Task-Space Decomposed Motion Planning Framework for Multi-Robot Loco-Manipulation

Zhang, Xiaoyu; Yan, Lei; Lam, Tin Lun; Vijayakumar, Sethu

doi:10.1109/icra48506.2021.9560902

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…It solves a complicated global path-planning problem by decomposing it into simpler local path-planning problems. Zhang et al [ 35 ] proposed task-space-decomposed motion planning. The constrained manifold can be easily solved by a dual-resolution motion planning framework consisting of a global planner and a local planner.…”

Section: Related Workmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

Eoh

2023

Sensors

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This paper presents a novel object transportation method using deep reinforcement learning (DRL) and the task space decomposition (TSD) method. Most previous studies on DRL-based object transportation worked well only in the specific environment where a robot learned how to transport an object. Another drawback was that DRL only converged in relatively small environments. This is because the existing DRL-based object transportation methods are highly dependent on learning conditions and training environments; they cannot be applied to large and complicated environments. Therefore, we propose a new DRL-based object transportation that decomposes a difficult task space to be transported into simple multiple sub-task spaces using the TSD method. First, a robot sufficiently learned how to transport an object in a standard learning environment (SLE) that has small and symmetric structures. Then, a whole-task space was decomposed into several sub-task spaces by considering the size of the SLE, and we created sub-goals for each sub-task space. Finally, the robot transported an object by sequentially occupying the sub-goals. The proposed method can be extended to a large and complicated new environment as well as the training environment without additional learning or re-learning. Simulations in different environments are presented to verify the proposed method, such as a long corridor, polygons, and a maze.

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Section: Related Workmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

Eoh

2023

Sensors

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“…In such scenarios, task or restricted space regions of the end-effector are useful for the manipulators to plan within the constraints [15] [16]. In [17], the global planner first explored the low-dimensional decomposed task space regions of each robot, and then the local planner computed paths in the high-dimensional constrained configuration space. Another type is to consider the overall configuration of the system.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning of Manipulator by Points-Guided Sampling Network

Lyu

Liu

Wang

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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Multiple robot systems are favored for object manipulation and transportation, especially for large objects. However, in more complex manipulation such as flipping, these systems encounter a new challenge, configuration disconnectivity of manipulators. Grasping objects by manipulators will impose closedchain constraints on the system, which in turn limits the feasible motions of manipulators and further compromises the configuration connectivity. Multiple mobile manipulator systems show much more flexibility in object manipulation with the mobility of the mobile platform and have the potential to address the above problem. In this paper, a novel planning framework is proposed for complex flipping manipulation by incorporating platform motions and regrasping. Firstly, two types of trajectories, mobile manipulator planning and regrasping planning, are classified and can be assigned different priorities for different tasks. Secondly, corresponding planning methods are designed for each type of trajectory. Specifically, in mobile manipulator planning, the configuration of the platform is determined through optimization to ensure connectivity when the manipulator approaches configuration boundaries. In regrasping planning, closed-chain constraints are temporarily disregarded and the manipulation capabilities are prioritized to facilitate subsequent planning. Finally, the structure of the overall planning framework is provided. Experimental results demonstrate that the proposed planner efficiently plans the motions of the system to accomplish flipping manipulation. Additionally, a comprehensive experiment emphasizes the significance of our planner in extending the capabilities of multiple mobile manipulator systems in complex tasks.

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Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots Using Coupling Dynamic Movement Primitives

Zhang

Lei

et al. 2022

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)

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Task-Space Decomposed Motion Planning Framework for Multi-Robot Loco-Manipulation

Cited by 6 publications

References 25 publications

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

Motion Planning of Manipulator by Points-Guided Sampling Network

Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots Using Coupling Dynamic Movement Primitives

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