scholarly journals Energy-aware Coverage Path Planning for Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo Ferreira Jr.
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Partial Information ◽  
State Of The Art ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Aerial Vehicles ◽  
Novel Approaches ◽  
Grid Based

Coverage Path Planning (CPP) problem is a motion planning subtopic in robotics, where it is necessary to build a path for a robot to explore every location in a given scenario. Unmanned Aerial Vehicles (UAV) have been employed in several applications related to the CPP problem. However, one of the significant limitations of UAVs is endurance, especially in multi-rotors. Minimizing energy consumption is pivotal to prolong and guarantee coverage. Thus, this work proposes energy-aware coverage path planning solutions for regular and irregular-shaped areas containing full and partial information. We consider aspects such as distance, time, turning maneuvers, and optimal speed in the UAV’s energy consumption. We propose an energy-aware spiral algorithm called E-Spiral to perform missions over regular-shaped areas. Next, we explore an energy-aware grid-based solution called EG-CPP for mapping missions over irregular-shaped areas containing no-fly zones. Finally, we present an energy-aware pheromone-based solution for patrolling missions called NC-Drone. The three novel approaches successfully address different coverage path planning scenarios, advancing the state-of-the-art in this area.

scholarly journals Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 4 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo R. Ferreira Jr.
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Performance Metrics ◽  
Partial Information ◽  
Convex Polygons ◽  
Cellular Decomposition ◽  
Coverage Path Planning ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Smart Farming

Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

scholarly journals A Design Methodology for Energy-Aware Processing in Unmanned Aerial Vehicles

2022 ◽  
Vol 27 (1) ◽  
pp. 1-20
Author(s):  
Jingyu He ◽  
Yao Xiao ◽  
Corina Bogdan ◽  
Shahin Nazarian ◽  
Paul Bogdan
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Homeland Security ◽  
Design Methodology ◽  
Energy Savings ◽  
Visual Navigation ◽  
Parallel Execution ◽  
Disaster Mitigation ◽  
Energy Aware ◽  
Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have rapidly become popular for monitoring, delivery, and actuation in many application domains such as environmental management, disaster mitigation, homeland security, energy, transportation, and manufacturing. However, the UAV perception and navigation intelligence (PNI) designs are still in their infancy and demand fundamental performance and energy optimizations to be eligible for mass adoption. In this article, we present a generalizable three-stage optimization framework for PNI systems that (i) abstracts the high-level programs representing the perception, mining, processing, and decision making of UAVs into complex weighted networks tracking the interdependencies between universal low-level intermediate representations; (ii) exploits a differential geometry approach to schedule and map the discovered PNI tasks onto an underlying manycore architecture. To mine the complexity of optimal parallelization of perception and decision modules in UAVs, this proposed design methodology relies on an Ollivier-Ricci curvature-based load-balancing strategy that detects the parallel communities of the PNI applications for maximum parallel execution, while minimizing the inter-core communication; and (iii) relies on an energy-aware mapping scheme to minimize the energy dissipation when assigning the communities onto tile-based networks-on-chip. We validate this approach based on various drone PNI designs including flight controller, path planning, and visual navigation. The experimental results confirm that the proposed framework achieves 23% flight time reduction and up to 34% energy savings for the flight controller application. In addition, the optimization on a 16-core platform improves the on-time visit rate of the path planning algorithm by 14% while reducing 81% of run time for ConvNet visual navigation.

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