Simultaneous Dual-Arm Motion Planning for Minimizing Operation Time

Abstract

Dual-arm robots are expected to perform work in a dynamic environment. One of the most basic tasks that a dual-arm robot does is pick-and-place work. However, this work is more complicated when there are several objects in the robot's workspace. Additionally, it is likely to take a long time to finish the work as the number of objects increases. Therefore, we propose a method using a combination of two approaches to achieve efficient pick-and-place performance by a dual-arm robot to minimize its operation time. First, we use mixed integer linear programming (MILP) for the pick-and-place work to determine which arm should move an object and in which order these objects should be moved while considering the dual-arm robot's operation range. Second, we plan the path using the rapidly exploring random tree so that the arms do not collide, enabling the robot to perform efficient pick-and-place work based on the MILP planning solution. The effectiveness of the proposed method is confirmed by simulations and experiments using an actual dual-arm robot.

Combining Task and Motion Planning: Challenges and Guidelines

“…In addition, different assignments of sub-tasks to arms, while taking the individual working ranges into account as well as task steps in which the arms have to cooperate, lead to a further combinatorial complexity. The assembly planning problem was addressed in the literature via different methods, including prioritized TAMP ( Kurosu et al, 2017 ), fixed-path planning ( O’Donnell and Lozano-Pérez, 1989 ), and fixed-roadmap planning. For this paper, we analyze the latter approach, which uses a flexible model and solver for simultaneous task allocation and motion scheduling that is based on constraint programming (CP) and constraint optimization ( Behrens et al, 2019a ).…”

A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

“…LaValle [17] formulates the motion scheduling problem in a joint configuration space. Prioritized planning assigns an order to the robots (arms) according to which their movements are planned (e.g., [15]). In fixed-path planning -also referred to as time-scaling -only timings are adjusted to prevent collisions [23].…”

“…Conversely, we create dense roadmaps to enable the close coordination of arms, thus allow more parallel movements of arms. Kurosu et al [15] describe a decoupled MILP-based approach to solve a STAAMS, where the motion planner is prone to fail due to simplified motion and cost models used in the one-shot MILP formulation. This is not the case for us, as a single CP solver finds a mutually feasible solution for all sub-problems.…”

Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot

“…During this process, it must be ensured that the manipulator does not collide with any obstacle in the workspace or itself. Existing motion planning algorithms include rapidly random-exploring tree (RRT) [12][13][14][15], probabilistic roadmap method (PRM) [16,17], A* [18], neural network [19], and artificial potential field (APF) [20][21][22][23]. RRT is the most popular algorithm among scientists all over the world to process motion planning problems, and the related algorithms include RRT*, Informed-RRT, RRT-Connect, and so on.…”