Manipulation planning with Workspace Goal Regions

Abstract

Abstract-We present an approach to path planning for manipulators that uses Workspace Goal Regions (WGRs) to specify goal end-effector poses. Instead of specifying a discrete set of goals in the manipulator's configuration space, we specify goals more intuitively as volumes in the manipulator's workspace. We show that WGRs provide a common framework for describing goal regions that are useful for grasping and manipulation. We also describe two randomized planning algorithms capable of planning with WGRs. The first is an extension of RRT-JT that interleaves exploration using a Rapidly-exploring Random Tree (RRT) with exploitation using Jacobian-based gradient descent toward WGR samples. The second is the IKBiRRT algorithm, which uses a forward-searching tree rooted at the start and a backward-searching tree that is seeded by WGR samples. We demonstrate both simulation and experimental results for a 7DOF WAM arm with a mobile base performing reaching and pick-and-place tasks. Our results show that planning with WGRs provides an intuitive and powerful method of specifying goals for a variety of tasks without sacrificing efficiency or desirable completeness properties.

Controlling a Humanoid Robot Arm for Grasping and Manipulating a Moving Object in the Presence of Obstacles without Cameras

“…Depiction of the RRT-JT algorithm searching in C-space: from the start configuration to (WGRs). The blue regions are obstacles, the forward-searching tree is shown with green nodes [18].…”

“…The researchers who use the visual servoing system and the cameras for grasping moving object find many difficulties to record images, to treat them, because of a lot computing and image processing and also who use the predictive algorithms find a problem in the complexity of Ali Chaabaani, Mohamed Sahbi Bellamine, and Moncef Gasmi Controlling a Humanoid Robot Arm for Grasping and Manipulating a Moving Object in the Presence of Obstacles without Cameras algorithms witch based on many calculated and estimation [18]. In this research we want to grasp a moving object with limited motion velocity.…”

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

“…The classical approach in robotics for reaching to grasp objects has been to divide the overall problem into two sub-problems, where one first reaches for and then grasps the objects [1,20,21]. Although both the issues of reaching to a pre-grasp pose and formation of grasp around arbitrary objects are intensively studied, very few [22][23][24] have looked into combining the two so as to have a unified reach-grasp system.…”

“…LaValle and Kuffner [29] proposed RRT's as a faster alternative to manipulation planning problems, provided the existence of an efficient inverse kinematic (IK) solver. RRT based methods [1] are currently the fastest online planners due to their efficient searching ability. The reported planning times are of the order of 100 ms for single arm reaching tasks in the absence of any obstacles [20].…”

“…Planning and control of constrained grasping motions has often been studied as two separate problems in which one first generates the arm motion [1,2] and then shapes the hand to grasp stably the targeted object [3,4]. The sheer complexity of each of these two problems when controlling high dimensional arm-hand systems has discouraged the use of a single coherent framework for carrying out both tasks simultaneously.…”