A unified framework for coordinated multi-arm motion planning

Author:

Mirrazavi Salehian Seyed Sina1,Figueroa Nadia1,Billard Aude1

Affiliation:

1. Learning Algorithms and Systems Laboratory (LASA), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

Abstract

Coordination is essential in the design of dynamic control strategies for multi-arm robotic systems. Given the complexity of the task and dexterity of the system, coordination constraints can emerge from different levels of planning and control. Primarily, one must consider task-space coordination, where the robots must coordinate with each other, with an object or with a target of interest. Coordination is also necessary in joint space, as the robots should avoid self-collisions at any time. We provide such joint-space coordination by introducing a centralized inverse kinematics (IK) solver under self-collision avoidance constraints, formulated as a quadratic program and solved in real-time. The space of free motion is modeled through a sparse non-linear kernel classification method in a data-driven learning approach. Moreover, we provide multi-arm task-space coordination for both synchronous or asynchronous behaviors. We define a synchronous behavior as that in which the robot arms must coordinate with each other and with a moving object such that they reach for it in synchrony. In contrast, an asynchronous behavior allows for each robot to perform independent point-to-point reaching motions. To transition smoothly from asynchronous to synchronous behaviors and vice versa, we introduce the notion of synchronization allocation. We show how this allocation can be controlled through an external variable, such as the location of the object to be manipulated. Both behaviors and their synchronization allocation are encoded in a single dynamical system. We validate our framework on a dual-arm robotic system and demonstrate that the robots can re-synchronize and adapt the motion of each arm while avoiding self-collision within milliseconds. The speed of control is exploited to intercept fast moving objects whose motion cannot be predicted accurately.

Funder

EU project Cogimon

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software

Cited by 65 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Comparison of Imitation Learning Algorithms for Bimanual Manipulation;IEEE Robotics and Automation Letters;2024-10

2. Globally Optimal Inverse Kinematics as a Non-Convex Quadratically Constrained Quadratic Program;IEEE Robotics and Automation Letters;2024-06

3. Obstacle Avoidance of Manipulator Based on Artificial Potential Field in Joint Space;2024 36th Chinese Control and Decision Conference (CCDC);2024-05-25

4. A Method for Multi-Robot Asynchronous Trajectory Execution in MoveIt2;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

5. Learning Complex Motion Plans using Neural ODEs with Safety and Stability Guarantees;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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