A Hybrid Task-Constrained Motion Planning for Collaborative Robots in Intelligent Remanufacturing

TL;DR

An efficient hybrid motion planning algorithm that consists of an A$^* algorithm and an online manipulator reconfiguration mechanism to tackle challenges in task and configuration spaces respectively is proposed.

Abstract

Industrial manipulators have extensively collaborated with human operators to execute tasks, e.g., disassembly of end-of-use products, in intelligent remanufacturing. A safety task execution requires real-time path planning for the manipulator's end-effector to autonomously avoid human operators. This is even more challenging when the end-effector needs to follow a planned path while avoiding the collision between the manipulator body and human operators, which is usually computationally expensive and limits real-time application. This paper proposes an efficient hybrid motion planning algorithm that consists of an A$^*$ algorithm and an online manipulator reconfiguration mechanism (OMRM) to tackle such challenges in task and configuration spaces respectively. The A$^*$ algorithm is first leveraged to plan the shortest collision-free path of the end-effector in task space. When the manipulator body is risky to the human operator, our OMRM then selects an alternative joint configuration with minimum reconfiguration effort from a database to assist the manipulator to follow the planned path and avoid the human operator simultaneously. The database of manipulator reconfiguration establishes the relationship between the task and configuration space offline using forward kinematics, and is able to provide multiple reconfiguration candidates for a desired end-effector's position. The proposed new hybrid algorithm plans safe manipulator motion during the whole task execution. Extensive numerical and experimental studies, as well as comparison studies between the proposed one and the state-of-the-art ones, have been conducted to validate the proposed motion planning algorithm.

Figures (7)

• Figure 1: The potential collision scene: the manipulator motion is introduced in the left-down corner of the figure, the orientations of the end-effector in the four manipulator states are the same, and we focus on the motion from state B to state C. The green line is the planned collision-free path, the yellow box is the obstacle.
• Figure 2: The framework of solving the formulated optimization problem, where the green dots represent the joint configuration candidates corresponding to $x_k$
• Figure 3: The structure of the reconfiguration database: each $\Theta^+$ stands for the set of configuration candidates that leads to the same $x$ in the task space.
• Figure 4: Planning simulation results: the blue dot is the start position, the red dot is the target position, the green box represents the obstacle.
• Figure 5: UR5e motion planning experimental test scenario A: Sub-figure (a) is the initial frame of the task execution by the manipulator, and the red line is the end-effector's path planned by the A* algorithm. A sudden intervention occurs with the appearance of the human arm, blocking the manipulator's intended movement, as shown in (b). The green circle indicates the potential collision on the manipulator link. The manipulator changes its configuration and moves below the human arm to avoid the collision, as shown in (c). A short video of the experimental tests is available via this https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2024/06/OMRM_supplemental_videos.mp4 and in the supplemental materials.