Experimental Multi-Vehicle Path Coordination under Communication Connectivity Constraints

Abichandani, Pramod; Mallory, Kenneth; Hsieh, M. Ani

doi:10.1007/978-3-319-00065-7_14

Cited by 2 publications

(1 citation statement)

References 15 publications

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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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Section: Model Predictive Formation Controlmentioning

confidence: 99%

Recent Advances in Formations of Multiple Robots

Cohen

Agmon

2021

Curr Robot Rep

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Route assignment and scheduling with trajectory coordination

Matin-Moghaddam

Sefair

2020

IISE Transactions

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We study the problem of finding optimal routes and schedules for multiple vehicles traveling in a network. Vehicles may have different origins and destinations, and must coordinate their trajectories to keep a minimum distance from each other at any time. We determine a route and a schedule for each vehicle, which possibly requires vehicles to wait at some nodes. Vehicles are heterogeneous in terms of their speed on each arc, which we assume is known and constant once in motion. Applications of this problem include air and maritime routing, where vehicles maintain a steady cruising speed as well as a safety distance to avoid collision. Additional related problems arise in the transportation of hazardous materials and in military operations, where vehicles cannot be too close to each other given the risk posed to the population or the mission in case of a malicious attack. We discuss the hardness of this problem and present an exact formulation for its solution. We devise an exact solution algorithm based on a network decomposition that exploits the sparsity of the optimal solution. We illustrate the performance of our methods on real and randomly generated networks.

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Experimental Multi-Vehicle Path Coordination under Communication Connectivity Constraints

Cited by 2 publications

References 15 publications

Recent Advances in Formations of Multiple Robots

Recent Advances in Formations of Multiple Robots

Route assignment and scheduling with trajectory coordination

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