An integrated traveling salesman and coverage path planning problem for unmanned aircraft systems

Unmanned aircraft systems (UASs) have gained great popularity in land monitoring, 3-D mapping, search and rescue, among others. Existing studies on UAS path planning in these missions consider only a single region to be examined. However, it is frequently encountered that multiple regions need to be considered while performing a real life mission. How to design the optimal path for the UAS to cover multiple regions is then critical. From a strategical point of view, such a problem can be considered as a variant of the traveling salesman problem (TSP) combined and enhanced with the coverage path planning (CPP) problem. Although TSP and CPP have been studied extensively, its combination, which here is given the name TSP-CPP, hasn't received any attention. In this letter, a preliminary study on how to formulate and solve this problem is conducted. Two novel approaches including a grid-based approach and a dynamic programming based approach are introduced to find the (near) optimal solution. Both numerical analysis and simulation studies are conducted to prove and illustrate the optimality and efficiency of the proposed TSP-CPP approaches.

Unmanned aircraft systems (UASs) have gained great popularity in land monitoring, 3-D mapping, search and rescue, among others. Existing studies on UAS path planning in these missions consider only a single region to be examined. However, it is frequently encountered that multiple regions need to be considered while performing a real life mission. How to design the optimal path for the UAS to cover multiple regions is then critical. From a strategical point of view, such a problem can be considered as a variant of the traveling salesman problem (TSP) combined and enhanced with the coverage path planning (CPP) problem. Although TSP and CPP have been studied extensively, its combination, which here is given the name TSP-CPP, hasn't received any attention. In this letter, a preliminary study on how to formulate and solve this problem is conducted. Two novel approaches including a grid-based approach and a dynamic programming based approach are introduced to find the (near) optimal solution. Both numerical analysis and simulation studies are conducted to prove and illustrate the optimality and efficiency of the proposed TSP-CPP approaches.