Complete Coverage Path Planning Research Articles

Spiral complete coverage path planning based on conformal slit mapping in multi-connected domains

The generation of smoother and shorter spiral complete coverage paths in multi-connected domains is a crucial research topic in path planning for robotic cavity machining and other related fields. Traditional methods for spiral path planning in multi-connected domains typically incorporate a subregion division procedure that leads to excessive subregion bridging, requiring longer, more sharply turning, and unevenly spaced spirals to achieve complete coverage. To address this issue, this paper proposes a novel spiral complete coverage path planning method using conformal slit mapping. It takes advantage of the fact that conformal slit mapping can transform multi-connected domains into regular disks or annuluses without the need for subregion division. Firstly, a slit mapping calculation technique is proposed for segmented cubic spline boundaries with corners. Secondly, a spiral path spacing control method is developed based on the maximum inscribed circle radius between adjacent conformal slit mapping iso-parameters. Thirdly, the spiral coverage path is derived by offsetting iso-parameters. Numerical experiments indicate that our method shares a comparable order-of-magnitude in computation time with the traditional PDE-based spiral complete coverage path method, but it excels in optimizing total path length, smoothness, and spacing consistency. Finally, we performed experiments on cavity milling and dry runs to compare the new method with the traditional PDE-based method in terms of machining duration and steering impact, respectively. The comparison reveals that, with both algorithms achieving complete coverage, the new algorithm reduces machining time and steering impact by 12.34% and 22.78%, respectively, compared with the traditional PDE-based method.

Vision detection and path planning of mobile robots for rebar binding

AbstractFocused on the problems of cumbersome operation, low efficiency, and high cost in the traditional manual rebar binding process, we propose a mobile robot vision detection and path‐planning method for rebar binding to realize automated rebar binding by combining deep learning and path‐planning technology. A MobileNetV3‐SSD rebar binding crosspoints recognition model is built based on TensorFlow deep learning framework, and a crosspoints localization method combining control factor α and feature projection curve is introduced to achieve the localization of unbound crosspoints. In addition, A back‐and‐forth path‐planning algorithm with priority constraints combined with dead zone escape algorithm based on improved A* is proposed to achieve complete coverage path planning of the working area and path transfer of the dead zone. In the field test of the robot prototype, the classification accuracy and localization accuracy reached 94.40% and 90.49%, and the robot was able to reach complete coverage path planning successfully. The experimental results show that the visual detection method can achieve fast, noncontact and intelligent recognition of rebar binding crosspoints, which has good robustness and application value. At the same time, the proposed path‐planning method has higher efficiency in the execution of robot complete coverage path planning, and meets the basic requirements of path planning for rebar binding process.

A Complete Coverage Path Planning Approach for an Autonomous Underwater Helicopter in Unknown Environment Based on VFH+ Algorithm

An Autonomous Underwater Helicopter (AUH) is a disk-shaped, multi-propelled Autonomous Underwater Vehicle (AUV), which is intended to work autonomously in underwater environments. The near-bottom area sweep in unknown environments is a typical application scenario, in which the complete coverage path planning (CCPP) is essential for AUH. A complete coverage path planning approach for AUH with a single beam echo sounder, including the initial path planning and online local collision avoidance strategy, is proposed. First, the initial path is planned using boustrophedon motion. Based on its mobility, a multi-dimensional obstacle sensing method is designed with a single beam range sonar mounted on the AUH. The VFH+ algorithm is configured for the heading decision-making procedure before encountering obstacles, based on their range information at a fixed position. The online local obstacle avoidance procedure is simulated and analyzed with variations of the desired heading direction and corresponding polar histograms. Finally, several simulation cases are set up, simulated and compared by analyzing the heading decision in front of different obstacle situations. The simulation results demonstrate the feasibility of the complete coverage path planning approach proposed, which proves that AUH completing a full coverage area sweep in unknown environments with a single beam sonar is viable.

Optimal guidance track generation for precision agriculture: A review of coverage path planning techniques

AbstractThe Complete Coverage Path Planning (CCPP) problem is a subfield of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soil compaction, and increased yields. This paper reviews the CCPP problem in the context of machines operating in agricultural fields and proposes a methodological approach consisting of three steps: Generating the Guidance Tracks (i.e., the track system along which the path should be oriented), determining the traversing sequence through these tracks, and planning a smooth and drivable path. This paper provides an in‐depth review of optimization‐based approaches that deal with the first step, the generation of the guidance track system. Thereby, a comprehensive and pedagogical approach for the generation of guidance tracks for arbitrarily shaped two‐dimensional regions of interest is provided, along with an overview and detailed elaboration on different exact cellular decomposition techniques found in literature. Furthermore, cost functions are outlined for the different approaches presented in this work, which are utilized to generate optimal guidance tracks. Finally, this survey serves as an introductory guide for research and practitioners to solve the CCPP problem effectively and efficiently.

A spanning tree algorithm with adaptive pruning with low redundancy coverage rate

In reality, when UAV is used in region reconnaissance, the shape of region is usually irregular. Selecting an algorithm to find a complete coverage path with smaller coverage redundancy rate, lower path motion complexity and theoretical energy consumption has certain research significance for the complete coverage path planning (CCPP). In existing algorithms, there is always a high redundant coverage rate under irregularly shaped regions. The motion complexity and the theoretical energy consumption of the path also increase. In this paper, a kind of spanning tree with adaptive pruning (STAP) algorithm based on the grid map is proposed to solve the above problems: on the one hand, it uses a greedy growth strategy to determine the branching growth direction, and proposes an adaptive pruning method for self-optimization of the initial path; on the other hand, a kind of nearest neighbor sub-region connection rule of adjacent paths is proposed to reduce the complexity and the coverage redundancy rate. The STAP algorithm searches for all grid centers, grows, and optimizes the spanning tree by creating paths between the centers. At the end of the optimization, it creates a complete coverage path based on the distribution of the spanning tree. Experiments show that when the STAP algorithm is applied to unobstructed regions, it plans the path with the lowest redundant coverage rate, which reaches 2.6296% and 4.9402%; when it is applied to the regions include obstacles, the advantages of the algorithm are more significant: On the one hand, the path planned by STAP has the least total length, the lowest number of turning and turning back. On the other hand, the redundant coverage rate of the path is still the lowest, reaching 4.1362% and 2.7765%, respectively. The STAP is primarily suitable for situations about CCPP under grid maps formed by irregularly shaped regions, and it holds certain significance for ground reconnaissance of UAVs.

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.

Collaborative path planning and task allocation for multiple agricultural machines

Path planning and task allocation are critical concerns in multi-machine collaborative operations for unmanned farms. Nevertheless, several problems remain in the operation of agricultural machinery, such as the slow path planning algorithm, the omission of the working area, and the unreasonable scheduling of machines, resulting in low efficiency and wasted resources. Collaborative and complete coverage path planning was achieved to solve the problems of slow path planning algorithms and the omission of working areas. The farm’s electronic map was constructed using the topological map method. The improved Dijkstra algorithm based on priority queues was combined with three different complete coverage methods: the nested method, the reciprocating method, and the combination of nested and internal spiral path methods. The simulation results show that the improved Dijkstra method based on priority queues can effectively minimize the running time of the algorithm. The reciprocating method has a higher coverage index than the other two methods, with an average coverage rate of 94.73 %. To solve the problem of illogical scheduling of the same type of agricultural machines, an improved ant colony method was presented based on the whole working path to minimize the path cost. The simulation results show that the proposed method can allocate the task properly, and the path cost is reduced by 14 %–33 %. By combining the proposed path planning and task allocation methods, the whole-process path planning of a single agricultural machine and multiple agricultural machines of the same type was achieved, providing a technical solution for promoting the construction of unmanned farms.

An Improved Algorithm for Complete Coverage Path Planning Based on Biologically Inspired Neural Network

Complete coverage path planning (CCPP) requires the mobile robots to traverse every part of the workspace, which is one of the major challenges in cleaning robots and many other robotic systems. The biologically inspired neural network (BINN) algorithm has been extensively applied in path planning, recently. In this paper, a new CCPP strategy with biologically inspired neural network is proposed. The planned path of cleaning robot is not only determined by the dynamic neural activities, but also by the distribution of obstacles in the environmental map. By distinguishing the connectivity between different areas of the environmental map, and using the proposed path backtracking algorithm, the improved CCPP algorithm can autonomously plan a collision-free path and reduce the path repetition ratio. Besides, an improved dynamic deadlock escape algorithm is presented to select the optimal escape target point. The simulation results show that the proposed CCPP algorithm without any templates or learning procedures is able to generate an orderly path in both known and unknown environment.

Field Complete Coverage Path Planning Based on Improved Genetic Algorithm for Transplanting Robot

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

Approaches to complete coverage path planning using neural network and grid division

Since the goal of using complete coverage path planning is to generate a continuous and uninterrupted path that covers an area of interest while avoiding obstacles, its use could be extremely vital in today's field of robotics. Not only could it help our daily lives like lawnmowers, window cleaners, and painter robots, but it could also solve some dangerous or complex but vital problems for human beings; For example, mine detection, vacuum cleaning, and photogrammetry. This paper proposes two paths of successful approaches to Complete Coverage Path Planning: Neural Network and Grid division. After detailed data comparisons, both proved to have been efficient and successful, respectively. In addition, both plans' field applications would be placed at the end.

Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning.

Complete coverage path planning requires that the mobile robot traverse all reachable positions in the environmental map. Aiming at the problems of local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm, a complete coverage path planning algorithm based on Q-learning is proposed. The global environment information is introduced by the reinforcement learning method in the proposed algorithm. In addition, the Q-learning method is used for path planning at the positions where the accessible path points are changed, which optimizes the path planning strategy of the original algorithm near these obstacles. Simulation results show that the algorithm can automatically generate an orderly path in the environmental map, and achieve 100% coverage with a lower path repetition ratio.

Complete coverage path planning for reconfigurable omni-directional mobile robots with varying width using GBNN([formula omitted

Self-reconfigurable robots which can change their footprint’s width have demonstrated the ability to access confined areas. To enhance the efficiency of area coverage in complete coverage path planning (CCPP) for complex and confined environments, it is advantageous to cover areas fast in wide areas while maintaining the ability to navigate through tight spaces and cover hard to access areas. This can be achieved by leveraging the flexibility of an omnidirectional self-reconfigurable robot footprint. The widest footprint is used to speed up the area coverage when there are no obstacles around, while the smallest footprint is used to navigate through tight spaces. However, the generation of robotic width reconfiguration state during autonomous CCPP generation, poses challenges. In this paper, a CCPP for omni-directional robots of varying width with n-reconfiguration states is proposed. To this end, the proposed CCPP is a modified GBNN with n-reconfiguration states (GBNN(n)). It generates the global path autonomously, which determines the robot width as per the nth reconfiguration states so as to increase area coverage in open areas and reduce robot footprint in tight spaces. The proposed complete coverage path planning are compared against state-of-the-art GBNN and CCPP optimization using depth-limited search and successfully demonstrate that the proposed algorithm helps robots of varying widths achieve higher area coverage in lesser steps, energy and distance. The supporting simulation and experimental video link11Link for simulation and experimental video: https://tinyurl.com/2n4n7ry9. is also provided to highlight the outcomes.

Complete coverage path planning for wheeled agricultural robots

AbstractIn the agricultural industry, an evolutionary effort has been made over the last two decades to achieve precise autonomous systems to perform typical in‐field tasks, including harvesting, mowing, and spraying. One of the main objectives of an autonomous system in agriculture is to improve the efficiency while reducing the environmental impact and cost. Due to the nature of these operations, complete coverage path planning (CCPP) approaches play an essential role to find an optimal path which covers the entire field while taking into account land topography, operation requirements, and robot characteristics. The aim of this paper is to propose a CCPP approach defining the optimal movements of mobile robots over an agricultural field. First, a method based on tree exploration is proposed to find all potential solutions satisfying some predefined constraints. Second, a similarity check and selection of optimal solutions method is proposed to eliminate similar solutions and find the best solutions. The optimization goals are to maximize the coverage area and to minimize overlaps, nonworking path length, and overall travel time. To explore a wide range of possible solutions, our approach is able to consider multiple entrances for the robot. For fields with a complex shape, different dividing lines to split them into simple polygons are also considered. Our approach also computes the headland zones and covers them automatically which leads to a high coverage rate of the field.

A metaheuristic approach to optimal morphology in reconfigurable tiling robots

Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies.

An Algorithm of Complete Coverage Path Planning for Unmanned Surface Vehicle Based on Reinforcement Learning

A deep reinforcement learning method to achieve complete coverage path planning for an unmanned surface vehicle (USV) is proposed. This paper firstly models the USV and the workspace required for complete coverage. Then, for the full-coverage path planning task, this paper proposes a preprocessing method for raster maps, which can effectively delete the blank areas that are impossible to cover in the raster map. In this paper, the state matrix corresponding to the preprocessed raster map is used as the input of the deep neural network. The deep Q network (DQN) is used to train the complete coverage path planning strategy of the agent. The improvement of the selection of random actions during training is first proposed. Considering the task of complete coverage path planning, this paper replaces random actions with a set of actions toward the nearest uncovered grid. To solve the problem of the slow convergence speed of the deep reinforcement learning network in full-coverage path planning, this paper proposes an improved method of deep reinforcement learning, which superimposes the final output layer with a dangerous actions matrix to reduce the risk of selection of dangerous actions of USVs during the learning process. Finally, the designed method validates via simulation examples.

Parameter value selection strategy for complete coverage path planning based on the Lü system to perform specific types of missions

Parameter value selection strategy for complete coverage path planning based on the Lü system to perform specific types of missions

Distributed Cooperative Complete Coverage Path Planning in an Unknown Environment Based on a Heuristic Method

This paper proposes a distributed collaborative complete coverage path planning (CCPP) algorithm based on a heuristic method to solve a CCPP problem for multiple agents in an unknown environment. Based on the relationship between path length, energy consumption and number of turns, the algorithm instructs the agents to autonomously plan their respective paths in real time by giving the priority of directions. Simulation experiments show that the proposed CCPP algorithm can guarantee efficient collision-free complete coverage compared with related approaches.

Distributed collaborative complete coverage path planning based on hybrid strategy

Distributed collaborative complete coverage path planning based on hybrid strategy

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot.

The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot.

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

The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C light to automate the disinfection processes. But performing this process in an optimum way introduces some challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the environment. In this work, both challenges are addressed with the design of a complete coverage path planning (CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN), a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed. One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on the performance of the robot navigation.