Obstacle Avoidance for Kinematically Redundant Manipulators in Dynamically Varying Environments

Abstract

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.

A dynamical system approach to realtime obstacle avoidance

“…However, it is also possible to integrate other algorithms to perform collision avoidance for the whole robot. For example, while the end-effector follows the commanded velocity from the proposed approach, one can use the kinematics null-space to avoid link collision (Maciejewski and Klein, 1985). Furthermore, similarly to (Park et al, 2008), we could also use the presented approach to control the kinematics null-space movement.…”

A unifying framework for robot control with redundant DOFs

“…Therefore, the combination of these tasks into a single large task is not always practical and, instead, the tasks need prioritization, e.g., the higher the number of the task, the higher its priority. Task prioritized control solutions have been discussed in the literature (Nakamura et al 1987;Hollerbach and Suh 1987;Maciejewski and Klein 1985;Hanafusa et al 1981;Yamane and Nakamura 2003;Sentis and Khatib 2004;Siciliano and Slotine 1991;Khatib et al 2004;Sentis and Khatib 2005). Most previous approaches were kinematic and discussed only a small, fixed number of tasks; to our knowledge, Khatib 2004, 2005) were among the first to discuss arbitrary task numbers and dynamical decoupling, i.e., a different metric from our point of view.…”

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

“…The use of kinematically redundant manipulators is expected to increase dramatically in recent years because of their ability to avoid the internal singularity configurations and obstacles (Baker and Wampler 1988;Maciejemski and Klein 1985), and to optimize dynamic performance (Klein and Huang 1983), as well as to conduct the end-effector motion task. The forward kinematics problem of a robot manipulator in robotics is concerned with the transformation of position and orientation information in a joint space to a Cartesian space described by a forward kinematics equation:…”