Energy-Aware Coverage Path Planning of UAVs

By Carmelo Di Franco, Giorgio C. Buttazzo. IEEE International Conference on Autonomous Robot Systems and Competitions, 2015

340 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d8bb3750502a6f22fa6fd6c345c378f767e3aa2e

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In Aerospace Engineering

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Citations in the corpus

Survey on Coverage Path Planning with Unmanned Aerial Vehicles (2019, methodology): “The E-BF [25] and the E-Spiral [40] adopt the energy model proposed by Di Franco and Buttazzo [24] derived from real measurements.”
FarmBeats: An IoT Platform for Data-Driven Agriculture (2017, background): “Recent research proposals like [17] do not guarantee the minimum number of waypoints either.”
Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach (2018, background): “Fewer works study the interplay between UAV’s trajectory (control) and the wireless communication performance such as [7], [13], [14].”
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, background): “Di Franco et al. presented an algorithm for energy-aware path planning with UAVs [153].”
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications (2017, methodology): “The total energy consumption of the rotary wing UAV while moving between two stop locations can be computed as done in [28]:”
Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges (2018, neutral): “IRIS quadrotor Deriving the energy consumption model [113] – – –”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “However, there is no mathematical model on UAV energy consumption suggested in [69], which makes the result difﬁcult to be generalized for other UAVs.”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, background): “It is worth noting that as a related line of research, UAV path planning for dedicated missions without communication performance consideration has been studied in [62]–[65], where the UAV paths were designed such that the UAVs can reach their pre-determined destinations while avoiding collisions wi”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, methodology): “For example, in the works in [67], [68], the energy consumption was modeled based on the acceleration vector and control force, whereas in [69], an empirical energy consumption model was applied for a UAV path planning application that is deployed for aerial imaging where several measurements were…”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, methodology): “In [134], a UAV path planning algorithm for photographic sensing of a given geographical area was proposed.”
Energy-Efficient UAV Communication With Trajectory Optimization (2016, methodology): “1 To the best of our knowledge, the derived model is the first theoretical model that relates the UAV’s energy consumption with its velocity (including both flying speed and direction) and acceleration, whereas existing literature mostly uses heuristic energy consumption models only taking into acco”
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach (2018, background): “Di Franco and Buttazzo [34] proposed an energy model which”