Coordinated navigation of multi-robot systems with binary constraints

Brüggemann, Bernd; Schulz, Dirk

doi:10.1109/iros.2010.5652679

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…We generally want to focus on multiple-query roadmaps because they can be pre-computed based on a given environment. The usage of PRMs for multiple agents was considered by [4,11,20]. Where [4] uses separate instances of the same roadmap for the agents and an additional graph to represent constraints between them.…”

Section: Related Workmentioning

confidence: 99%

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Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

Henkel

Toussaint

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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We present a novel approach called Optimized Directed Roadmap Graph (ODRM). It is a method to build a directed roadmap graph that allows for collision avoidance in multi-robot navigation. This is a highly relevant problem, for example for industrial autonomous guided vehicles. The core idea of ODRM is, that a directed roadmap can encode inherent properties of the environment which are useful when agents have to avoid each other in that same environment. Like Probabilistic Roadmaps (PRMs), ODRM's first step is generating samples from C-space. In a second step ODRM optimizes vertex positions and edge directions by Stochastic Gradient Descent (SGD). This leads to emergent properties like edges parallel to walls and patterns similar to two-lane streets or roundabouts. Agents can then navigate on this graph by searching their path independently and solving occurring agent-agent collisions at run-time. Using the graphs generated by ODRM compared to an non-optimized graph significantly fewer agent-agent collisions happen.We evaluate our roadmap with both, centralized and decentralized planners. Our experiments show that with ODRM even a simple centralized planner can solve problems with high numbers of agents that other multi agent planners can not solve. Additionally, we use simulated robots with decentralized planners and online collision avoidance to show how agents are a lot faster on our roadmap than on standard grid maps.

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Section: Related Workmentioning

confidence: 99%

“…The usage of PRMs for multiple agents was considered by [4,11,20]. Where [4] uses separate instances of the same roadmap for the agents and an additional graph to represent constraints between them.…”

Section: Related Workmentioning

confidence: 99%

Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

Henkel

Toussaint

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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“…In [45,46], the authors proposed a method for navigating a multi-robot system through an environment while obeying spatial constraints. Mohanarajah et al [47] presented architecture, protocol, and parallel algorithms for collaborative 3D mapping in the cloud with low-cost robots.…”

Section: Related Workmentioning

confidence: 99%

3D Reconstruction Framework for Multiple Remote Robots on Cloud System

et al. 2017

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This paper proposes a cloud-based framework that optimizes the three-dimensional (3D) reconstruction of multiple types of sensor data captured from multiple remote robots. A working environment using multiple remote robots requires massive amounts of data processing in real-time, which cannot be achieved using a single computer. In the proposed framework, reconstruction is carried out in cloud-based servers via distributed data processing. Consequently, users do not need to consider computing resources even when utilizing multiple remote robots. The sensors' bulk data are transferred to a master server that divides the data and allocates the processing to a set of slave servers. Thus, the segmentation and reconstruction tasks are implemented in the slave servers. The reconstructed 3D space is created by fusing all the results in a visualization server, and the results are saved in a database that users can access and visualize in real-time. The results of the experiments conducted verify that the proposed system is capable of providing real-time 3D scenes of the surroundings of remote robots.

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“…The need of robots as relay station can be viewed in a more general way to allow additional constraints besides communication. A centralized planner for such constrained navigation problems can be found in (Brüggemann and Schulz, 2010). Such an application leads to a different kind of global plan, the constrained multi-robot global plan.…”

Section: Global Plansmentioning

confidence: 99%

From a Multi-robot Global Plan to Single-robot Actions

Brueggemann¹,

Langetepe²,

Lenerz³

et al. 2012

Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics

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Planning for and coordinating robots in a multi-robot system (MRS) is crucial for optimizing the performance of the whole MRS. Thus a plan for the movement of the MRS must exist. If some centralized entity calculates the plan, it may result in one plan for the whole group. Such a global plan, which shows how the MRS can reach a goal state, has to be transformed into action guidelines for each robot. This task becomes harder if such a global plan includes dependencies caused by necessary cooperation of the robots. In this paper we present an approach to transform a global multi-robot plan into single-robot actions. We also provide a method to determine how many robots are needed to fulfil the global plan while obeying some constraints. Here we use plans generated by a coordinated navigation planner with spatial constraints, but the method could be expanded to a more general class of plans built from a centralized entity.

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Coordinated navigation of multi-robot systems with binary constraints

Cited by 8 publications

References 8 publications

Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

3D Reconstruction Framework for Multiple Remote Robots on Cloud System

From a Multi-robot Global Plan to Single-robot Actions

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