Task Assignment Of Vehicles Research Articles

Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm

As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes.

A Formation Reconfiguration Method for Multiple Unmanned Surface Vehicles Executing Target Interception Missions

This paper presents a formation reconfiguration method for multiple unmanned surface vehicles (MUSVs) executing target interception missions. In its essence, the objective can be described as follows: given a target at a given position and a set of USVs, maneuver the USVs from their initial positions towards the target so that they end forming a “protective barrier” in the vicinity of the target. In its simplest form, this barrier can be simply obtained through a colinear formation of the USVs, with the center at an appropriate distance from the target. This can be done in two distinct phases: maneuver planning and vehicle task assignment. In terms of maneuver planning, an adaptive null-space-based (ANSB) strategy is adopted. In order to address explicitly the maneuverability constraints of each vehicle (for example, intrinsic limitations on the rate of turn while progressing at a given linear speed), an improved adaptive null space method (IMANSB) is proposed for vehicle speed assignment. In terms of task assignment, by introducing the virtual attractive force and rigid body dynamics, a method of redefined Dubins path was proposed, which can more quickly match the optimal path that conforms to the heading angle of the departure and the heading angle of the destination. And, a method of formation reconfiguration for sea surface target interception of USV was presented, including spatial decoupling search and redefined Dubins path planning, in order to solve the problem of multi-objective optimization of the MUSV formation internal reconfiguration which is helpful to accelerate formation forming by considering the influence of cooperative time consistency and total distance. To verify the feasibility of proposed method, a simulation platform for the formation control of MUSVs was established, and a simulation experiment was conducted.

The Task Assignment of Vehicles for a Production Company

The article investigates the problem of task assignment of vehicles for a production company. The presented problem is a complex decision-making issue which has not been analyzed in the literature before. Two stages must be passed through in order to solve the task assignment problem of the vehicles for the production company. The first stage is to designate the tasks, the other one is to determine the number of the vehicles that is needed to perform these tasks. The task in the analyzed problem is defined as transporting the cargo from the suppliers to the warehouses and from the warehouses to the production company. The number of the tasks depends on the type of the vehicle which carries out a given task. In order to solve the presented problem, the mathematical model has been developed, i.e., decision variables, constraints, and criterion functions. There are three types of decision variables occurring in the model, which means that this problem is quite complex. The first type of the decision variables determines the volume of the cargo which flows among the facilities on a given working day, the second type of the decision variables determines the use of a given type of the vehicle in the task, and the third type of the decision variables determines the number of the vehicles which perform the task. The criterion functions take the following form: the fuel consumption costs, the transition costs of the cargo via the warehouses, the purchase costs of the cargo, and the task completion time. In order to solve the task assignment problem of the vehicles, a genetic algorithm has been developed. The proposed method of task assignment solution is unique due to the coding method of individuals and related recombination procedures. The construction stages of this algorithm are presented. The algorithm has been verified by the use of the real input data. The developed model and method of its solution are unique in the application to the service of manufacturing enterprises. Due to the high efficiency and multi-aspect approach, it can be applied in enterprises of various industries as support for decision-makers in the optimization of resources.

Embedding Remaining Useful Life Predictions into a Modified Receding Horizon Task Assignment Algorithm to Solve Task Allocation Problems

The Task Allocation problem is one of the fundamental combinatorial optimization problems with applications on various domains. Solving a Task Allocation problem consists in, given a set of tasks to be performed and a set of resources, defining which resource will perform each task in order to optimize an objective function. In this paper, we present a modified version of the Receding Horizon Task Assignment (RHTA) algorithm to solve multiple vehicle task assignment problems. In the proposed method, we generate a rejection list to reduce the number of candidate missions that are evaluated in each iteration of the RHTA algorithm. In addition, we incorporate in the mathematical formulation of the problem a set of constraints that limit the maximum mission duration that can be assigned to each vehicle. These constraints represent the predicted Remaining Useful Life (RUL) of each vehicle. Our model takes into account the execution time of each task and assumes that all vehicles must finish their missions at a base. The proposed model allows the vehicles to go to a base for maintenance during their missions. Numerical experiments are carried out using twenty benchmark problem instances. The results show that incorporating RUL predictions into task allocation problems increases the quality and the robustness of solutions.

Active Exploration in Robust Unmanned Vehicle Task Assignment

This paper presents new formulations for the robust allocation of unmanned vehicles (UVs) in the presence of parametric uncertainty. Standard robust optimization approaches for hedging against the worst-case plan performance can lead to overly conservative plans. Onewaytoreducethisconservatismistoemployactiveexplorationtoreducetheuncertainty in the model parameters, but the UV literature does not address the coupling between uncertainty reduction and improvement in worst-case mission performance.This paper presents a new algorithm that assigns active exploration tasks that provide the most benefit in reducing the conservatism of the robust plans. This algorithm is extended to a more complex, receding horizon task assignment framework which has been validated in previous UV hardware demonstrations. We then show that this novel formulation can be formulated as an integer program and improves overall mission score when compared to alternative decoupled formulations. Finally, this paper shows that this coupled formulation demonstrates more tightly coordinated behavior between heterogeneous UVs in complex resource allocation problems.

Reducing transit fleet emissions through vehicle retrofits, replacements, and usage changes over multiple time periods

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO 2 equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.

A value-added approach for automated guided vehicle task assignment

Abstract Efficient material flow is essential in achieving high productivity levels. Design and control of the material handling system directly affect the efficiency of the material flow. In the case of automated guided vehicle (AGV) systems, this translates to guidepath design, AGV dispatching, scheduling, and routing. The focus of this paper is on vehicle dispatching, or vehicle task assignment, which is defined as the selection of the next load for pickup and delivery. The algorithm developed in this research is inherently a demand-driven strategy (that is, pull), which further prioritizes the loads requiring an AGV according to the value added to them as they go through the manufacturing processes. Benchmarking for several performance factors, such as throughput and flow time, through simulation experiments shows this algorithm to outperform some of the best dispatching rules reported in the literature.

A value-added approach for automated guided vehicle task assignment

It has been suggested that the Dark Triad (DT) personality constellation is an evolved facilitator of men’s short-term mating strategies. However, previous studies have relied on self-report data to consider the sexual success of DT men. To explore the attractiveness of the DT personality to the other sex, 128 women rated created (male) characters designed to capture high DT facets of personality or a control personality. Physicality was held constant. Women rated the high DT character as significantly more attractive. Moreover, this greater attractiveness was not explained by correlated perceptions of Big 5 traits. These findings are considered in light of mating strategies, the evolutionary ‘arms race’ and individual differences.