Optimal Coverage Path Research Articles

Information-integration-based optimal coverage path planning of agricultural unmanned systems formations: From theory to practice

Industrial information integration engineering (IIIE) is an innovative research subject for analyzing complicated and large-scale systems. Autonomous and efficient path coverage of unmanned systems formations is an important subject of intelligent industrial agriculture. As one typical kind of complicated systems, agricultural unmanned systems formations are urgently required to optimize their operating trajectories. In this paper, an IIIE design for coverage path planning of the agricultural unmanned systems formations is presented as an IIIE application to verify the entire performances with considering the couplings between the formations and the working environment. In this design, one key concept of field-state iteration for information coupling integration is inherited and introduced in detail. Furthermore, its simulation models were developed based on structure (unmanned system agent structure and formation structure), geometry (map model and graph theory), dynamics (unmanned system agent model and formation model), and control (formation coverage path planning, formation control and trajectory recurrence) in the practice environments. The practice results were analyzed to validate the effectiveness of the proposed information integration design. Further, this paper puts forward a coverage path planning scheme for unmanned systems formations based on the rotating beam and improved probability roadmap algorithms, which can maintain 99.8% coverage rate, 0.08% repetition rate, and 0.007% redundant coverage rate while ensuring the optimal time. Then, two types of three-dimensional practice platform software including CarSim and Gazebo, are selected to graft the proposed algorithm into agricultural tractors formation and plant protection UAVs formation respectively, and the feasibility of the algorithm is verified under the condition closest to the real environment. Multiple experimentalresults demonstrate that the algorithm proposed in this paper has superior feasibility for engineering practice.

Optimal coverage path planning for USV-assisted coastal bathymetric survey: Models, solutions, and lake trials

With portable and cost-effective attributes, unmanned surface vehicles (USVs) are becoming a crucial role in coastal bathymetric surveys. However, in practical applications, the deployment of USVs for bathymetry encounters challenges from multiple facets, including intricacies of coastline shapes, determination of optimal coverage paths, and the sub-region sequencing. To address these issues, this paper introduces a novel coverage planning scheme designed for a coastal bathymetric survey in a lake region. The scheme commences with the Control Point Douglas-Peucker algorithm that effectively utilizes control points to envelope complex coastal lines, ensuring coastline accuracy while facilitating cellular decomposition. Subsequently, an optimal coverage strategy, characterized by fast convergence, complete coverage, and solution optimality, is meticulously designed to generate the most effective back-and-forth survey paths. Finally, by applying the mapping mechanism and fuzzy rules, a Fuzzy Biased Random Key Evolutionary Algorithm is devised to determine the optimal sequence of survey areas. Extensive simulation studies and comparative evaluations substantiate the superiority of the proposed scheme over existing state-of-the-art methods across various problem variants. In addition, real-world lake trials with USV SL-20 are conducted, providing empirical validation and emphasizing the augmented practicality of implementing the proposed methodology in engineering applications.

Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints

Complete coverage path planning (CCPP) is vital in mobile robot applications. Optimizing CCPP is particularly significant in precision agriculture, where it enhances resource utilization, reduces soil compaction, and boosts crop yields. This work offers a comprehensive approach to CCPP for agricultural vehicles with curvature constraints. Our methodology comprises four key stages. First, it decomposes complex agricultural areas into simpler cells, each equipped with guidance tracks, forming a fixed track system. The subsequent route planning and smooth path planning stages compute a path that adheres to path constraints, optimally traverses the cells, and aligns with the track system. We use the generalized traveling salesman problem (GTSP) to determine the optimal traversing sequence. Additionally, we introduce an algorithm for calculating paths that are both smooth and curvature-constrained within individual cells, as well as paths that enable seamless transitions between cells, resulting in a smooth, curvature-constraint coverage path. Our modular approach allows method flexibility at each step. We evaluate our method on real agricultural fields, demonstrating its effectiveness in minimizing path length, ensuring efficient coverage, and adhering to curvature constraints. This work establishes a strong foundation for precise and efficient agricultural coverage path planning, with potential for further real-world applications and enhancements.

Optimal Coverage Path Planning in a Wireless Sensor Network for Intelligent Transportation System

With the enhancement of the intelligent and communication technology, an intelligent transportation plays a vital role to facilitate an essential service to many people, allowing them to travel quickly and conveniently from place to place. Wireless sensor networks (WSNs) are well-known for their ability to detect physical significant barriers due to their diverse movement, self-organizing capabilities, and the integration of this mobile node on the intelligent transportation system to gather data in WSN contexts is becoming more and more popular as these vehicles proliferate. Although these mobile devices might enhance network performance, however it is difficult to design a suitable transportation path with the limited energy resources with network connectivity. To solve this problem, we have proposed a novel itinerary planning schema data gatherer (IPS-DG) model. Furthermore, we use the path planning module (PPM) which finds the transportation path to travel the shortest distance. We have compared our results under different aspect such as life span, energy consumption, and path length with Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-Hop Weighted Revenue (MWR), Single-Hop Data Gathering Procedure (SHDGP). Our model outperforms in terms of energy usage, shortest path, and longest life span of with LEACH, MWR, SHDGP routing protocols.

Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning

In this letter, we tackle the problem of planning an optimal coverage path for a robot operating indoors. Many existing approaches attempt to discourage turns in the path by covering the environment along the least number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coverage lines</i> , i.e., straight-line paths. This is because turning not only slows down the robot but also negatively affects the quality of coverage, e.g., tools like cameras and cleaning attachments commonly have poor performance around turns. The problem of minimizing coverage lines however is typically solved using heuristics that do not guarantee optimality. In this work, we propose a turn-minimizing coverage planning method that computes the optimal number of axis-parallel (horizontal/vertical) coverage lines for the environment in polynomial time. We do this by formulating a linear program (LP) that optimally partitions the environment into axis-parallel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ranks</i> (non-intersecting rectangles of width equal to the tool width). We then generate coverage paths for a set of real-world indoor environments and compare the results with state-of-the-art coverage approaches.

Algorithms for maximum internal spanning tree problem for some graph classes

For a given graph G, a maximum internal spanning tree of G is a spanning tree of G with maximum number of internal vertices. The Maximum Internal Spanning Tree (MIST) problem is to find a maximum internal spanning tree of the given graph. The MIST problem is a generalization of the Hamiltonian path problem. Since the Hamiltonian path problem is NP-hard, even for bipartite and chordal graphs, two important subclasses of graphs, the MIST problem also remains NP-hard for these graph classes. In this paper, we propose linear-time algorithms to compute a maximum internal spanning tree of cographs, block graphs, cactus graphs, chain graphs and bipartite permutation graphs. The optimal path cover problem, which asks to find a path cover of the given graph with maximum number of edges, is also a well studied problem. In this paper, we also study the relationship between the number of internal vertices in maximum internal spanning tree and number of edges in optimal path cover for the special graph classes mentioned above.

An optimal coverage path plan for an autonomous vehicle based on polygon decomposition and ant colony optimisation

This paper proposes a coverage path planner for the autonomous vehicle used in inspection operation. The proposed coverage path planner is developed based on a back and forth pattern (equivalent to boustrophedon pattern), producing the output path for the inspection operation with the least trajectory length and number of turns covering the entire workspace. Implementing a back and forth pattern on an arbitrary polygon (extracted from workspace boundaries) is not always viable due to the existence of concave vertices in the polygon. So, this work proposes the tree search model based on the polygon width and back and forth pattern’s sweep line, which decomposes the input polygon in numerous ways. The Dijkstra algorithm solves this tree model to obtain back and forth pattern viable polygons with the least number of turns. Later, the vehicle visits these polygons one by one during the inspection operation. The order in which polygon visits are made by the vehicle affects the overall trajectory length. So, this work proposes the ant colony optimisation based trajectory length optimiser, which finds the polygon visiting order that yields minimum trajectory length. The developed coverage path planner is versatile, and it applies to any autonomous vehicle that operates in the planar region.

Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations

The number of casualties in the maritime sector is increasing consistently. To reduce the scope of search area extensions in the event of a maritime accident, maritime authorities and operation centers are currently trying to develop a quick search for survivors at sea using unmanned aerial vehicles (UAVs) in maritime search and rescue (SAR) operations. Here, we propose a two-phase method for solving the coverage path planning (CPP) problem of multiple-UAV areas in maritime SAR. In phase 1, we propose a grid-based area decomposition method that minimizes the decomposed search area to transform the search area into a graph made up of vertices and edges. In phase 2, we formulate a mixed-integer linear programming (MILP) model to derive an optimal coverage path that minimizes the completion time. To solve the model for large-scale instances, a randomized search heuristic (RSH) algorithm is developed. We conducted extensive numerical experiments to validate the performance of the algorithm. Experimental results show that the RSH yields a better solution with an approximately 0.7% optimality gap within a much shorter computation time than that of a commercial solver. In addition, our grid-based CPP algorithm outperforms those used in previous research with respect to the solution quality. Furthermore, we showed the results of real flight experiments in the marine field using the proposed algorithm.

Efficient Coverage Path Planning for Mobile Disinfecting Robots Using Graph-Based Representation of Environment.

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.

Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm

This paper presents new optimal offline approaches to solve the coverage path planning problem. A novel hybrid genetic algorithm (HGA), which uses, the turn-away starting point and backtracking spiral algorithms for performing local search, is proposed for grid-based environmental representations. The HGA algorithm is validated using the following three different fitness functions: the number of cell visits, traveling time, and a new energy fitness function based on experimentally acquired energy values of fundamental motions. Computational results show that compared to conventional methods, HGA improves paths up to 38.4%; moreover, HGAs have a consistent fitness for different starting positions in an environment. Furthermore, experimental results prove the validity of the fitness function.

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.

A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

Dynamic area coverage is widely used in military and civil fields. Improving coverage efficiency is an important research direction for multi-agent dynamic area coverage. In this paper, we focus on the non-optimal coverage problem of free dynamic area coverage algorithms. We propose a distributed dynamic area coverage algorithm based on reinforcement learning and a $\gamma $ -information map. The $\gamma $ -information map can transform the continuous dynamic coverage process into a discrete $\gamma $ point traversal process, while ensuring no-hole coverage. When agent communication covers the whole target area, agents can obtain the global optimal coverage strategy by learning the whole dynamic coverage process. In the event that communication does not cover the whole target area, agents can obtain a local optimal coverage strategy; in addition, agents can use the proposed algorithm to obtain a global optimal coverage path through off-line planning. Simulation results demonstrate that the required time for area coverage with the proposed algorithm is close to the optimal value, and the performance of the proposed algorithm is significantly better than the distributed anti-flocking Algorithms for dynamic area coverage.

Equitable persistent coverage of non-convex environments with graph-based planning

In this article, we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring every point to be constantly revisited. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared with other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.

Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

A new method using knowledge reasoning techniques for improving robot performance in coverage path planning

Robots are a rapidly evolving development field encompassing variable domains ranging from industrial robots to empathetic robots for human companions. Future robots will be highly dependent on the ability to understand, interpret, and generate a representation of the environment in which they are operating, ideally in both a human and machine-readable formalism. An important element in this process lies in Path Planning (PP) with obstacle avoidance in dynamic environments (including cleaning and monitoring in robotics) to identify optimal coverage paths. The study in this paper presents a new approach which combines knowledge reasoning techniques with Breadth First Search to find the optimal path for a cleaning robot in a dynamic environment. This approach is used to apply knowledge inference with conventional coverage PP algorithms to enable robot control and avoid obstacles with optimal coverage PP. The experimental results show that using the proposed approach a robot avoids fixed and mobile obstacles, optimal PP reducing both computational cost and time. When compared to other current approaches, the proposed approach with high-coverage rate and low-repetition rate in coverage performs better than the conventional robot algorithms.

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

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.

Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree

The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal path, which contributes to minimizing the energy/time consumption. For the multirobot case, first the DARP (Divide Areas based on Robots Initial Positions) algorithm is utilized to divide the area into separate equal subareas, so much so that it transforms the mCPP problem into several CPP problems, degrading the computation complexity. During the second phase, spanning tree in each subarea is constructed by the aforementioned algorithm. In the last phase, the specific end nodes are exchanged among subareas to achieve ideal-shaped spanning trees, which can also decrease the number of turns in coverage path. And the complete algorithms are proven to be approximately polynomial algorithms. Finally, the simulation confirms the complete algorithms’ advantages: complete coverage, nonbacktracks, minimum length, zero preparation time, and the least number of turns.

DARP: Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning

This paper deals with the path planning problem of a team of mobile robots, in order to cover an area of interest, with prior-defined obstacles. For the single robot case, also known as single robot coverage path planning (CPP), an źź(n) optimal methodology has already been proposed and evaluated in the literature, where n is the grid size. The majority of existing algorithms for the multi robot case (mCPP), utilize the aforementioned algorithm. Due to the complexity, however, of the mCPP, the best the existing mCPP algorithms can perform is at most 16 times the optimal solution, in terms of time needed for the robot team to accomplish the coverage task, while the time required for calculating the solution is polynomial. In the present paper, we propose a new algorithm which converges to the optimal solution, at least in cases where one exists. The proposed technique transforms the original integer programming problem (mCPP) into several single-robot problems (CPP), the solutions of which constitute the optimal mCPP solution, alleviating the original mCPP explosive combinatorial complexity. Although it is not possible to analytically derive bounds regarding the complexity of the proposed algorithm, extensive numerical analysis indicates that the complexity is bounded by polynomial curves for practical sized inputs. In the heart of the proposed approach lies the DARP algorithm, which divides the terrain into a number of equal areas each corresponding to a specific robot, so as to guarantee complete coverage, non-backtracking solution, minimum coverage path, while at the same time does not need any preparatory stage (video demonstration and standalone application are available on-line http://tinyurl.com/DARP-app).

C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty

We introduce a new problem of continuous, coverage-aware trajectory optimization under localization and sensing uncertainty. In this problem, the goal is to plan a path from a start state to a goal state that maximizes the coverage of a user-specified region while minimizing the control costs of the robot and the probability of collision with the environment. We present a principled method for quantifying the coverage sensing uncertainty of the robot. We use this sensing uncertainty along with the uncertainty in robot localization to develop C-OPT, a coverage-optimization algorithm which optimizes trajectories over belief-space to find locally optimal coverage paths. We highlight the applicability of our approach in multiple simulated scenarios inspired by surveillance, UAV crop analysis, and search-and-rescue tasks. We also present a case study on a physical, differential-drive robot. We also provide quantitative and qualitative analysis of the paths generated by our approach.

Mix-opt: A new route operator for optimal coverage path planning for a fleet in an agricultural environment

To accomplish agriculture tasks, a field is usually divided into tracks based on the implement width. The order in which the crop tracks are covered during this process is critical because it directly affects the distances travelled by the agricultural machines while completing the task and, consequently, soil compaction and inputs. Identifying the best tracks for a set of vehicles to completely cover a field can be formulated as a capacitated vehicle routing problem (CVRP), in which tracks can be viewed as the customers of the CVRP problem. In other words, given a set of n tracks and m vehicles, the objective is to determine a set of routes such that each track is covered exactly once by any of the involved vehicles while minimizing the total cost of covering all the tracks. There are many metaheuristic optimisation methods that address the CVRP problem by using operators to iteratively improve the routes. Most of these operators consist of easy elementary operations such as relocations, swaps or inversions of the order in which customers in the route are visited. In this paper, a new operator, named Mix-opt, is proposed with the aim of accelerating the convergence of metaheuristic optimisation methods and make them less dependent on the operator chosen on routing problems. The proposed operator combines and extends some of the features of the most commonly used route operators by integrating the best-performing elementary operations on which they are based. Further variants of those elementary operations were tested, such as the use of different numbers of elements in the relocations or swaps or reverse orders as well as combining the operations with local searches. The best variants were selected for integration into the proposed operator. Furthermore, Mix-opt was compared against well-established operators by integrating each of them into a Simulated Annealing algorithm and solving well-known CVRP benchmarks and a typical and complex agricultural routing problem. Finally, the proposed operator was applied to be integrated into an agricultural route planner to identify the best routes in some illustrative agricultural problems.All tests demonstrated that Mix-opt, on average, outperforms existing approaches for solving general routing problems as well as a broad spectrum of agricultural routing situations. This helps to better route plan in agricultural contexts, even better than other approaches in a very short time, which is interesting to route plan in real time, for example, because one vehicle may fail during the execution and then it is necessary to route the plan again and very fast to distribute the remaining part of the global task among the rest of the vehicles in the fleet.