Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints

Höffmann, Maria; Patel, Shruti; Büskens, Christof

doi:10.3390/agriculture13112112

Cited by 7 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The above approaches only reduce the searching space, without contributing to the computation of the trajectory. In (3), the feature, without loss of generality, uses a Dijkstralike algorithm [49][50][51][52] at the end to compute the best trajectory. In other words, the foundations and method used to finally locate the best trajectory rely on a variation of the Dijkstra algorithm [53].…”

Section: Resultsmentioning

confidence: 99%

Algorithm Based on Morphological Operators for Shortness Path Planning

Perez-Ramos,

Ramirez-Rosales,

Canton-Enriquez

et al. 2024

Algorithms

View full text Add to dashboard Cite

The problem of finding the best path trajectory in a graph is highly complex due to its combinatorial nature, making it difficult to solve. Standard search algorithms focus on selecting the best path trajectory by introducing constraints to estimate a suitable solution, but this approach may overlook potentially better alternatives. Despite the number of restrictions and variables in path planning, no solution minimizes the computational resources used to reach the goal. To address this issue, a framework is proposed to compute the best trajectory in a graph by introducing the mathematical morphology concept. The framework builds a lattice over the graph space using mathematical morphology operators. The searching algorithm creates a metric space by applying the morphological covering operator to the graph and weighing the cost of traveling across the lattice. Ultimately, the cumulative traveling criterion creates the optimal path trajectory by selecting the minima/maxima cost. A test is introduced to validate the framework’s functionality, and a sample application is presented to validate its usefulness. The application uses the structure of the avenues as a graph. It proposes a computable approach to find the most suitable paths from a given start and destination reference. The results confirm that this is a generalized graph search framework based on morphological operators that can be compared to the Dijkstra approach.

show abstract

Section: Resultsmentioning

confidence: 99%

Algorithm Based on Morphological Operators for Shortness Path Planning

Perez-Ramos,

Ramirez-Rosales,

Canton-Enriquez

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…However, there are cases where that number could be reduced or increased by the system. When there is a narrow area in the field or field boundary corners are sharp, headland passes may intersect or overlap [42]. In this case, the number of headland passes set by the user is automatically and gradually reduced by one.…”

Section: Headland Passesmentioning

confidence: 99%

Integrated Route-Planning System for Agricultural Robots

Asiminari,

Moysiadis,

Kateris

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs’ performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation.

show abstract

“…The strategy aims to achieve maximum coverage of the designated space through precise calculation and planning while minimizing path duplication and omissions [1][2][3]. Currently, coverage path planning has shown its importance in multiple practical applications, including precision agriculture [4], automated cleaning [5,6], disaster response and search and rescue [7], patrol monitoring [8,9] and so on.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

Ni,

Gu,

Tang

et al. 2024

Electronics

View full text Add to dashboard Cite

With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.

show abstract

Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints

Cited by 7 publications

References 49 publications

Algorithm Based on Morphological Operators for Shortness Path Planning

Algorithm Based on Morphological Operators for Shortness Path Planning

Integrated Route-Planning System for Agricultural Robots

Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

Contact Info

Product

Resources

About