Heuristics for Two Depot Heterogeneous Unmanned Vehicle Path Planning to Minimize Maximum Travel Cost

Abstract

A solution to the multiple depot heterogeneous traveling salesman problem with a min-max objective is in great demand with many potential applications of unmanned vehicles, as it is highly related to a reduction in the job completion time. As an initial idea for solving the min-max multiple depot heterogeneous traveling salesman problem, new heuristics for path planning problem of two heterogeneous unmanned vehicles are proposed in this article. Specifically, a task allocation and routing problem of two (structurally) heterogeneous unmanned vehicles that are located in distinctive depots and a set of targets to visit is considered. The unmanned vehicles, being heterogeneous, have different travel costs that are determined by their motion constraints. The objective is to find a tour for each vehicle such that each target location is visited at least once by one of the vehicles while the maximum travel cost is minimized. Two heuristics based on a primal-dual technique are proposed to solve the cases where the travel costs are symmetric and asymmetric. The computational results of the implementation have shown that the proposed algorithms produce feasible solutions of good quality within relatively short computation times.

Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

“…In order to compare the effects of different methods more scientifically, each method runs 35 times independently, and the average value of 35 experiments is taken as the final result. In order to demonstrate the performance of the proposed model, it is compared with Reference [11,14,15]. e results are shown in Table 2, that is, the minimum cost and success rate of finding the best route for distribution vehicles, where iteration rate � the number of iterations required to find the best route/the total number of iterations.…”

“…1 if vehicle k passes customer i to j 0 otherwise 􏼨 ; N is a collection of network nodes (including distribution centers and customer points); and K is the collection of vehicles. Equation (8) represents the capacity limit of the vehicle; equation (9) means that each customer has one and only one car to provide service; equation (10) means that all vehicles can only depart from the distribution center once and finally return to the distribution center; equation (11) represents the time relationship; and equation (12) indicates that the vehicle must leave after completing the service.…”

“…However, the impact of the real-time status of the urban traffic network on the route plan is not considered. In Reference [11], a new heuristic algorithm for heterogeneous vehicle path planning based on primitive dual technology was proposed for the task assignment and routing problems of two heterogeneous unmanned vehicles located in different warehouses and a set of targets to be visited. High-quality feasible solutions can be obtained in a short time, but the solution efficiency is difficult to meet actual needs.…”

Improving Lateral Safety Distance-Based on Feature Detection and Probabilistic Roadmaps for Unmanned Vehicle Path Planning

“…Fethi, D et al [18] proposed a new approach of optimal simultaneous localization, mapping, and path planning based on the optimal control theory and simultaneous localization and mapping. As an initial idea for solving a minmax multiple-depot heterogeneous traveling salesman problem, Bae, J. et al [19] proposed new heuristics for the path planning problem of two heterogeneous unmanned vehicles. To plan a global path minimizing the risk of an unmanned vehicle on the battlefield, Shin, J. et al [20] proposed a global path replanning method.…”

Velocity-Based Heuristic Evaluation for Path Planning and Vehicle Routing for Victim Assistance in Disaster Scenarios

“…Planning node-to-node paths can be the first step in a VRP solution to build the input cost matrix. A time-aware planning can be achieved by considering the traversal times of the edges in topological search graphs [1] [8]. Cells in grid representations can be used as nodes in graph-search methods derived from the Dijkstra algorithm [7], but high resolution maps may result in very large search graphs.…”