1,574 research outputs found
A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption
The past decade has seen a substantial increase in the use of small unmanned
aerial vehicles (UAVs) in both civil and military applications. This article
addresses an important aspect of refueling in the context of routing multiple
small UAVs to complete a surveillance or data collection mission. Specifically,
this article formulates a multiple-UAV routing problem with the refueling
constraint of minimizing the overall fuel consumption for all of the vehicles
as a two-stage stochastic optimization problem with uncertainty associated with
the fuel consumption of each vehicle. The two-stage model allows for the
application of sample average approximation (SAA). Although the SAA solution
asymptotically converges to the optimal solution for the two-stage model, the
SAA run time can be prohibitive for medium- and large-scale test instances.
Hence, we develop a tabu-search-based heuristic that exploits the model
structure while considering the uncertainty in fuel consumption. Extensive
computational experiments corroborate the benefits of the two-stage model
compared to a deterministic model and the effectiveness of the heuristic for
obtaining high-quality solutions.Comment: 18 page
Comparison of partially decoupled and combined methods of path planning and task allocation
Developing autonomous unmanned aerial vehicles (UAVs) reduces the risks to which soldiers are subjected by enabling the UAVs to make efficient decisions, regardless of the situation. This requires each group of UAVs to be proficient in planning their own paths and assigning tasks in a way that minimizes the total cost of the mission. Two methods are presented for doing this, the partially decoupled approach and the combined approach. After comparing two methods, the partially decoupled approach costs an average of 3.0 meters less than the combined approach, while taking an average of 0.327 seconds longer to complete. This indicates that the partially decoupled method should be chosen if the main concern is the cost of the mission and the combined approach should be chosen if computational time is the main concern
Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms
The paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.Unión Europea 789051Ministerio de Ciencia, Innovación y Universidades IJC2018-035395-
Task allocation and consensus with groups of cooperating Unmanned Aerial Vehicles
The applications for Unmanned Aerial Vehicles are numerous and cover a range of areas from military applications, scientific projects to commercial activities, but many of these applications require substantial human involvement. This work focuses on the problems and limitations in cooperative Unmanned Aircraft Systems to provide increasing realism for cooperative algorithms. The Consensus Based Bundle Algorithm is extended to remove single agent limits on the task allocation and consensus algorithm. Without this limitation the Consensus Based Grouping Algorithm is proposed that allows the allocation and consensus of multiple agents onto a single task. Solving these problems further increases the usability of cooperative Unmanned Aerial Vehicles groups and reduces the need for human involvement. Additional requirements are taken into consideration including equipment requirements of tasks and creating a specific order for task completion. The Consensus Based Grouping Algorithm provides a conflict free feasible solution to the multi-agent task assignment problem that provides a reasonable assignment without the limitations of previous algorithms. Further to this the new algorithm reduces the amount of communication required for consensus and provides a robust and dynamic data structure for a realistic application. Finally this thesis provides a biologically inspired improvement to the Consensus Based Grouping Algorithm that improves the algorithms performance and solves some of the difficulties it encountered with larger cooperative requirements
Comparison of path-planning and search methods for cooperating unmanned aerial vehicles
The main goal of this research effort is develop a simulation environment for cooperating UAVs within MATLAB\u27s SIMULINK. This is the first step in a process that will eventually lead to the implementation of model UAVs on a model battlefield. The interest in cooperation of UAVs over the past decade has grown significantly. This is due to several reasons including lower operational cost, lower risk for humans, and greater maneuverability.;This research explores two scenarios. The first is a scenario in which all of the characteristics of a battlefield are known prior to the UAVs being launched. Three prevalent path-planning methods are compared based on calculation speed and optimization. This thesis shows that a visibility graph method leads to the lowest cost solution, while the Voronoi diagram method provides a computationally inexpensive solution.;The second scenario is a search and destroy mission where nothing is known about the battlefield prior to UAVs launch. This will consist of the vehicles visiting a set of predetermined waypoints until a target is found. The result of this research produces a simulation of cooperating UAVs that shows the potential of fulfilling many realistic missions in a battlefield environment
Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand
Humanitarian logistics service providers have two major responsibilities
immediately after a disaster: locating trapped people and routing aid to them.
These difficult operations are further hindered by failures in the
transportation and telecommunications networks, which are often rendered
unusable by the disaster at hand. In this work, we propose two-echelon vehicle
routing frameworks for performing these operations using aerial uncrewed
autonomous vehicles (UAVs or drones) to address the issues associated with
these failures. In our proposed frameworks, we assume that ground vehicles
cannot reach the trapped population directly, but they can only transport
drones from a depot to some intermediate locations. The drones launched from
these locations serve to both identify demands for medical and other aids
(e.g., epi-pens, medical supplies, dry food, water) and make deliveries to
satisfy them. Specifically, we present two decision frameworks, in which the
resulting optimization problem is formulated as a two-echelon vehicle routing
problem. The first framework addresses the problem in two stages: providing
telecommunications capabilities in the first stage and satisfying the resulting
demands in the second. To that end, two types of drones are considered. Hotspot
drones have the capability of providing cell phone and internet reception, and
hence are used to capture demands. Delivery drones are subsequently employed to
satisfy the observed demand. The second framework, on the other hand, addresses
the problem as a stochastic emergency aid delivery problem, which uses a
two-stage robust optimization model to handle demand uncertainty. To solve the
resulting models, we propose efficient and novel solution approaches
The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case
The popularity of drones is rapidly increasing across the different sectors
of the economy. Aerial capabilities and relatively low costs make drones the
perfect solution to improve the efficiency of those operations that are
typically carried out by humans (e.g., building inspection, photo collection).
The potential of drone applications can be pushed even further when they are
operated in fleets and in a fully autonomous manner, acting de facto as a drone
swarm. Besides automating field operations, a drone swarm can serve as an
ad-hoc cloud infrastructure built on top of computing and storage resources
available across the swarm members and other connected elements. Even in the
absence of Internet connectivity, this cloud can serve the workloads generated
by the swarm members themselves, as well as by the field agents operating
within the area of interest. By considering the practical example of a
swarm-powered 3D reconstruction application, we present a new optimization
problem for the efficient generation and execution, on top of swarm-powered
ad-hoc cloud infrastructure, of multi-node computing workloads subject to data
geolocation and clustering constraints. The objective is the minimization of
the overall computing times, including both networking delays caused by the
inter-drone data transmission and computation delays. We prove that the problem
is NP-hard and present two combinatorial formulations to model it.
Computational results on the solution of the formulations show that one of them
can be used to solve, within the configured time-limit, more than 50% of the
considered real-world instances involving up to two hundred images and six
drones
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