (MAN) scheme have been made to demonstrate the advantages of the unified approach.

Inverse kinematics of a 7-DOF redundant robot manipulator using the active set approach under joint physical limits

This paper presents a new approach for an online solution of the inverse kinematics problem based on nonlinear optimization for robots with joint physical constraints. The inverse kinematics problem is stated as a constrained nonlinear optimization problem and is solved using Kuhn--Tucker conditions analysis. The nonlinear multivariable optimization problem is locally converted into independent local linear constrained subproblems and each subproblem is analytically solved. For each joint, position limits and velocity limits are considered as physical constraints. The proposed structure benefits from a very low complexity design. The proposed method is fast and requires few calculations. The convergence of the proposed algorithm is proven based on the Lyapunov function. While keeping the algorithm stable, it can navigate the manipulator to a desired position under joint physical limits. The algorithm is simulated on a 7-DOF PA-10 manipulator. Results indicate good efficiency for the proposed structure in constrained path planning of joint space.

Acceleration-Level Inequality-Based MAN Scheme for Obstacle Avoidance of Redundant Robot Manipulators

In this paper, a new inequality-based criterion is proposed and investigated for obstacle avoidance of redundant robot manipulators at the joint-acceleration level. By incorporating such a dynamically updated inequality criterion and the joint physical constraints (i.e., joint-angle limits, joint-velocity limits, and joint-acceleration limits), a novel minimum-acceleration-norm (MAN) scheme is presented and investigated for robots' redundancy resolution. In addition, the resultant obstacle-avoidance MAN scheme resolved at the joint-acceleration level is reformulated as a general quadratic program (QP). Moreover, two important “Bridge” theorems are established, which show that such a QP problem can be equivalent to linear variational inequality (LVI) and then to piecewise-linear projection equation (PLPE). An LVI-based numerical method is thus developed and applied for online solution of the QP problem and the inequality-based obstacle-avoidance MAN scheme. Simulative results based on the PA10 robot manipulator in the presence of window-shaped and point obstacles further demonstrate the efficacy and superiority of the proposed acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators. In addition, experimental verification conducted on a practical six-link planar robot manipulator substantiates the effectiveness and physical realizability of the proposed obstacle-avoidance scheme.

Simulation and Experimental Verification of Weighted Velocity and Acceleration Minimization for Robotic Redundancy Resolution

This paper proposes and investigates a weighted velocity and acceleration minimization scheme to prevent the occurrence of high joint velocity and joint acceleration caused by the minimum acceleration norm (MAN) scheme in redundant robot manipulators. The proposed scheme considers minimum kinetic energy (MKE) and MAN criterions via two weighting factors, thus guaranteeing the final joint velocity of motion to be near zero, which is acceptable for engineering applications. Joint physical constraints (i.e., joint angle limits, joint velocity limits, and joint acceleration limits) are incorporated in the formulation of the proposed scheme. The proposed scheme is reformulated as a quadratic program and then calculated by using a numerical algorithm based on linear variational inequality. Computer simulation results of a PUMA560 robot manipulator verify the efficacy and flexibility of the proposed scheme for redundancy resolution in robot manipulators. Experimental verifications conducted on a six-link planar robot manipulator demonstrate the effectiveness and physical realizability of the proposed scheme.

Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

For avoiding obstacles and joint physical constraints of robot manipulators, this paper proposes and investigates a novel obstacle avoidance scheme (termed the acceleration-level obstacle-avoidance scheme). The scheme is based on a new obstacle-avoidance criterion that is designed by using the gradient neural network approach for the first time. In addition, joint physical constraints such as joint-angle limits, joint-velocity limits and joint-acceleration limits are incorporated into such a scheme, which is further reformulated as a quadratic programming (QP). Two important ‘bridge’ theorems are established so that such a QP can be converted equivalently to a linear variational inequality and then equivalently to a piecewise-linear projection equation (PLPE). A numerical algorithm based on a PLPE is thus developed and applied for an online solution of the resultant QP. Four path-tracking tasks based on the PA10 robot in the presence of point and window-shaped obstacles demonstrate and verify the effectiveness and accuracy of the acceleration-level obstacle-avoidance scheme. Besides, the comparisons between the non-obstacle-avoidance and obstacle-avoidance results further validate the superiority of the proposed scheme.

A New Inequality-Based Obstacle-Avoidance MVN Scheme and Its Application to Redundant Robot Manipulators

This paper proposes a new inequality-based criterion/constraint with its algorithmic and computational details for obstacle avoidance of redundant robot manipulators. By incorporating such a dynamically updated inequality constraint and the joint physical constraints (such as joint-angle limits and joint-velocity limits), a novel minimum-velocity-norm (MVN) scheme is presented and investigated for robotic redundancy resolution. The resultant obstacle-avoidance MVN scheme resolved at the joint-velocity level is further reformulated as a general quadratic program (QP). Two QP solvers, i.e., a simplified primal-dual neural network based on linear variational inequalities (LVI) and an LVI-based numerical algorithm, are developed and applied for online solution of the QP problem as well as the inequality-based obstacle-avoidance MVN scheme. Simulative results that are based on PA10 robot manipulator and a six-link planar robot manipulator in the presence of window-shaped and point obstacles demonstrate the efficacy and superiority of the proposed obstacle-avoidance MVN scheme. Moreover, experimental results of the proposed MVN scheme implemented on the practical six-link planar robot manipulator substantiate the physical realizability and effectiveness of such a scheme for obstacle avoidance of redundant robot manipulator.