A solution to the service agent transport problem

Bays, Matthew J.; Wettergren, Thomas A.

doi:10.1109/iros.2015.7354298

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…Until the locations are known from the actual survey and RI actions, we assume the location of the MILCOs and mines follow density functions p M ILCO (s) and p mine (s). Using the expected number of MILCOs and mines, we assume that expected time costs for performing RI and neutralize actions can be estimated for each survey area s. We denote the expected RI and neutralization costs asĉ RI r,s andĉ neut n,s using vehicle r or n on survey area s. We outline the general logistical constraints for an operation where multiple UUVs may be transported throughout the field via an USV based largely on [28]. We then expand the logistical constraints with the specific constraints developed for general survey, reacquire and identify (RI), and prosecution tasking such as found in MCM operations.…”

Section: Mine Countermeasure Logistics Scheduling Using Milpmentioning

confidence: 99%

Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming

Bays¹,

Wettergren²

2016

Self Cite

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Section: Mine Countermeasure Logistics Scheduling Using Milpmentioning

confidence: 99%

Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming

Bays¹,

Wettergren²

2016

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“…Multi-robot systems, such as teams of UAVs, have been commissioned as Mobile Sensor Networks (MSNs) in applications which require observing (i.e., monitoring and surveilling) the physical world and gathering information [1,2]. MSNs have numerous applications in surveillance and disaster response [3,4,5,6], environment monitoring and agriculture [7,8], wildfire fighting [9,10,11], search-and-rescue [4], manufacturing [12,13], homeland security and border patrol [14,15].…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees

Seraj¹,

Silva²,

Gombolay³

2022

Preprint

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In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been commissioned by researchers to enable accurate, online wildfire coverage and tracking. While the majority of prior work focuses on the coordination and control of such multirobot systems, to date, these UAV teams have not been given the ability to reason about a fire's track (i.e., location and propagation dynamics) to provide performance guarantee over a time horizon. Motivated by the problem of aerial wildfire monitoring, we propose a predictive framework which enables cooperation in multi-UAV teams towards collaborative field coverage and fire tracking with probabilistic performance guarantee. Our approach enables UAVs to infer the latent fire propagation dynamics for time-extended coordination in safety-critical conditions. We derive a set of novel, analytical temporal, and tracking-error bounds to enable the UAV-team to distribute their limited resources and cover the entire fire area according to the case-specific estimated states and provide a probabilistic performance guarantee. Our results are not limited to the aerial wildfire monitoring case-study and are generally applicable to problems, such as search-and-rescue, target tracking and border patrol. We evaluate our approach in simulation and provide demonstrations of the proposed framework on a physical multi-robot testbed to account for real robot dynamics and restrictions. Our quantitative evaluations validate the performance of our method accumulating 7.5× and 9.0× smaller tracking-error than state-of-the-art model-based and reinforcement learning benchmarks, respectively.

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“…Numerous authors have attempted to optimize mixedinteger optimization problems using an initial heuristic, and then the MIP solver on a reduced-complexity problem. 13,14 However, these heuristics typically have at most formal properties on the bounds of their optimality, and are suboptimal.…”

Section: Introductionmentioning

confidence: 99%

Optimal vehicle planning and the search tour problem

Wettergren

Bays

2016

SPIE Proceedings

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We describe a problem of optimal planning for unmanned vehicles and illustrate two distinct procedures for its solution. The problem under consideration, which we refer to as the search tour problem, involves the determination of multi-stage plans for unmanned vehicles conducting search operations. These types of problems are important in situations where the searcher has varying performance in different regions throughout the domain due to environmental complexity. The ability to provide robust planning for unmanned systems under difficult environmental conditions is critical for their use in search operations. The problem we consider consists of searches with variable times for each of the stages, as well as an additional degree of freedom for each stage to select from one of a finite set of operational configurations. As each combination of configuration and stage time leads to a different performance level, there is a need to determine the optimal configuration of these stages. When the complexity of constraints on total time, as well as resources expended at each stage for a given configuration, are added, the problem becomes one of non-trivial search effort allocation and numerical methods of optimization are required. We show two solution approaches for this numerical optimization problem. The first solution technique is to use a mixed-integer linear programming formulation, for which commercially available solvers can find optimal solutions in a reasonable amount of time. We use this solution as a baseline and compare against a new inner/outer optimization formulation. This inner/outer optimization compares favorably to the baseline solution, but is also amenable to adaptation as the search operation progresses. Numerical examples illustrate the utility of the approach for unmanned vehicle search planning.

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A solution to the service agent transport problem

Cited by 12 publications

References 16 publications

Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming

Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming

Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees

Optimal vehicle planning and the search tour problem

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