Planning for Decentralized Formation Flight of UAV fleets in Uncertain Environments with Dec-POMDP

Floriano, Bruno R.O.; Borges, Geovany A.; Ferreira, Henrique C.

doi:10.1109/icuas.2019.8797928

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…From a control architecture perspective, these solutions can be divided into two groups -centralized and distributed [2]. Although centralized architectures often produce optimal or near-optimal plans due to their global viewpoint [8], [9], [10], distributed architectures typically exhibit better reliability, flexibility, adaptability, and robustness [11], [12], [13], even if the solutions they provide are often suboptimal.…”

Section: Related Workmentioning

confidence: 99%

“…Probabilistic multi-robot coordination approaches based on decentralized, partially observable Markov decision processes have also been studied [11], [15], [16]. The advantage of this approach is its inherent suitability for uncertain environments; however, the scalability problem makes them unsuitable for real-world applications with multiple robots and complex tasks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams

Ferreira¹,

Petrović²,

Bogdan³

2021

Preprint

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In this paper, we propose a distributed multi-stage optimization method for planning complex missions for heterogeneous multi-robot teams. This class of problems involves tasks that can be executed in different ways and are associated with cross-schedule dependencies that constrain the schedules of the different robots in the system. The proposed approach involves a multi-objective heuristic search of the mission, represented as a hierarchical tree that defines the mission goal. This procedure outputs several favorable ways to fulfil the mission, which directly feed into the next stage of the method. We propose a distributed metaheuristic based on evolutionary computation to allocate tasks and generate schedules for the set of chosen decompositions. The method is evaluated in a simulation setup of an automated greenhouse use case, where we demonstrate the method's ability to adapt the planning strategy depending on the available robots and the given optimization criteria.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams

Ferreira¹,

Petrović²,

Bogdan³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As an example, Ding et al (2014) present a method using MDPs for solving optimal controls problems, an interesting case showing how a continuous system may be examined using the inherently discrete system states for an MDP. MDPs have been also been used for optimizing planning processes, Karami et al (2009) show an application which schedules manual and autonomous activity in a collaborative robotics task, Fox et al (2001) approaches policy planning of material acquisition under an agent based framework, and Floriano et al (2019) showcase a fascinating use of MDPs for managing interaction in a fleet of UAVs. These highly varied cases highlight the potential for MDPs as means of analyzing the dynamics of complex systems.…”

Section: Markov Decision Processesmentioning

confidence: 99%

Goal Agnostic Planning using Maximum Likelihood Paths in Hypergraph World Models

Robinson

2021

Preprint

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In this paper, we present a hypergraph-based machine learning algorithm, a datastructuredriven maintenance method, and a planning algorithm based on a probabilistic application of Dijkstra's algorithm. Together, these form a goal agnostic automated planning engine for an autonomous learning agent which incorporates beneficial properties of both classical Machine Learning and traditional Artificial Intelligence. We prove that the algorithm determines optimal solutions within the problem space, mathematically bound learning performance, and supply a mathematical model analyzing system state progression through time yielding explicit predictions for learning curves, goal achievement rates, and response to abstractions and uncertainty. To validate performance, we exhibit results from applying the agent to three archetypal planning problems, including composite hierarchical domains, and highlight empirical findings which illustrate properties elucidated in the analysis.

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“…The studies conducted in this area lack explicit details and discussions on communication and coordination among multiple heterogeneous UAVs. Instead of considering this as the main focus of research, studies embed communication in the overall research context, which broadly involves multiple UAV path planning [31], UAV formation patterns [15], navigation, and control [17]. Path planning mostly includes coverage path planning (CPP) by multiple UAVs, whereas UAV formation patterns include flight formation [15], coalition formation [32], and relay UAVs [33].…”

Section: Introductionmentioning

confidence: 99%

“…Instead of considering this as the main focus of research, studies embed communication in the overall research context, which broadly involves multiple UAV path planning [31], UAV formation patterns [15], navigation, and control [17]. Path planning mostly includes coverage path planning (CPP) by multiple UAVs, whereas UAV formation patterns include flight formation [15], coalition formation [32], and relay UAVs [33]. Further, communication patterns employed by these studies are largely ad hoc and discussed at an abstract level, which makes it difficult to assess the effectiveness of these solutions.…”

Section: Introductionmentioning

confidence: 99%

Communication Among Heterogeneous Unmanned Aerial Vehicles (UAVs): Classification, Trends, and Analysis

Sultan¹,

Anjum

Rehman

et al. 2021

IEEE Access

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In the past decade, unmanned aerial vehicles (UAVs) have become a major research topic in robotics, despite their existence since 1915. UAVs can perform various activities efficiently and effectively; therefore, involving a group of UAVs in performing a certain task has become a growing research trend. Research involving multiple UAVs has been carried out in various areas such as force protection, warfare in the military, remote sensing, disaster response activities, and surveillance. While performing critical tasks, efficiency and robustness are important considerations that can be achieved by involving heterogeneous UAV teams with effective communication and coordination techniques. Communication among heterogeneous UAVs is an interesting and critical research area that needs to be thoroughly explored. In this research, an evidence-based approach is adopted to explore research carried out on multiple heterogeneous UAVs and their communication patterns while performing various activities. A mapping study research technique is employed to systematically collect, analyze, and assess the evidence available on the topic under discussion. The time period defined for this study was set from 2005 to 2019, and 46 primary studies were considered for thematic analysis. The findings show that research studies fall under the category of validation research by constructing simulations to provide a proof-of-concept implementation of the proposed solutions. The communication patterns among heterogeneous UAVs involve various components of UAV communication, networking, formation, and path planning. The application areas that were largely focused on were search, rescue, monitoring, and surveillance missions. The overall trend showed that interest in multiple heterogeneous UAV usage increases over time with a focus on UAV networks, formations, and path planning while considering communication as an implicit part of all these structures. This research has conducted an in-depth analysis of existing research on heterogeneous UAVs and provides classification and trends of various themes emerging within this research area.

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Planning for Decentralized Formation Flight of UAV fleets in Uncertain Environments with Dec-POMDP

Cited by 12 publications

References 14 publications

Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams

Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams

Goal Agnostic Planning using Maximum Likelihood Paths in Hypergraph World Models

Communication Among Heterogeneous Unmanned Aerial Vehicles (UAVs): Classification, Trends, and Analysis

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