Path Planning for UAV to Cover Multiple Separated Convex Polygonal Regions

Abstract

In many unmanned aerial vehicle (UAV) applications such as land assessment, search and rescue, and precision agriculture, UAVs are often required to survey multiple spatially distributed regions. To perform these applications, one of the key steps is to plan the path for the UAV to quickly cover all regions. The new path planning problem explored here, which we call the TSP-CPP problem, can be viewed as an integration of the traveling salesman problem (TSP) and the coverage path planning (CPP) problem, which has not been well studied in the literature. In this paper, we conduct a systematic investigation on the TSP-CPP problem. In particular, we first provide a mixed integer programming formulation for this new problem, and then introduce a CPP method for covering a single convex polygonal region. Based on this method, we then develop two approaches to solve the TSP-CPP problem, including 1) a dynamic programming-based exact approach that can find the (near) optimal tour, and 2) a heuristic approach that can generate high-quality tours very efficiently. Through comprehensive theoretical analyses and simulation studies, we demonstrate the optimality and efficiency of the proposed approaches. INDEX TERMS Path planning, traveling salesman problem, coverage path planning, multiple convex polygonal regions, unmanned aerial vehicle, optimal path, heuristic algorithm.

Coverage Path Planning Techniques for Inspection of Disjoint Regions With Precedence Provision

“…The main reason that they incur a large cost is because they fail to follow the order as observed in Figure 13. Keeping up with the trend in respect to coverage cost, we can conclude that our approach with precedence provision outperforms all the set of works [5], [13], [40].…”

“…Popular classical approaches focusing on optimality guarantees for coverage path planning are: Potential Field [33], Grid-Based [34], Probabilistic Roadmap Methods (PRM) [35] and Rapidly exploring Random Trees (RRT) [36]. The heuristic approaches which search the solution space for near optimal solution can be sub-categorized as: Neural Network [37], Fuzzy logic techniques [38], Gravitational Search Algorithm [39], Genetic Algorithm [40] and Ant-Colony Algorithm [41]. The optimization problem of computing the coverage path has been classified as NP-Hard, the problem of coverage of disjoint regions adds to its complexity.…”

“…2) Dynamic Programming [13]: The coverage problem is solved using integrated approach of dynamic programming. 3) Extended Genetic Algorithm [40]: The coverage problem is used as an integrated problem using genetic algorithm [49].…”

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

“…Recent works have considered more complex environ-ments. For instance, in [139], Xie et al presented a path planning algorithm for UAVs covering disjoint convex regions. The authors' method considered an integration of both coverage path planning and the traveling salesman problem.…”

Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring

“…It has been increasingly applied in monitoring missions, which can be regarded as curvature-constrained path planning problems. In some research, the path planning of UAV is simplified as the traveling salesman problem (TSP) [6]- [10]. However, it is very important to consider the kinematic mand center will plan a data collection tour for the UAV, and command the UAV to circularly collect the latest data from all DNs to obtain the information of monitoring areas.…”