Time-Efficient Coverage Path Planning for Energy-Constrained UAV

Abstract

Unmanned aerial vehicles (UAVs) have the characteristics of high mobility and wide coverage, making them widely used in coverage, search, and other fields. In these applications, UAV often has limited energy. Therefore, planning a time-efficient coverage path for energy-constrained UAV to cover the area of interest is the core issue. The existing coverage path planning algorithms assume that the UAV moves at a constant speed, without taking into account the cost of turns (including deceleration, turning, and acceleration), which is unrealistic. To solve the above problem, we propose a time-efficient coverage path planning (TECPP) algorithm for the energy-constrained UAV. We build a novel gadget-based graph model, which formalizes the time and energy costs of the flight path including straight flights and making turns (deceleration, turning, and acceleration). Moreover, our graph model is suitable for irregular-shaped areas with multiple obstacles. Finally, we transform the above problem into a generalized traveling salesman problem (GTSP) and use the generalized large neighborhood search (GLNS) solver to solve it. The experimental results show that TECPP outperforms the existing coverage path planning algorithms, and TECPP saves at least 21.6% of time.

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

“…System Environment Modeling. Using the geometric graph method [25], the battlefield area can be rasterized into a set MAP = fðm, nÞjm = 1, 2, ⋯, L x , n = 1, 2, ⋯, L y g, with a size of L x × L y corresponding to the cell in row m and column n.…”

Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

“…As an important branch of multi-UAV task planning, coverage-path planning includes region allocation and path planning. It has been studied by numerous scholars from various aspects, such as region shape [15,16], energy constraints [17][18][19][20], and obstacle avoidance [21,22]. Nielsen [15] tackled the issue of region coverage for non-convex polygons by dividing the area into numerous separate convex sub-polygons and utilizing a scanning pattern to ensure complete coverage.…”

“…Nielsen [15] tackled the issue of region coverage for non-convex polygons by dividing the area into numerous separate convex sub-polygons and utilizing a scanning pattern to ensure complete coverage. Huang [17] determined the energy consumption of UAVs in various flight modes and presented a coverage path-planning algorithm that relies on a UAV energy-limited model, aiming to minimize the flight duration of UAVs on coverage paths. Franco [19] takes into account additional factors, including energy, speed, acceleration, and image resolution, in coverage-path planning.…”

A Practical Interlacing-Based Coverage Path Planning Method for Fixed-Wing UAV Photogrammetry in Convex Polygon Regions

“…Finally, when it comes to the performance metrics of complete coverage path planning, the total traveled distance or the path length [27], coverage flight time [28], energy consumption [29], and the number of turns [30] are most commonly found in the literature. Among them, the number of turning maneuvers is frequently utilized as the main performance metric in CPP.…”