Specification and Planning of Interactive UAV Missions in Adversarial Environments

Karaman, Sertaç; Faied, Mariam; Girard, Anouck

doi:10.2514/6.2009-6215

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In military mission planning applications, scheduling is used to plan the routes of unmanned vehicles [5]- [7], [24]. These applications include detailed mission specifications and vehicle kinematics.…”

Section: A Literature Reviewmentioning

confidence: 99%

Distributed Constrained Minimum-Time Schedules in Networks of Arbitrary Topology

et al. 2013

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This paper introduces the minimum-time arbitrarily constrained distributed scheduling problem. The optimal distributed nonsequential backtracking algorithm is used by agents to schedule tasks where the tasks are related by constraints and where the assignment of tasks to agents may change during scheduling. The problem is distributed in that no agent has full knowledge either of the communication network or of the scheduling constraints. Each agent knows the assignments of the agents with which it can communicate and the scheduled times of the tasks assigned to those agents. The minimum-time arbitrarily constrained distributed scheduling problem is for the agents to use this local knowledge to find a schedule that satisfies all constraints and minimizes the time needed to complete all tasks. The optimal distributed nonsequential backtracking algorithm is able to find minimum-time schedules that satisfy all constraints. This algorithm is designed to work concurrently with a distributed assignment algorithm that satisfies certain communication requirements detailed in this paper. This paper presents proof of the algorithm's correctness, completeness, and optimality.

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Section: A Literature Reviewmentioning

confidence: 99%

Distributed Constrained Minimum-Time Schedules in Networks of Arbitrary Topology

et al. 2013

Self Cite

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“…The authors of Ref. [6] use a process algebra framework to model interactive tasks and optimize allocation using a statespace search algorithm. Hierarchical task assignment strategies using evolutionary optimization 7 or market-based algorithms 8 have been proposed to control large numbers of UAVs.…”

Section: Iia Related Researchmentioning

confidence: 99%

Flight Testing a Heterogeneous Multi-UAV System with Human Supervision

Kopeikin

Clare

Toupet

et al. 2012

AIAA Guidance, Navigation, and Control Conference

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This paper presents the outdoor flight test results of a decentralized multi-UAV system supervised by a human operator. The system balances the roles of the human operator and the UAV autonomous behaviors with the objective of maximizing the execution performance. The operator manages the mission by inputting and modifying tasks instead of controlling individual UAVs. The Consensus-Based Bundle Algorithm (CBBA) is used as a real-time, scalable, dynamic multi-agent multi-task planning algorithm to allocate tasks approved by the operator to UAVs. A team of three quadrotors and one fixed wing UAV collaborated in an operationally relevant scenario supporting a cargo UAV resupply mission. Thirteen of fourteen multi-UAV outdoor flight test trials successfully accomplished the mission objectives. The framework was shown to be robust to system failures and degradations commonly encountered during field testing primarily because of health monitoring and management tools that were incorporated in the design. Instances of task allocation and path planning churning were observed which are linked to uncertainties of operating outdoors. Lessons learned during flight test operations are highlighted as they are relevant to other similar types of systems and missions.

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Identifying Suitable Algorithms for Human-Computer Collaborative Scheduling of Multiple Unmanned Vehicles

Clare

Cummings

Bertuccelli

et al. 2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Real-time scheduling and task assignment for multiple Unmanned Vehicles (UVs) in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Identifying the characteristics that make a scheduling algorithm suitable for human-computer collaboration is essential for the development of an effective scheduling system. This high-level systems analysis paper begins the process of deriving requirements for collaborative scheduling algorithms by conducting a survey of 117 publications within the past five years in academia and industry on multiple UV scheduling algorithms. The goal of the survey is to determine the types and frequency of scheduling algorithms that are currently in use and to classify the characteristics and capabilities of these algorithms. Results show that academia has settled on meta-heuristic and auction-based algorithms as providing the best balance of performance and computational speed. In industry, however, the most widely used solution methods are "iterative" approaches that monotonically improve the schedule with further iterations. Industry-developed algorithms are more likely to be capable of scheduling heterogeneous UVs, but university researchers have developed more algorithms that can account for uncertainty and provide estimates of robustness. The different objectives of industry practitioners and academic researchers may be driving these disparities. Addressing this gap will be essential to the development and adoption of future humancomputer collaborative scheduling systems. Nomenclature J= objective function of the optimization algorithm x = decision variables f, g = generic functions θ = generic variable

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Specification and Planning of Interactive UAV Missions in Adversarial Environments

Cited by 3 publications

References 18 publications

Distributed Constrained Minimum-Time Schedules in Networks of Arbitrary Topology

Distributed Constrained Minimum-Time Schedules in Networks of Arbitrary Topology

Flight Testing a Heterogeneous Multi-UAV System with Human Supervision

Identifying Suitable Algorithms for Human-Computer Collaborative Scheduling of Multiple Unmanned Vehicles

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