Ant colony optimization for multi-UAV minimum time search in uncertain domains

Pérez-Carabaza, Sara; Besada-Portas, Eva; López-Orozco, J. A.; Cruz, J.M. de la

doi:10.1016/j.asoc.2017.09.009

Cited by 144 publications

(90 citation statements)

References 31 publications

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“…The ant colony algorithm (ACO) is used to solve the path planning problem, which can be divided into two parts: path construction and pheromone update [11]. The artificial ant colony algorithm and the real ant foraging process are moving from one position to the next, and the position change is realized by the probability selection strategy.…”

Section: Ant Colony Optimization Algorithmmentioning

confidence: 99%

Unmanned vehicle path planning using a novel ant colony algorithm

Yue

Chen

2019

J Wireless Com Network

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The ant colony optimization algorithm is an effective way to solve the problem of unmanned vehicle path planning. First, establish the environment model of the unmanned vehicle path planning, process and describe the environmental information, and finally realize the division of the problem space. Next, the biomimetic behavior of the ant colony algorithm is described. The ant colony algorithm has been improved by adding a penalty strategy. This penalty strategy can enhance the utilization of resources and guide the ants to explore other unknown areas by using the worse value in the search history to enhance the volatility of the pheromone.

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Section: Ant Colony Optimization Algorithmmentioning

confidence: 99%

Unmanned vehicle path planning using a novel ant colony algorithm

Yue

Chen

2019

J Wireless Com Network

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“…One of the main goals of MTS planners is to reduce the target detection time, which can be achieved by optimizing the expected time of target detection [5][6][7][8][9][10]. Other PTSP approaches optimize alternative criteria, such as maximizing the probability of target detection [11][12][13][14] or minimizing its counterpart probability of nondetection [15,16], maximizing the information gain [17], minimizing the system entropy [18], minimizing its uncertainty (areas with intermediate belief of target presence) [19], or optimizing normalized or discounted versions of the previous criteria [4,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Other PTSP approaches optimize alternative criteria, such as maximizing the probability of target detection [11][12][13][14] or minimizing its counterpart probability of nondetection [15,16], maximizing the information gain [17], minimizing the system entropy [18], minimizing its uncertainty (areas with intermediate belief of target presence) [19], or optimizing normalized or discounted versions of the previous criteria [4,[20][21][22]. A common characteristic of the different approaches is that, in scenarios with bounded resources (e.g., limited flying time or fuel), they often obtain better results than predefined search patterns (e.g., spiral, lawnmower), as they adapt the UAV trajectories to the scenario specific target initial belief and motion [6,20]. Besides, although the approaches that optimize the previous PTSP criteria can share the same elements and probabilistic models, MTS distinctiveness is the extreme influence of the visiting order of the high probability regions in the expected time of target detection, as prioritizing flying over high probability areas first increases the chances of finding the target earlier [4].…”

Section: Introductionmentioning

confidence: 99%

“…The NP-hard complexity of PTSP [23] is tackled with suboptimal algorithms and heuristics, such as gradientbased approaches [13,[15][16][17]19], greedy methods [8,12,20], cross-entropy optimization [4,7], Bayesian optimization algorithms [5], ant colony optimization [6,9], or genetic algorithms [10]. Besides, streamlined formulations of the problem are typically accepted in order to further simplify the problem complexity.…”

Section: Introductionmentioning

confidence: 99%

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Minimum Time Search in Real-World Scenarios Using Multiple UAVs with Onboard Orientable Cameras

et al. 2019

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This paper proposes a new evolutionary planner to determine the trajectories of several Unmanned Aerial Vehicles (UAVs) and the scan direction of their cameras for minimizing the expected detection time of a nondeterministically moving target of uncertain initial location. To achieve this, the planner can reorient the UAVs cameras and modify the UAVs heading, speed, and height with the purpose of making the UAV reach and the camera observe faster the areas with high probability of target presence. Besides, the planner uses a digital elevation model of the search region to capture its influence on the camera likelihood (changing the footprint dimensions and the probability of detection) and to help the operator to construct the initial belief of target presence and target motion model. The planner also lets the operator include intelligence information in the initial target belief and motion model, in order to let him/her model real-world scenarios systematically. All these characteristics let the planner adapt the UAV trajectories and sensor poses to the requirements of minimum time search operations over real-world scenarios, as the results of the paper, obtained over 3 scenarios built with the modeling aid-tools of the planner, show.

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“…ACO is a swarm intelligence technology, which is very suitable to solve the routing problem, such as the travelling salesman problem (TSP) problem [25]. ACO has already successfully applied in several applications, such as multi-UAV minimum time search with uncertain domains [26], irrigation scheduling [27], water Journal of Power and Energy Engineering alternating gas injection process [28], bridge inspection routing [29] and so on. All these problems were routing or scheduling related.…”

Section: Introductionmentioning

confidence: 99%

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

Zhang¹

2018

JPEE

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Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.

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Ant colony optimization for multi-UAV minimum time search in uncertain domains

Cited by 144 publications

References 31 publications

Unmanned vehicle path planning using a novel ant colony algorithm

Unmanned vehicle path planning using a novel ant colony algorithm

Minimum Time Search in Real-World Scenarios Using Multiple UAVs with Onboard Orientable Cameras

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

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