Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem (original) (raw)

Kinematic control of redundant robots with guaranteed joint limit avoidance

Robotics and Autonomous Systems, 2016

h i g h l i g h t s • A novel kinematic control signal guaranteeing joint limit avoidance is proposed. • In sensor driven tasks it generates feasible paths to the target. • With planned task trajectories it can act as a null-space velocity. • Smooth joint trajectories and accurate target reaching are achieved. • Experimental results with a KUKA LWR4+ manipulator demonstrate its performance.

On null-space control of kinematically redundant robot manipulators

2016 European Control Conference (ECC), 2016

In this study, we consider the null-space control problem of redundant robot manipulators. Specifically for robot manipulators with kinematically redundancy where at least one extra degree of freedom is present, we introduce a sub-task controller that will ensure the use of the extra degrees of freedom for possible control purposes while still ensuring the main objective. The stability of the main (end-effector tracking) and sub-task objectives are obtained via Lyapunov based arguments. Extension to adaptive controller formulation for robotic devices with uncertain system dynamics is also presented. Numerical studies for the adaptive controller are presented to illustrate the liability of the proposed method.

Kinematic control algorithms for on-line obstacle avoidance for redundant manipulators

2003

The paper deals with kinematic control algorithms for on-line obstacle avoidance which allow a kinematically redundant manipulator to move in an unstructured environment without colliding with obstacles. The presented approach is based on the redundancy resolution at the velocity level. The primary task is determined by the end-effector trajectories and for the obstacle avoidance the internal motion of the manipulator is used. The obstacle avoiding motion is defined in onedimensional operational space and hence, the system has less singularities what makes the implementation easier. Instead of the exact pseudoinverse solution we propose an approximate one which is computationally more efficient and allows also to consider many simultaneously active obstacles without any problems. The fast cycle times of the numerical implementation enable to use the algorithm in real-time control. For illustration some simulation results of highly redundant planar manipulator moving in an unstructured and time-varying environment and experimental results of a four link planar manipulator are given.

Smooth Transitions in Trajectory Profiles for Redundant Robots Performing Secondary Tasks

2012

The kinematics model for redundant robot can be based in a classical solution using Denavit-Hartanberg or, more recently, using the screw theory. The model based on screw theory associated with Davies method for redundant robot results in set of solutions which is always dimensional consistent; the same cannot be said from solutions bases on Moore-Penrose pseudoinverse Jacobians. More recently, some works have shown the use of virtual chains to construct a strategy of trajectory generation for redundant robots satisfying an additional task: avoidance collision. However the proposed solution has problems for high joint velocities movements. This disadvantage is characterized by a discontinuity on velocities and aceleration funtions when the robot is under imminence of a collision. This paper proposes an algorithm to smooth the transitions of joint velocities and accelerations. The proposed method is based on polynomial functions. The proposed solution is verified in a P 3R redundant manipulator.

Motion Control of Redundant Robots under Joint Constraints: Saturation in the Null Space

We present a novel efficient method addressing the inverse differential kinematics problem for redundant manipulators in the presence of different hard bounds (joint range, velocity, and acceleration limits) on the joint space motion. The proposed SNS (Saturation in the Null Space) iterative algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution and reintroducing them at a saturated level by means of a projection in a suitable null space. The method is first defined at the velocity level and then moved to the acceleration level, so as to avoid joint velocity discontinuities due to the switching of saturated joints. Moreover, the algorithm includes an optimal task scaling in case the desired task trajectory is unfeasible under the given joint bounds. We also propose the integration of obstacle avoidance in the Cartesian space by properly modifying on line the joint bounds. Simulation and experimental results reported for the 7-dof lightweight KUKA LWR IV robot illustrate the properties and computational efficiency of the method.

Obstacle Avoidance for Kinematically Redundant Manipulators in Dynamically Varying Environments

The International Journal of Robotics Research, 1985

The vast majority of work to date concerned with obstacle avoidance for manipulators has dealt with task descriptions in the form ofpick-and-place movements. The added flexibility in motion control for manipulators possessing redundant degrees offreedom permits the consideration of obstacle avoidance in the context of a specified end-effector trajectory as the task description. Such a task definition is a more accurate model for such tasks as spray painting or arc welding. The approach presented here is to determine the required joint angle rates for the manipulator under the constraints of multiple goals, the primary goal described by the specified end-effector trajectory and secondary goals describing the obstacle avoidance criteria. The decomposition of the solution into a particular and a homogeneous component effectively illustrates the priority of the multiple goals that is exact end-effector control with redundant degrees of freedom maximizing the distance to obstacles. An efficient numerical implementation of the technique permits sufficiently fast cycle times to deal with dynamic environments.

Prioritized multi-task motion control of redundant robots under hard joint constraints

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

We present an efficient method for motion control of redundant robots performing multiple prioritized tasks in the presence of hard bounds on joint range, velocity, and acceleration/torque. This is an extension of our recently proposed SNS (Saturation in the Null Space) algorithm developed for single tasks. The method is defined at the level of acceleration commands and proceeds by successively discarding one at a time the commands that would exceed their bounds for a task of given priority, and reintroducing them at their saturated levels by projection in the null space of a suitable Jacobian associated to the already considered tasks. When processing all tasks in their priority order, a correct preemptive strategy is realized in this way, i.e., a task of higher priority uses in the best way the feasible robot capabilities it needs, while lower priority tasks are accommodated with the residual capability and do not interfere with the execution of higher priority tasks. The algorithm automatically integrates a multi-task least possible scaling strategy, when some ordered set of original tasks is found to be unfeasible. Simulation and experimental results on a 7-dof lightweight KUKA LWR IV robot illustrate the good performance of the method.

Control of Redundant Robots Under Hard Joint Constraints: Saturation in the Null Space

IEEE Transactions on Robotics, 2015

We present an efficient method for addressing online the inversion of differential task kinematics for redundant manipulators, in the presence of hard limits on joint space motion that can never be violated. The proposed Saturation in the Null Space (SNS) algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution. When processing multiple tasks with priority, the SNS method realizes a preemptive strategy by preserving the correct order of priority in spite of the presence of saturations. In the single-and multitask case, the algorithm automatically integrates a least possible task-scaling procedure, when an original task is found to be unfeasible. The optimality properties of the SNS algorithm are analyzed by considering an associated quadratic programming problem. Its solution leads to a variant of the algorithm, which guarantees optimality even when the basic SNS algorithm does not. Numerically efficient versions of these algorithms are proposed. Their performance allows real-time control of robots executing many prioritized tasks with a large number of hard bounds. Experimental results are reported.

Kinematic Control and Obstacle Avoidance for Redundant Manipulators Using a Recurrent Neural Network

Lecture Notes in Computer Science, 2001

In this paper, a recurrent neural network called the Lagrangian network is applied for obstacle avoidance in kinematically redundant manipulators. Conventional numerical methods implemented in digital computers for obstacle avoidance redundancy resolution calculation could only compute the solution in milliseconds while neural network realized by hardware could complete the computation in microseconds, which is more desirable in real-time control of manipulators. By giving the desired end-effector velocities and the obstacle location, the neural network could generate the joint velocity vector which drives the manipulator to avoid obstacles and tracks the desired end-effector trajectory simultaneously. Simulation results show that the neural network is capable of computing the redundancy resolution for obstacle avoidance.