An approach for coverage path planning for UAVs

Le, Hoai Nam; Huang, L.; Li, Xue Jun; Xu, Jianfeng

doi:10.1109/amc.2016.7496385

Cited by 68 publications

(37 citation statements)

References 6 publications

(3 reference statements)

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“…Robotic bush trimming is a task related to the coverage path planning problem that has been extensively studied in the literature (Galceran & Carreras, ). The task is fundamental to several robotic applications: agricultural fields plowing (Jin & Tang, ), crops harvesting (van Henten et al, ), lawn mowing (Arkin, Fekete, & Mitchell, ), inspection of underwater structures (Englot & Hover, ), environment surveillance with unmanned aerial vehicles (UAVs; Nama, Huanga, Lia, & Xub, ), spray‐painting of automotive parts (Sheng, Xi, Song, Chen, & Macneille, ), spray forming (Sheng, Chen, Xi, Chen, & Motivation, ), computer numerical control (CNC) machining (Macleod, Dobie, Pierce, Summan, & Morozov, ), laser cutting (Ma et al, ), and cleaning (Palacin, Palleja, Valganon, Pernia, & Roca, ). The problem is intimately related to the traveling salesman problem (TSP).…”

Section: Introductionmentioning

confidence: 99%

Coverage trajectory planning for a bush trimming robot arm

Kaljaca

Vroegindeweij

Henten

2019

Journal of Field Robotics

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A novel motion planning algorithm for robotic bush trimming is presented. The algorithm is based on an optimal route search over a graph. Differently from other works in robotic surface coverage, it entails both accuracy in the surface sweeping task and smoothness in the motion of the robot arm. The proposed method requires the selection of a custom objective function in the joint space for optimal node traversal scheduling, as well as a kinematically constrained time interpolation. The algorithm was tested in simulation using a model of the Jaco arm and three target bush shapes. Analysis of the simulated motions showed how, differently from classical coverage techniques, the proposed algorithm is able to ensure high tool positioning accuracy while avoiding excessive arm motion jerkiness. It was reported that forbidding manipulation posture changes during the cutting phase of the motion is a key element for task accuracy, leading to a decrease of the tool positioning error up to 90%. Furthermore, the algorithm was validated in a real‐world trimming scenario with boxwood bushes. A target of 20 mm accuracy was proposed for a trimming result to be considered successful. Results showed that on average 82% of the bush surface was affected by trimming, and 51% of the trimmed surface was cut within the desired level of accuracy. Despite the fact that the trimming accuracy turned out to be lower than the stated requirements, it was found out this was mainly a consequence of the inaccurate, early stage vision system employed to compute the target trimming surface. By contrast, the trimming motion planning algorithm generated trajectories that smoothly followed their input target and allowed effective branch cutting.

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Section: Introductionmentioning

confidence: 99%

Coverage trajectory planning for a bush trimming robot arm

Kaljaca

Vroegindeweij

Henten

2019

Journal of Field Robotics

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“…The mission completion time includes flying time and hovering time. We lean on the following formula from [4] to find the completion time denoted τ:…”

Section: Completion Timementioning

confidence: 99%

“…The usage of an Unmanned Aerial Vehicle (UAV) (also known as a drone) has become very fast-growing recently [1]. It pervades many application domains to achieve different missions such as terrain monitoring and smart agriculture [2,3], photogrammetry [4], harsh and disaster environments management [5], smart policing, wildfire tracking [6], and many more. One of the main active research areas for UAV is the Coverage Path Planning (CPP).…”

Section: Introductionmentioning

confidence: 99%

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Ghaddar

Merei

2020

Future Internet

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The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

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“…Grid-based methods are used over approximate areas to generate coverage paths for the UAVs [36]. Convex decomposition transforms the irregular shaped AOI into regular shaped cells in order to reduce the number of turns [37].…”

Section: Background and Related Workmentioning

confidence: 99%

A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

Majeed

Lee

2019

Applied Sciences

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This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.

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An approach for coverage path planning for UAVs

Cited by 68 publications

References 6 publications

Coverage trajectory planning for a bush trimming robot arm

Coverage trajectory planning for a bush trimming robot arm

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

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