Abstract
The application of an unmanned vehicle system allows for accelerating the performance of various tasks. Due to limited capacities, such as battery power, it is almost impossible for a single unmanned vehicle to complete a large-scale mission area. An unmanned vehicle swarm has the potential to distribute tasks and coordinate the operations of many robots/drones with very little operator intervention. Therefore, multiple unmanned vehicles are required to execute a set of well-planned mission routes, in order to minimize time and energy consumption. A two-phase heuristic algorithm was used to pursue this goal. In the first phase, a tabu search and the 2-opt node exchange method were used to generate a single optimal path for all target nodes; the solution was then split into multiple clusters according to vehicle numbers as an initial solution for each. In the second phase, a tabu algorithm combined with a 2-opt path exchange was used to further improve the in-route and cross-route solutions for each route. This diversification strategy allowed for approaching the global optimal solution, rather than a regional one with less CPU time. After these algorithms were coded, a group of three robot cars was used to validate this hybrid path programming algorithm.
Highlights
Mainstream applications are currently focused on unmanned vehicle robots used in manufacturing; unmanned air vehicles (UAVs) in monitoring the earth’s surface; emergency aid and disaster control and prevention efforts; commercial aerial photography; logistics; and unmanned combat air vehicle operations (UCAVs) [1,2]
Facing this problem of a large area multi-waypoints mission dealing with a multi-agent system, we designed a hybrid dynamic path programming algorithm to help us achieve the goals of saving time and energy, with shorter and more efficient routes so that the robot cars were not running redundant paths
This section describes the tests for the proposed hybrid path programming method with This section thefor tests the proposed hybrid path programming method with maximum maximum rangedescribes constraint thefor mission planning of multi-robot cars
Summary
Mainstream applications are currently focused on unmanned vehicle robots used in manufacturing; unmanned air vehicles (UAVs) in monitoring the earth’s surface; emergency aid and disaster control and prevention efforts; commercial aerial photography; logistics; and unmanned combat air vehicle operations (UCAVs) [1,2]. To complete the tasks more efficiently, well-planned path programming is a must, so as to minimize time and energy consumption by shortening the overall distances of the routes. Facing this problem of a large area multi-waypoints mission dealing with a multi-agent system, we designed a hybrid dynamic path programming algorithm to help us achieve the goals of saving time and energy, with shorter and more efficient routes so that the robot cars were not running redundant paths. Unlike other path-programming works, this study contributes to the field with a hybrid path programming algorithm involving a combined tabu search and a 2-opt swap under the maximum
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