Decentralized multi-vehicle path coordination under communication constraints

Abichandani, Pramod; Benson, Hande Y.; Kam, Moshe

doi:10.1109/iros.2011.6048822

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Different methods can be used to linearize (17). For instance, [1] considers the use of a collection of tangential planes. Here we follow the method that we have developed in [18], where we use linear regression based on linear leastsquares fitting.…”

Section: A: Euclidean-based Formulationmentioning

confidence: 99%

“…A typical mission can be conveniently represented in terms of a set of tasks where tasks can have dependencies, priorities, time windows, utilities, costs, and so on. Traditionally, the concept of task is related to the actions that agents execute, 1 Henceforth we use the term agent and robot interchangeably without attaching to it a notion of spatial locality. Instead, in this work we emphasize that in many mission scenarios of practical interest, tasks are related to specific spatial locations, where the task 'resides' and must be serviced.…”

Section: Introductionmentioning

confidence: 99%

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Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams

Flushing¹,

Gambardella²,

Di³

2021

IEEE Access

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This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.

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Section: A: Euclidean-based Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams

Flushing¹,

Gambardella²,

Di³

2021

IEEE Access

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“…The coordination of motion of multiple robotic vehicles in a shared workspace so that they avoid collisions is known as Multi-Vehicle Path Coordination (MVPC). In previous work, they reported on simulation and experimental validation of a real-time Receding Horizon Mixed Integer Non-linear Programming -based optimization framework that achieves decentralized MVPC under communication constraints [97][98][99]. Here, they extend their work to autonomous outdoor quadcopters by explicitly enforcing spatial formation constraints, while maintaining a connected Mobile AdHoc Network (MANET).…”

Section: Model Predictive Formation Controlmentioning

confidence: 99%

Recent Advances in Formations of Multiple Robots

Cohen

Agmon

2021

Curr Robot Rep

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Purpose of Review Formation control is a canonical problem in multi-robot systems, which focuses on the ability of a group of robots to travel in coordination through an area, while maintaining a certain shape or a particular behavior. The robot groups vary in their communication, computation, and sensing capabilities. Moreover, the formation control task itself may have various objectives. These divergences force the use of different models for controlling the formation and for analyzing the task performance. In this paper, we describe the formation control problem and survey recent advances focusing on aspects of maintaining a formation by a group of robots distinguished by the means of analysis.Recent Findings Various approaches may be applied for the sake of formation maintenance, whereas each approach possesses a different perspective in regard with formation control. Recent research focuses on combining those approaches, due to their applicability regarding certain scenarios. For instance, consensus-based control and collision avoidance are usually intertwined together for the sake of reaching a consensus in a manner which is collision-free. Furthermore, machine learning (ML)-based methods for navigating a robot team through unknown complex environments can be incorporated, where the robot team aims to reach a goal position while avoiding collisions and maintaining connectivity. Moreover, recent approaches focus on developing new mechanisms or adapt existing ones for formation control for tolerating limitations in sensing, communication, and coordination, preferably distributively while providing performance guarantees. ConclusionSuch combined approaches yield that the means of analysis, which can be applied to each one separately, can also be utilized in an intertwined manner, and thus provide us with novel methods for preserving formation. Whereas some approaches were vastly investigated (e.g., consensus-based formation control) and need to be adapted to distributed imperfect settings, others still require further insight for unveiling brand new architectures and tools (e.g., ML-based formation control).

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“…Another group of methods take two-stage approach [8] [9] [10], in which each robot plan their paths independently and then coordination is done in the centralized manner. This decomposition enables application to large problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot scheduling and trajectory planning using state roadmap

Tazaki

Suzuki

2014

2014 Proceedings of the SICE Annual Conference (SICE)

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This paper proposes a scheduling and trajectory planning method that constructs collision-free and optimized trajectories for a team of multiple robots. The proposed method consists of path planning and scheduling. First, each robot performs path planning using a state roadmap. Using a state roadmap, each robot can find a path that navigates the robot to the destination without collisions with static obstacles. Second, the scheduler adjusts the departing times and the velocities at the via-points of all robots to avoid collisions between the robots and to minimize the sum of arrival times of the robots. The proposed method is evaluated through simulations.

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Decentralized multi-vehicle path coordination under communication constraints

Cited by 9 publications

References 13 publications

Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams

Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams

Recent Advances in Formations of Multiple Robots

Multi-robot scheduling and trajectory planning using state roadmap

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