Abstract
Currently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unnecessary consumption of time, distance, and energy waste. The aim of the present study was to develop a multiple-vehicle system that can automatically compile a set of optimum vehicle paths, complement failed assignments, and avoid passing through no-travel zones. A heuristic algorithm was used to obtain an approximate solution within a reasonable timeline. The A* Search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array was set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. A diversification strategy helped identify a global optimal solution rather than a regional one. The final experiments successfully demonstrated a group of three robot cars that were simultaneously dispatched to each of their planned routes; when any car failed during the test, its path was immediately reprogrammed by the monitoring station and passed to the other cars to continue the task until each target point had been visited.
Highlights
Unmanned vehicles are applied in various fields
The aim of this research was to develop a group of robot cars as a multi-agent system (MAS) with multi-path programming under the constraint of a maximum distance limit, which is the maximum capable range of a robot car
The final final test test conducted conducted for for this this study study was was the the test test of of failure failure complementation
Summary
Unmanned vehicles are applied in various fields. Unmanned aerial vehicles (UAV) monitor earth, provide emergency aid, perform disaster control and prevention, and execute aerial photography [1,2]. Along with the mission scale, mission areas are growing much wider and may soon be beyond a single vehicle’s reach. Compared to a traditional single robot system, multi-robots cooperating to achieve a global mission can be used to solve problems for a wide variety of application domains. A multi-robot team is usually beneficial for shortening the time required for a search and rescue mission as well as for reducing energy consumption in large area cargo delivery, etc. All team members cooperate to fulfill a mission by dividing the labor of execution
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