Abstract
AbstractThis paper presents a new approach for geometrically constrained path planning applied to the field of robotic grasping. The method proposed in this paper is based on the Fast Marching Square (FM $\, ^2$ ) and a path calculation approach based on an optimization evolutionary filter named Differential Evolution (DE). The geometric restrictions caused by the link lengths of the kinematic chain composed by the robot arm and hand are introduced in the path calculation phase. This phase uses both the funnel potential of the surroundings created with FM $\, ^2$ and the kinematic constraints of the robot as cost functions to be minimized by the evolutionary filter. The use of an optimization filter allows for a near-optimal solution that satisfies the kinematic restrictions, while preserving the characteristics of a path computed with FM $\, ^2$ . The proposed method is tested in a simulation using a robot composed by a mobile base with two arms.
Highlights
Path planning is a problem that has received a lot of attention in recent years
This paper presents a new approach for geometrically constrained path planning applied to the field of robotic grasping
The method proposed in this paper is based on the Fast Marching Square (FM 2) and a path calculation approach based on an optimization evolutionary filter named Differential Evolution (DE)
Summary
Path planning is a problem that has received a lot of attention in recent years. It consists of finding an obstacle-free trajectory from an initial configuration to a final configuration [1]. Decoupled approaches solve the problem by following a series of consecutive steps, such as computing a collision-free path neglecting the constraints and smoothing it to satisfy the kinematic constraints and make the path feasible for the robot. The algorithms proposed in this paper are able to plan the paths for the arm-hand set, while avoiding and keeping a safe distance from obstacles, and following the kinematic constraints of the chain. This allows for planning grasping tasks in cluttered environments.
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