Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning

Xin, Jianbin; Qu, Yingming; Zhang, Fangfang; Negenborn, Rudy R.

doi:10.23919/csms.2022.0017

Cited by 4 publications

(2 citation statements)

References 32 publications

(40 reference statements)

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“…In the past few decades, the Robot Operating System (ROS) framework has been recognized as one of the most popular component-based software development frameworks in robotics [11,16,17] . It creatively solves heterogeneity by reusing software components and robotic algorithms across diverse robotic hardware platforms, establishing the defacto software development standard in robotic communities.…”

Section: Related Workmentioning

confidence: 99%

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

Yang,

Zhang

2023

Complex Syst. Model. Simul.

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Nowadays, autonomous robots are expected to accomplish more complex tasks and operate in an open-world environment with uncertainties. Developing software for such robots involves the design of task planning paradigms and the implementation of robotic software architectures, making software development rather tricky and time-consuming. In recent decades, component-based software development approaches have been increasingly adopted in robotics to improve software development efficiency by reusing data and controlling flows between components. However, few works have tackled the more critical issue of reusing complex high-level task planning paradigms and robotic software architectures. To make up for the limitation, this paper first identifies the mainstream task planning paradigms and proposes a set of novel patterns for interaction pipelines between the robotic functions of sensing, planning, and acting. Then this paper presents a novel Behavior Tree (BT) based development framework Structural-BT, which provides a set of reusable BT structures that implement abstract interaction pipelines while maintaining interfaces for task-specific customization. The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components. With the Structural-BT framework, software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary. In the experiment, this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System (ROS) and classical BT development frameworks for comparison. By quantitatively measuring the reuse frequencies and ratios of BT structures, the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.

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Section: Related Workmentioning

confidence: 99%

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

Yang,

Zhang

2023

Complex Syst. Model. Simul.

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“…In multi-robot systems, existing motion planning algorithms often lack addressing for poor coordination and increasing low real-time performance. In such cases, model predictive Introductory Chapter: Motion Planning for Dynamic Agents DOI: http://dx.doi.org/10.5772/intechopen.1003898 contouring control (MPCC) allows separating the tracking accuracy and productivity for modifying productivity [11]. The MPCC allows every single robot to exchange other robots' predicted paths while generating collision-free motion parallelly.…”

Section: Introductionmentioning

confidence: 99%

Introductory Chapter: MOTION PLANNING FOR DYNAMIC AGENTS

Anwar Ali

2024

Motion Planning for Dynamic Agents

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Long Short‐Term Memory‐Based Multi‐Robot Trajectory Planning: Learn from MPCC and Make It Better

Xin,

Xu,

Zhu

et al. 2024

Advanced Intelligent Systems

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The current trajectory planning methods for multi‐robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short‐term memory (LSTM) networks for real‐time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar‐based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi‐robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi‐robot trajectory planning in logistics transportation tasks.

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Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning

Cited by 4 publications

References 32 publications

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

Introductory Chapter: MOTION PLANNING FOR DYNAMIC AGENTS

Long Short‐Term Memory‐Based Multi‐Robot Trajectory Planning: Learn from MPCC and Make It Better

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