VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

Abstract

The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown significantly in tasks involving terrain coverage such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered by the flight path a challenging objective, especially when the area to be monitored is irregular, large and includes many blind spots. Accordingly, state-of-the-art total viewshed algorithms can be of great help to analyse large areas and find new paths providing maximum visibility. This paper shows how the total viewshed computation is a valuable tool for generating paths that provide maximum visibility during a flight. We introduce a new heuristic called Visibility-based Path Planning (VPP) that offers a different solution to the path planning problem. VPP identifies the hidden areas from the target territory to generate a path that provides the highest visual coverage. Simulation results show that VPP can cover up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located within the province of Malaga (Spain) and chosen as regions of interest. In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.

Efficient Path Planning Algorithm for Mobile Robots Performing Floor Cleaning Like Operations

“…In [33] a non-user-oriented path planning scheme using a family of spiral-shaped curves for moving aerial base stations (ABSs), aiming to optimize mechanical energy and achieve uniform coverage across a cell, demonstrating significant power consumption savings compared to existing radial paths is presented. In [36], the authors propose a non-user-oriented path planning scheme based on spiral-shaped curves, aiming to achieve energy efficiency and uniform coverage for all users within a cell. article addresses path planning for multiple UAVs to achieve sweep coverage, especially focusing on forest fire early warning and monitoring.…”

“…article addresses path planning for multiple UAVs to achieve sweep coverage, especially focusing on forest fire early warning and monitoring. A Predator-Prey reward-based Q-Learning CPP, overcoming local optima challenges is studied in [35] and [36] introduces a visibility-based path planning (VPP) heuristic for optimizing visibility during UAV flights.…”

An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments

“…Conversely, ref. [33] employed a viewshed algorithm to generate UAV paths that maximize the visual coverage of hidden areas in a target territory.…”

Path Planning Research of a UAV Base Station Searching for Disaster Victims’ Location Information Based on Deep Reinforcement Learning

“…The UAV path planning method using an improved artificial potential field was proposed in [ 6 , 7 ], which effectively solved the path planning of multi-UAS and UAV obstacle avoidance against dynamic obstacles by introducing a rotating potential field and Markov prediction model. In [ 8 , 9 , 10 ], an improved heuristic algorithm was proposed to solve the problem of UAV path planning and mission area coverage in complex environments. To solve the coverage path planning problem for autonomous heterogeneous UAVs over a finite area, an original clustering-based algorithm was designed in [ 11 ], which divided the mission area into clusters to obtain the optimal UAV point-to-point path.…”