Multi-UAV Planning in Search and Rescue Missions using Optimal Search Effort Allocation

Optimal effort allocation in stratified, large area trawl surveys, with application to shrimp surveys in the Barents Sea

A simulation model of lake trout Salvelinus namaycush (Walbaum 1792) population dynamics in Lake Pend Oreille, Idaho, was used to estimate (1) the optimal allocation of effort among gillnet mesh sizes that minimizes abundance in the shortest time; (2) the number of years needed to suppress the population to 90% of peak abundance; and (3) once suppressed, how much effort could be reduced to sustain abundance indefinitely. A density-dependent stochastic simulation model was parameterized from data in 2006–2016, including parameter uncertainty and implementation error. Time to suppression could be reduced by using more large-mesh gillnet than was used during 2007–2016. Continued fishing at the peak level of total gillnetting effort, but using an optimal effort allocation among meshes, would suppress abundance to the target level within 7–13 years. Once suppressed, gillnet effort could be reduced 76–86% (157,000 m, 95% CI 116,000–199,000 m) to sustain abundance at the target level. Our findings suggest that time to suppression of lake trout populations in other systems may be able to be reduced by optimizing gillnet effort allocation among mesh sizes, and that total effort can be greatly reduced to sustain abundance at the reduced level thereafter.

Secondary production estimates based on the size‐frequency method can be improved through the use of optimal sample allocation without increasing the total sampling effort. We advocate a two‐step procedure. First, sampling dates should be chosen to minimize bias. Then, sample sizes on each date should be allocated in such a way as to minimize variance. Calculating the optimal sample allocation requires some prior variance information, but even rather imperfect information will usually result in improved estimates. An example is presented using published data on the mayfly Ephemerella dorothea.

An optimal inversion-based iterative learning control (Opt-In ILC) approach for overactuated systems is proposed. The Opt-In ILC update law is formulated as a constrained optimization problem using the plant model. Specifically, the ILC update law is designed to minimize control effort subject to a user-specified error convergence rate. To achieve robust monotonic convergence (MC), a frequency-domain optimization framework is adopted to determine the best plant model and robustness filter for Opt-In ILC. Simulations and experiments on an overactuated coarse–fine stage are used to demonstrate optimal control effort allocation and optimal selection of plant model and robustness filter via the proposed Opt-In ILC approach.

The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

Recent technological developments (intensified fishing by modern fleets, sea pollution) and trends in international law (expanded territorial limits) underscore the role of fisheries (taken here to mean the stocks of fish and enterprises that exploit them) as an important resource to be optimally exploited and conserved. In the U.S.A., optimal management of fisheries is required by law, the Fishery Conservation and Management Act of 1976 (the “200 mile law”), and there, as elsewhere, the trend is toward more regulations and conservation measures.

The problem of optimal (or near-optimal) exhaustive search for a moving target is of importance in many civilian and military applications. Search-andrescue in open sea or in sparsely-populated areas and search missions for previously-spotted enemy targets are just a few examples. Yet, few known algorithms exist for solving this problem and none of them combine the optimal allocation of search effort with the actual computation of trajectories that a searcher must (and physically can) follow. We propose a divide-andconquer geometric approach for constructing optimal search paths for arbitrarily-shaped regions of interest. The technique is both generalizable to multiple search agents and extensible in that additional real-life search requirements (maneuverability constraints, additional information about the sensor, etc.) can be incorporated into the existing framework. Another novelty of our approach is the ability to optimally deal with a search platform which, due to design constraints, can only perform detection while moving along straight-line sweeps.

Plant import inspection can prevent the introduction of exotic pests and diseases, thereby averting economic losses. We explore the optimal allocation of a fixed budget, taking into account risk differentials, and the optimal-sized budget to minimise total pest costs. A partial-equilibrium market model is used to compute the economic consequences of pest invasion. An application to Dutch imports of chrysanthemum cuttings shows that the optimal allocation of a fixed inspection budget halves the cost of pest invasion compared to allocating the same budget equally over all imports. A budget increase that enables 42 per cent more inspection can reduce total societal costs by 81 per cent compared to smaller, constrained budget that ignores risk differentials

Optimal risk-based allocation of disease surveillance effort for clustered disease outbreaks

A problem of interest for ecology and conservation is that of determining the best allocation of survey effort in studies aimed at estimating the proportion of sites occupied by a species. Many species are difficult to detect and often remain undetected during surveys at sites where they are present. Hence, for the estimator of species occupancy to be unbiased, detectability needs to be taken into account. In such studies there is a trade-off between sampling more sites and expending more survey effort within each site. This design problem has not been addressed to date with an explicit consideration of the uncertainty in assumed parameter values. In this article we apply sequential and Bayesian design techniques and show how a simple two-stage design can significantly improve the efficiency of the study. We further investigate the optimal allocation of survey effort between the two study stages, given a prior distribution for the parameter values. We address this problem using asymptotic approximations and then explore how the results change when the sample size is small, considering second-order approximations and highlighting the value of simulations as a tool for study design. Given the efficiency gain, we recommend following the sequential design approach for species occupancy estimation. This article has supplementary material online.

A model of population growth of feral water buffalo is presented and used as a basis for simulating buffalo control exercises. Simulations incorporated annual removal of constant numbers of buffalo, annual allocations of constant amounts of control effort, or continous application of effort until control density had been achieved. The models rely on data from helicopter-based shooting and shooting by shooters on the ground (Bayliss 1986). Approximately 60% of a starting population had to be removed per annum for control to extinction to be achieved within two years. Annual allocations of constant levels of control effort resulted in failure to reach the control target within 150 years, control in about 40 years, or control within the first two years of operation, depending on the level of effort expended. Addition of two helicopters to a team of four helicopters working for 20 days changed the result from no control to control within two years. Optimal allocation of control effort requires continuous application of control measures until the objective has been achieved. Lower levels of effort result in prolonged campaigns that may not achieve desired objectives.

Optimal allocation of inspection effort in a serial multi-stage production system

Summary Environmental DNA (eDNA) sampling can be a highly sensitive method for detecting aquatic taxa; however, the cost‐efficiency of this technique relative to traditional methods has not been rigorously assessed. We show how methods that account for imperfect and stochastic detection can be used to (i) determine the optimal allocation of survey effort with eDNA sampling for a fixed budget (i.e. identify the optimal combination of water samples vs. site visits), and (ii) assess the cost‐efficiency of eDNA sampling relative to traditional survey techniques. We illustrate this approach by comparing eDNA sampling and bottle‐trapping for an exotic newt species (Lissotriton v. vulgaris) recently detected in Melbourne, Australia. Bottle traps produced much lower detection rates than eDNA sampling, but the cost‐efficiency of the two methods can be similar because bottle‐trapping is cheaper per sample. The relative cost‐efficiency of the two sampling methods was sensitive to the available survey budget, the costs of eDNA primer/probe development and sample processing and the number of positive quantitative PCR assays (qPCRs) used to designate a water sample as positive for newt DNA. Environmental DNA sampling was more cost‐efficient than bottle‐trapping for small to intermediate budgets when primer/probe development and sample processing costs were low, and 1/4 or 2/4 positive qPCRs were used to label a water sample as positive for newt eDNA. However, bottle traps were generally more cost‐efficient than eDNA sampling when primer/probe development and sample processing costs were high, regardless of qPCR threshold or survey budget. Traditional sampling methods may achieve lower detection probabilities compared to eDNA sampling, but the totality of costs can make eDNA sampling less efficient than traditional techniques in some circumstances. Our approach provides a quantitative framework for determining how many water samples and site visits are required to maximize detection probabilities with eDNA sampling, and can calculate the cost‐efficiency of any sampling method.

Capture-recapture methods are widely used for estimating population sizes in ecological and epidemiological studies, yet the optimal allocation of sampling effort often remains underexplored. This study explores sampling efforts in a hierarchical framework that subdivides each capture occasion in a Lincoln-Petersen experiment into multiple sub-occasions, allowing for flexible resource allocation. When detection probabilities are equal across occasions, an even split minimises variance; when probabilities differ, maximizing the joint detection probability is essential. A pseudo-Bayesian approach is also proposed to address scenarios with unknown catchabilities. Detailed simulation studies validate the theoretical findings and demonstrate the framework’s robustness. The resulting guidelines offer practical insights for designing more efficient capture-recapture experiments and improving population size estimates.

Management of an ungulate population to maintain it at a desired level requires monitoring population size and vital rates to be able to harvest the population wisely. We present equations for the optimal allocation of resources for estimating total population size, recruitment rate of juveniles into the adult population, and juvenile and adult survival to minimize the variance of the adult female population size and the adult female population rate of change. These equations require information on the variance of the population growth process (process variance), expected sampling variance, and per unit costs for each of the 4 sampling procedures.