Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments.

In this paper, a coverage path planning (CPP) scheme for an unmanned aerial vehicle (UAV) in coverage observation is proposed. The algorithms begin by decomposing the task region into several unit circles. The positions of the unit circles are optimized by the improved Particle-Glowworm Swarm Optimization algorithm based on the area size of the unit circle and task region. Then the connection sequence of all unit circles is solved by the Traveling Salesman Problem (TSP) algorithm according to the shortest path principle. Further, the cubic Bézier curve is used to fit the centers of all unit circles to get a smooth coverage path. Finally, two curvature adjustment algorithms are proposed to optimize curvature. The main contribution of the paper is to present a scheme that generates a smooth coverage path while providing coverage optimization and path length optimization. Computer simulations validate the effectiveness of the algorithms.

Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm

Coverage path planning is one of the important issues for robot mowers, which brings great convenience to our life. In this paper, hierarchical partitioning strategy is employed to fulfill robot's environment modeling and the double heuristic optimization algorithms have been adopted to plan optimal coverage paths. Ant Colony Optimization (ACO) is used for global path planning in the upper layer, and Tabu Search (TS) is used for local coverage planning in the lower layer. Finally, simulation experiments are carried out and the results show the proposed method obtains satisfactory coverage path planning.

In the field of aerial photography, UAVs can be used as an ideal carrier of sensors. It is meaningful to plan its coverage path combined with the sensor model. Previous works mainly used decentralized coverage to realize UAV formation cooperative operation, but for the aerial photography scene of a single task, the decentralized coverage algorithm is not effective. In this paper, a centralized coverage algorithm of UAV formation for aerial photography is proposed, which is divided into two sub-problems: coverage path planning and formation control. In the coverage path planning, due to high repetition rate and excessive number of turns of the traditional PRM algorithm, the constraint of the number of turns is introduced, and the back-and-forth mode is used to improve it. In formation control, in order to more accurately evaluate the performance of the coverage path, it refers to the consensus theory and combines the artificial potential field method to guide the reverse-V UAV formation to follow the coverage path. Multi-group simulations show that the proposed algorithm is feasible to implement the concentrated coverage for aerial photography under the comprehensive consideration of path length, number of turns, calculation time, coverage rate and repeating rate.

Coverage path planning solves the problem of moving an effector over all points within a specific region with effective routes. Most existing studies focus on geometric constraints, often overlooking robot-specific features, like the available energy, weight, maximum speed, sensor resolution, etc. This paper proposes a coverage path planning algorithm for Unmanned Aerial Vehicles (UAVs) that minimizes energy consumption while satisfying a set of other requirements, such as coverage and observation resolution. To deal with these issues, we propose a novel energy-optimal coverage path planning framework for monitoring tasks. Firstly, the 3D terrain’s spatial characteristics are digitized through a combination of parametric modeling and meshing techniques. To accurately estimate actual energy expenditure along a segmented trajectory, a power estimation module is introduced, which integrates dynamic feasibility constraints into the energy computation. Utilizing a Digital Surface Model (DSM), a global energy consumption map is generated by constructing a weighted directed graph over the terrain. Subsequently, an energy-optimal coverage path is derived by applying a Genetic Algorithm (GA) to traverse this map. Extensive simulation results validate the superiority of the proposed approach compared to existing methods.

In this paper, an offline flight planner, that generates an optimal coverage path for UAVs to support autonomous fruit counting in agriculture fields or orchards, is presented. The coverage path planning is the process of searching the route which covers every point of a given region of interest. The approach consists mainly of three steps: UAV photogrammetry, ROI map derivation and coverage path planning generation based on Rotating Calipers Algorithm. The proposed approach, as a useful tool, allows the UAV systems to define and generate a coverage path while including the takeoff and landing points. The resultant path can be dispatched to the UAV operator for the coverage area fruit counting.

Over the last few decades, the agricultural industry has made significant advances in autonomous systems, such as wheeled robots, with the primary objective of improving efficiency while reducing the impact on the environment. In this context, determining a path for the robot that optimizes coverage while taking into account topography, robot characteristics, and operational requirements, is critical. In this paper, we present H‐CCPP, a novel hybrid method that combines the comprehensive coverage benefits of our previous approach O‐CCPP with the computational efficiency of the Fields2Cover algorithm. Besides optimizing coverage area, overlaps, and overall travel time, it significantly improves the computation process, and enhances the flexibility of trajectory generation. H‐CCPP also considers terrain inclination to address soil erosion and energy consumption. In an effort to support this innovative approach, we have also created and made available a public data set that includes both 2D and 3D representations of 30 agricultural fields. This resource not only allows us to illustrate the effectiveness of our approach but also provides invaluable data for future research in complete coverage path planning (CCPP) for modern agriculture.

The effective disinfection of hospitals is paramount in lowering the COVID-19 transmission risk to both patients and medical personnel. Autonomous mobile robots can perform the surface disinfection task in a timely and cost-effective manner, while preventing the direct contact of disinfecting agents with humans. This paper proposes an end-to-end coverage path planning technique that generates a continuous and uninterrupted collision-free path for a mobile robot to cover an area of interest. The aim of this work is to decrease the disinfection task completion time and cost by finding an optimal coverage path using a new graph-based representation of the environment. The results are compared with other existing state-of-the-art coverage path planning approaches. It is shown that the proposed approach generates a path with shorter total travelled distance (fewer number of overlaps) and smaller number of turns.

<p>The effective disinfection of hospitals is paramount in lowering the COVID-19 transmission risk to both patients and medical personnel. Autonomous mobile robots can perform the surface disinfection task in a timely and cost-effective manner, while preventing the direct contact of disinfecting agents with humans. This paper proposes an end-to-end coverage path planning technique that generates a continuous and uninterrupted collision-free path for a mobile robot to cover an area of interest. The aim of this work is to decrease the disinfection task completion time and cost by finding an optimal coverage path using a new graph-based representation of the environment. The results are compared with other existing state-of-the-art coverage path planning approaches. It is shown that the proposed approach generates a path with shorter total travelled distance (fewer number of overlaps) and smaller number of turns.</p>

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

The ability to plan online paths, that passes through points of interest for coverage of an unknown environment is essential to robot autonomy. Existing state of the art coverage path planning techniques for active exploration (Frontier and Next-Best-View) are occupancy grid maps based, which do not scale well in large area environments, suffer from long term drift and are not robust at large values of sensor range noise, not address robot navigation safety. We proposed a Coverage Path Planning (CPP) algorithm, which is based on a coordinate system based topological map of the admissible locations of the environment. This algorithm scales well because of the sparse representation of large area environments, it is robust even at large value of sensor noise. This CPP is model free, computationally efficient and consider the robot safety by only considering the admissible locations for navigation. Viewpoints within the unexplored regions of robot workspace are chosen for traversal to cover the environment. The proposed procedure is experimentally verified in a 15 different 3D-gazebo world models using a 2-wheeled differential mobile robot with a Lidar sensor, using ROS framework for implementation. Simulation results shows the effectiveness of this algorithm for the real-world applications.

Autonomous underwater cleaning in water pools requires reliable perception, efficient coverage path planning, and robust control. However, existing autonomous underwater vehicle (AUV) cleaning systems often suffer from fragmented software frameworks that limit end-to-end performance. To address these challenges, this paper proposes an integrated vision-based autonomous underwater cleaning system that combines global-camera AprilTag localization, YOLOv8-based dirt detection, and a multi-scale A* coverage path planning algorithm. The perception and planning modules run on a host computer system, while a NanoPi-based controller executes motion commands through a lightweight JSON-RPC protocol over Ethernet. This architecture ensures real-time coordination between visual sensing, planning, and hierarchical control. Experiments conducted in a simulated pool environment demonstrate that the proposed system achieves accurate localization, efficient planning, and reliable cleaning without blind spots. The results highlight the effectiveness of integrating vision, multi-scale planning, and lightweight embedded control for autonomous underwater cleaning tasks.

The advanced and highly mobile design of UAVs has led to a wide range of applications in fields such as battlefield monitoring, intelligent agriculture, photogrammetry, and disaster management, all of which are based on the coverage path planning problem (CPP). In recent years, there have been numerous researches on UAV coverage path planning problem. It is very important to plan an effective flight path for UAV to cover the whole target area completely. The purpose of this paper is to summarize and analysis of the existing literature involving different methods to solve the problem of UAV coverage path planning, we will survey the coverage path planning algorithm is mainly divided into two categories: one is geometric algorithm based on polygon region decomposition, and the second is heuristic algorithm for variant TSP and GTSP problems based on grid division and gives out the summary and analysis method for every type of coverage.

Automating mosquito control is a pivotal advancement in the pest control industry with the primary objective of mitigating the prevalence of vector-borne diseases. Recent progress in pest control robotics has enabled the automation of mosquito activity restrictions. However, existing robotic solutions have exhibited limitations in effectively addressing mosquito control while lacking a sensitive strategy for maximizing area coverage with crowded areas as a priority. In response to these challenges, this article proposes a novel human-first approach for complete coverage path planning (HFA-CCPP) that leverages the Glasius Bio-inspired Neural Network (GBNN) to cover areas that simulate and consider human activity patterns systematically. In this study, a mosquito-capturing robot, Dragonfly, is demonstrated with HFA-CCPP. This article provides an in-depth exploration of the technical intricacies of the proposed solution. The efficacy of the proposed technique is evaluated in terms of total area coverage and times taken to cover the high human activity region in simulation and real-world environments by comparing results with traditional GBNN. Across all scenarios, the proposed HFA-CCPP surpasses the traditional method by delivering efficient area coverage with minimal time for human-dense area coverage and efficiency in mosquito trapping. This finding stands as a newfound direction in automated mosquito control, holding great potential for curbing vector-borne diseases.