Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Conclusions <s> This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body painting, machine milling, etc. This paper will focus on coverage path planning algorithms for mobile robots constrained to operate in the plane. These algorithms can be classified as either heuristic or complete. It is our conjecture that most complete algorithms use an exact cellular decomposition, either explicitly or implicitly, to achieve coverage. Therefore, this paper organizes the coverage algorithms into four categories: heuristic, approximate, partial-approximate and exact cellular decompositions. The final section describes some provably complete multi-robot coverage algorithms. <s> BIB001 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Conclusions <s> Most unmanned aerial vehicles nowadays engage in coverage missions using simple patterns, such as back-and-forth and spiral. However, there is no general agreement about which one is more appropriate. This letter proposes an E-Spiral algorithm for accurate photogrammetry that considers the camera sensor and the flight altitude to apply the overlapping necessary to guarantee the mission success. The algorithm uses an energy model to set different optimal speeds for straight segments of the path, reducing the energy consumption. We also propose an improvement for the energy model to predict the overall energy of the paths. We compare E-Spiral and E-BF algorithms in simulations over more than 3500 polygonal areas with different characteristics, such as vertices, irregularity, and size. Results showed that E-Spiral outperforms E-BF in all the cases, providing an effective energy saving even in the worst scenario with a percentage improvement of <inline-formula><tex-math notation="LaTeX">$10.37\%$</tex-math> </inline-formula> up to the best case with <inline-formula><tex-math notation="LaTeX">$\text{16.1}\%$</tex-math> </inline-formula> of improvement. Real flights performed with a quadrotor state the effectiveness of the E-Spiral over E-BF in two areas, presenting an improvement of <inline-formula><tex-math notation="LaTeX">$\text{9}\%$</tex-math> </inline-formula> in the time and <inline-formula><tex-math notation="LaTeX">$\text{7.7}\%$</tex-math></inline-formula> in the energy. The improved energy model increases the time and the energy estimation precision of <inline-formula> <tex-math notation="LaTeX">$\text{13.24}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX"> $\text{13.41}\%$</tex-math></inline-formula>, respectively. <s> BIB002
This paper presents a survey on coverage path planning with unmanned aerial vehicles, addressing simple geometric flight patterns, such as back-and-forth and spiral, and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to the classical taxonomy defined by Choset BIB001 ; no decomposition, exact and approximate cellular decomposition. The study contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. We also present performance metrics usually applied to evaluate the success of the coverage missions. The limited endurance of UAVs is the major concern to be overcome to perform more complex coverage missions. Some authors employ multiple vehicles to enhance the coverage performance in such missions, splitting long and high energy demand flights in more feasible flights. However, this technique usually demands computational complexity to solve coordination and communication issues. This cooperative context still lacks a more robust solution to handle problems more autonomously. The area of interest is usually discretized, divided and assigned to the members of a team using a centralized decision-making process. Moreover, communication between vehicles and a ground control station is required for coordination, which is not robust to failures or feasible in real-world scenarios. The proposed approaches aim to minimize the path length, the mission execution time, and the number of turning maneuvers to indirectly save energy. However, in some cases, performance metrics such as path length and energy consumption may be conflicting, once shorter paths may contain more abrupt maneuvers consuming more energy. As stated by Di Franco and Buttazzo  and Cabreira et al. BIB002 another issues, such as vehicle dynamics, turning angle, and optimal speed should be considered to minimize energy consumption. But these energy-aware approaches are still restricted to regular scenarios considering only simple flight patterns. We recently proposed a state-of-the-art energy-aware spiral coverage algorithm for regular areas and we are currently going forward to a global solution considering more complex scenarios with a grid-based method. Furthermore, we are also developing a decentralized approach for cooperative coverage path planning with UAVs considering communication and energy constraints. In the future, we intend to develop an efficient synchronization mechanism among the vehicles to avoid the need for a ground control station. Finally, we are studying the impact of the vehicle dynamics and the external environment conditions in a physics-based energy model for the energy-aware mission planning. With our current survey and future works in coverage path planning problems, we expect to advance the state-of-the-art in real-world feasible mission planning approaches for autonomous aerial vehicles. 