A Survey on Coverage Path Planning Algorithms for Autonomous Robots in Agriculture

2019 ◽  
Vol 7 (3) ◽  
pp. 815-827
Author(s):  
Kalaivanan Sandamurthy ◽  
Kalpana Ramanujam
Keyword(s):  
Path Planning ◽  
Autonomous Robots ◽  
Coverage Path Planning ◽  
Planning Algorithms

Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys

Robotica ◽  
2018 ◽  
Vol 36 (8) ◽  
pp. 1144-1166 ◽  
Author(s):  
Héctor Azpúrua ◽  
Gustavo M. Freitas ◽  
Douglas G. Macharet ◽  
Mario F. M. Campos
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Robots ◽  
Mineral Exploration ◽  
Statistical Investigation ◽  
Coverage Path Planning ◽  
Simulated Environment ◽  
Outdoor Setting ◽  
Geophysical Surveys ◽  
Multi Robot

SUMMARYThe field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

Coverage path planning algorithms for agricultural field machines

2009 ◽  
Vol 26 (8) ◽  
pp. 651-668 ◽  
Author(s):  
Timo Oksanen ◽  
Arto Visala
Keyword(s):  
Path Planning ◽  
Agricultural Field ◽  
Coverage Path Planning ◽  
Planning Algorithms

scholarly journals Pursuit predation with intermittent locomotion in zebrafish

2020 ◽  
pp. jeb.230623 ◽  
Author(s):  
Alberto P. Soto ◽  
Matthew J. McHenry
Keyword(s):  
Path Planning ◽  
Danio Rerio ◽  
Control Strategy ◽  
Autonomous Robots ◽  
Continuous Motion ◽  
Pure Pursuit ◽  
Burst Phase ◽  
Planning Algorithms ◽  
And Control

The control of a predator's locomotion is critical to its ability to capture prey. Flying animals adjust their heading continuously with control similar to guided missiles. However, many animals do not move with rapid continuous motion, but rather interrupt their progress with frequent pauses. To understand how such intermittent locomotion may be controlled during predation, we examined the kinematics of zebrafish (Danio rerio) as they pursued larval prey of the same species. Like many fishes, zebrafish move with discrete burst-and-coast swimming. We found that the change in heading and tail excursion during the burst phase was linearly related to the prey's bearing. These results suggest a strategy, which we call intermittent pure pursuit, that offers advantages in sensing and control. This control strategy is similar to perception and path-planning algorithms required in the design of some autonomous robots and may be common to a diversity of animals.

scholarly journals Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 46 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore
Keyword(s):  
Path Planning ◽  
Environmental Awareness ◽  
Robotic Systems ◽  
Self Awareness ◽  
Coverage Path Planning ◽  
Knowledge Reasoning ◽  
Operating Environments ◽  
Dynamic Operating ◽  
Planning Algorithms ◽  
Autonomous Robotic Systems

Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and operate in dynamic operating environments. This paper proposes a novel approach to enable autonomous robotic systems to achieve efficient coverage path planning, which combines adaptation with knowledge reasoning techniques and hedge algebras to achieve optimal coverage path planning in multiple decision-making under dynamic operating environments. To evaluate the proposed approach we have implemented it in a mobile cleaning robot. The results demonstrate the ability to avoid static and dynamic (moving) obstacles while achieving efficient coverage path planning with low repetition rates. While alternative current coverage path planning algorithms have achieved acceptable results, our reported results have demonstrated a significant performance improvement over the alternative coverage path planning algorithms.

scholarly journals Research on UAV Path Planning Algorithms

2021 ◽  
Vol 693 (1) ◽  
pp. 012120
Author(s):  
Hao Wang ◽  
Weijun Pan
Keyword(s):  
Path Planning ◽  
Planning Algorithms

scholarly journals R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

2021 ◽  
Vol 13 (8) ◽  
pp. 1525
Author(s):  
Gang Tang ◽  
Congqiang Tang ◽  
Hao Zhou ◽  
Christophe Claramunt ◽  
Shaoyang Men
Keyword(s):  
Path Planning ◽  
Google Earth ◽  
Simulation Experiments ◽  
Minimum Width ◽  
Coverage Path Planning ◽  
Optimal Paths ◽  
Planning Approach ◽  
Polygonal Area ◽  
Aerial Vehicle ◽  
Working Distance

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

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