Coverage Path Planning Research Articles

Cooperative survey of seabed ROIs using multiple USVs with coverage path planning

A fine-grained offshore seabed map is critical to the site selection and construction for offshore wind farms, which can be obtained by the cooperative survey with multiple unmanned surface vehicles (USVs). To accomplish the mission of surveying multiple regions of interest (ROIs) by cooperative coverage path planning of USVs, a sequential algorithm is proposed to facilitate the generating the ROIs’ boundary, decomposing concave polygons, and planning coverage path for multiple USVs. First, in order to extract the multiple ROIs and generate accurate polygon boundaries, the grid clustering method and the α -shapes method are adopted with a prior seabed distribution map information. Second, an improved Bayazit decomposition algorithm is presented to reduce the number of sub-regions and turnings by USVs during the coverage planning. Third, aiming at routing the traveling order of every sub-region, a novel discrete group teaching optimization algorithm with an adaptive neighborhood radius model is proposed, which performs robustly compared with five classical multiple traveling salesman algorithms. Finally, numerical simulation results show that the proposed algorithm integrating the ROIs determination and coverage path planning methods is effective for cooperative surveying of multiple USVs.

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.

The Construction of Robot Experiment Teaching Course for the Cultivation of Outstanding Talents in Intelligent Manufacturing

With the continuous progress of science and technology, intelligent manufacturing has become one of the main directions of industrial development, and training outstanding talents for it has become a historical mission that relevant universities must complete. As an important basis for realizing intelligent manufacturing, robot learning and application naturally become a key link in the cultivation of outstanding talents in intelligent manufacturing. On the basis of theoretical learning, the construction of robot experimental teaching courses can truly cultivate outstanding talents with practical application ability. This paper takes the construction of "Autonomous Navigation and Obstacle Avoiding Mobile Robot System Design Experiment" as an example to analyze how to build a student centered, interest oriented, practice based, and education oriented robot experiment teaching course that combines reality with fiction. This experimental course is based on scientific research projects, built around the student center, and oriented to teaching needs. It includes robot autonomous positioning and navigation, autonomous path planning, full coverage path planning, etc. Its experimental content, teaching methods, and concept design can be applied to the construction of similar experimental courses, with great promotion and application value.

A Divide and Conquer Strategy for Sweeping Coverage Path Planning

One of the main challenges faced by floor treatment service robots is to compute optimal paths that completely cover a set of target areas. Short paths are of noticeable importance because their length is directly proportional to the energy used by the robot. Such a problem is known to be NP-hard; therefore, efficient algorithms are needed. In particular, computation efficiency is important for mobile robots with limited onboard computation capability. The general problem is known as coverage path planning (CPP). The CPP has several variants for single regions and for disjoint regions. In this research, we are investigating the solutions for disjoint target regions (rooms) that have fixed connection points (doors). In particular, we have found effective simplifications for the cases of rooms with one and two doors, while the challenging case of an unbounded number of rooms can be solved by approximation. As a result, this work presents a divide and conquer strategy (DnCS) to address the problem of finding a path for a sweeping robot that needs to sweep a set of disjoint rooms that are connected by fixed doors and corridors. The strategy divides the problem into computing the sweeping paths for the target rooms and then merges those paths into one solution by optimising the room visiting order. In this strategy, a geometrical approach for room coverage and an undirected rural postman problem optimisation are strategically combined to solve the coverage of the entire area of interest. The strategy has been tested in several synthetic maps and a real scenario showing short computation times and complete coverage.

Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms.

Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm’s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here—parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.

Coverage Path Planning for Robotic Quality Inspection With Control on Measurement Uncertainty

The optical scanning gauges mounted on the robots are commonly used in quality inspection, such as verifying the dimensional specification of sheet structures. Coverage path planning (CPP) significantly influences the accuracy and efficiency of robotic quality inspection. Traditional CPP strategies focus on minimizing the number of viewpoints or traveling distance of robots under the condition of full coverage inspection. The measurement uncertainty when collecting the scanning data is less considered in the free-form surface inspection. To address this problem, a novel CPP method with the optimal viewpoint sampling strategy is proposed to incorporate the measurement uncertainty of key measurement points (MPs) into free-form surface inspection. At first, the feasible ranges of measurement uncertainty are calculated based on the tolerance specifications of the MPs. The initial feasible viewpoint set is generated considering the measurement uncertainty and the visibility of MPs. Then, the inspection cost function is built to evaluate the number of selected viewpoints and the average measurement uncertainty in the field of views of all the selected viewpoints. Afterward, an enhanced rapidly exploring random tree algorithm is proposed for viewpoint sampling using the inspection cost function and CPP optimization. Case studies, including simulation tests and inspection experiments, have been conducted to evaluate the effectiveness of the proposed method. Results show that the scanning precision of key MPs is significantly improved compared with the benchmark method.

C C I B A*: An Improved BA* Based Collaborative Coverage Path Planning Method for Multiple Unmanned Surface Mapping Vehicles

The main emphasis of this work is placed on the problem of collaborative coverage path planning for unmanned surface mapping vehicles (USMVs). As a result, the collaborative coverage improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$BA^{*}$ </tex-math></inline-formula> algorithm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CCIBA^{*}$ </tex-math></inline-formula> ) is proposed. In the algorithm, coverage path planning for a single vehicle is achieved by task decomposition and level map updating. Then a multiple USMV collaborative behavior strategy is designed, which is composed of area division, recall and transfer, area exchange and recognizing obstacles. Moverover, multiple USMV collaborative coverage path planning can be achieved. Consequently, a high-efficiency and high-quality coverage path for USMVs can be implemented. Water area simulation results indicate that our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CCIBA^{*}$ </tex-math></inline-formula> brings about a substantial increase in the performances of path length, number of turning, number of units and coverage rate.

Autonomous Tactile Localization and Mapping of Objects Buried in Granular Materials

Robots are expected to operate autonomously in unstructured, real-world environments, for tasks such as locating buried objects in search and rescue applications. When robots operate within opaque granular materials, tactile and proprioceptive feedback can be more informative than visual feedback. However, since tactile measurements are local and sparse, it can be difficult to efficiently build a global, tactile-based model of a search area. In this work, we developed a framework for tactile perception, mapping, and haptic exploration for the autonomous localization of objects buried in granular materials. Haptic exploration was performed within a densely packed sand mixture using a sensor model that accounts for granular material characteristics and aids in the interpretation of interaction forces between the robot and its environment. The haptic exploration strategy was designed to efficiently locate a buried object and refine its outline while simultaneously minimizing potentially damaging physical interactions with the buried object. Coverage path planning techniques were used to select haptic exploration movements from candidates that aimed to reduce map uncertainty. A continuous occupancy map was generated that fused local, sparse tactile information into a global Bayesian Hilbert Map. We demonstrated our framework in simulation and with a real robot with granular materials.

Learning to Coordinate for a Worker-Station Multi-Robot System in Planar Coverage Tasks

For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this letter, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources. We introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker-station</i> MRS consisting of multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workers</i> with limited resources for actual work, and one <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">station</i> with enough resources for resource replenishment. We aim to solve the mCPP problem for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker-station</i> MRS by formulating it as a fully cooperative multi-agent reinforcement learning problem. Then we propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workers</i> and rendezvous planning for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">station</i> . Our method manages to reduce the influence of random dynamic interferers on planning, while the robots can avoid collisions with them. We conduct simulation and real robot experiments, and the comparison results show that our method has competitive performance in solving the mCPP problem for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker-station</i> MRS in metric of task finish time.

Quadrotor Model for Energy Consumption Analysis

In this paper, a quadrotor dynamic model’s energy efficiency was investigated. A method for the design of the dynamic model which assures energy consumption estimation was presented. This model was developed to analyze the energy efficiency of the quadrotor during each maneuver. A medium-class quadrotor (4.689 kg) was used as a test platform. Thrust force correction factors obtained with FLIGHTLAB software were used to predict object behavior in forward flight. Model validation and long-duration flight tests in outdoor windy conditions are also presented. Monte-Carlo simulation was used to study the influence of uncertainties in model parameters on the simulation reliability. The developed model might be used for practical purposes (for example, energy-efficient coverage path planning).

Optimal selective floor cleaning using deep learning algorithms and reconfigurable robot hTetro

Floor cleaning robots are widely used in public places like food courts, hospitals, and malls to perform frequent cleaning tasks. However, frequent cleaning tasks adversely impact the robot’s performance and utilize more cleaning accessories (such as brush, scrubber, and mopping pad). This work proposes a novel selective area cleaning/spot cleaning framework for indoor floor cleaning robots using RGB-D vision sensor-based Closed Circuit Television (CCTV) network, deep learning algorithms, and an optimal complete waypoints path planning method. In this scheme, the robot will clean only dirty areas instead of the whole region. The selective area cleaning/spot cleaning region is identified based on the combination of two strategies: tracing the human traffic patterns and detecting stains and trash on the floor. Here, a deep Simple Online and Real-time Tracking (SORT) human tracking algorithm was used to trace the high human traffic region and Single Shot Detector (SSD) MobileNet object detection framework for detecting the dirty region. Further, optimal shortest waypoint coverage path planning using evolutionary-based optimization was incorporated to traverse the robot efficiently to the designated selective area cleaning/spot cleaning regions. The experimental results show that the SSD MobileNet algorithm scored 90% accuracy for stain and trash detection on the floor. Further, compared to conventional methods, the evolutionary-based optimization path planning scheme reduces 15% percent of navigation time and 10% percent of energy consumption.

CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams

This paper proposes CONCERTS: Coverage competency-based target search, a failure-resilient path-planning algorithm for heterogeneous robot teams performing target searches for static targets in indoor and outdoor environments. This work aims to improve search completion time for realistic scenarios such as search and rescue or surveillance, while maintaining the computational speed required to perform online re-planning in scenarios when teammates fail. To provide high-quality candidate paths to an information-theoretic utility function, we split the sample generation process into two steps, namely Heterogeneous Clustering (H-Clustering) and multiple Traveling Salesman Problems (TSP). The H-Clustering step generates plans that maximize the coverage potential of each team member, while the TSP step creates optimal sample paths. In situations without prior target information, we compare our method against a state-of-the-art algorithm for multi-robot Coverage Path Planning and show a 9% advantage in total mission time. Additionally, we perform experiments to demonstrate that our algorithm can take advantage of prior target information when it is available. The proposed method provides resilience in the event of single or multiple teammate failure by recomputing global team plans online. Finally, we present simulations and deploy real hardware for search to show that the generated plans are sufficient for executing realistic missions.

ECO-CPP: Energy constrained online coverage path planning

Some robotic platforms are expected to perform coverage path planning (CPP) on a daily basis. However, at times they may be expected to perform CPP in large unknown spaces, which are not fully coverable with the onboard limited energy supply. In these cases the platforms need to revisit the charging station multiple times, increasing mission times and the total energy needed, reducing the availability of the robots. This paper proposes a novel solution to this energy constrained online coverage path planning problem, showing effectiveness of this solution in various settings. The proposed solution is based on contour following and outperforms existing work in the literature. Additionally, this work presents a new lower bound for the minimum number of charges and energy consumed for both, offline and online, energy constrained coverage path planning problems.

Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles

In some specific conditions, UAVs are required to obtain comprehensive information of an area or to operate in the area in an all-round way. In this case, the coverage path planning (CPP) is required. This paper proposes a solution to solve the problem of short endurance time in the coverage path planning (CPP) problem of multi-solar unmanned aerial vehicles (UAVs). Firstly, the energy flow efficiency based on the energy model is proposed to evaluate the energy utilization efficiency during the operation. Moreover, for the areas with and without obstacles, the coverage path optimization model is proposed based on the undirected graph search method. The constraint equation is defined to restrict the UAV from accessing the undirected graph according to certain rules. A mixed integer linear programming (MILP) model is proposed to determine the flight path of each UAV with the objective of minimizing operation time. Through the simulation experiment, compared with the Boustrophedon Cellular Decomposition method for coverage path planning, it is seen that the completion time is greatly improved. In addition, considering the impact of the attitude angle of the solar powered UAV when turning, the operation time and the total energy flow efficiency are defined as the optimization objective. The bi-objective model equation is established to solve the problem of the CPP. A large number of simulation experiments show that the optimization model in this paper selects different optimization objectives and applies to different shapes of areas to be covered, which has wide applicability and strong feasibility.

Multi-UAV Collaboration to Survey Tibetan Antelopes in Hoh Xil

Reducing the total mission time is essential in wildlife surveys owing to the dynamic movement of animals throughout their migrating environment and potentially extreme changes in weather. This paper proposed a multi-UAV path planning method for counting various flora and fauna populations, which can fully use the UAVs’ limited flight time to cover large areas. Unlike the current complete coverage path planning methods, based on sweep and polygon, our work encoded the path planning problem as the satisfiability modulo theory using a one-hot encoding scheme. Each instance generated a set of feasible paths at each iteration and recovered the set of shortest paths after sufficient time. We also flexibly optimized the paths based on the number of UAVs, endurance and camera parameters. We implemented the planning algorithm with four UAVs to conduct multiple photographic aerial wildlife surveys in areas around Zonag Lake, the birthplace of Tibetan antelope. Over 6 square kilometers was surveyed in about 2 h. In contrast, previous human-piloted single-drone surveys of the same area required over 4 days to complete. A generic few-shot detector that can perform effective counting without training on the target object is utilized in this paper, which can achieve an accuracy of over 97%.

Multirobot Coverage Path Planning Based on Deep Q-Network in Unknown Environment

Aiming at the problems of security, high repetition rate, and many restrictions of multirobot coverage path planning (MCPP) in an unknown environment, Deep Q-Network (DQN) is selected as a part of the method in this paper after considering its powerful approximation ability to the optimal action value function. Then, a deduction method and some environments handling methods are proposed to improve the performance of the decision-making stage. The deduction method assumes the movement direction of each robot and counts the reward value obtained by the robots in this way and then determines the actual movement directions combined with DQN. For these reasons, the whole algorithm is divided into two parts: offline training and online decision-making. Online decision-making relies on the sliding-view method and probability statistics to deal with the nonstandard size and unknown environments and the deduction method to improve the efficiency of coverage. Simulation results show that the performance of the proposed online method is close to that of the offline algorithm which needs long time optimization, and the proposed method is more stable as well. Some performance defects of current MCPP methods in an unknown environment are ameliorated in this study.

A coverage path planning approach for autonomous radiation mapping with a mobile robot

In nuclear and radiation-related industries, it is crucial to ensure that the radiation dose exposure to the radiation worker is maintained below the permissible dose limit. A radiation map is a useful tool for visualizing the radiation distribution across the work area and for coordinating activities involving the hotspots (high radiation areas). The goal of this work was to design and implement a coverage path planning approach for autonomous radiation mapping carried out by a mobile robot. Given a 2D occupancy map, a method to generate uniformly distributed sampling points was proposed. The geometry of the region of interest, the radiation detector module, and the radiation measurement parameters were considered in formulating the sampling positions. Next, the coverage path planning planner integrates the nearest neighbor and depth-first search algorithms to create a continuous path that enables the robot to visit all the sampling points. The K-means clustering algorithm is added for systematic coverage of a large number of sampling points. The clustering provides options to partition the region of interest into smaller spaces, where the robot would perform the mapping cluster by cluster. Finally, the method of building the radiation map from the acquired data was also presented. The approach was implemented in ROS using a commercial mobile robot equipped with a Geiger–Muller detector. The performance and reliability of the proposed approach were evaluated with a series of simulations and real-world experiments. The results showed that the robot is able to perform autonomous radiation mapping at various target areas. The accuracy of the generated radiation map and the hotspots classifications were also compared and evaluated with conventional manual measurements. Overall, the theoretical frameworks and experiments have provided convincing results in the automation of hazardous work and subsequently toward improving the occupational safety of radiation workers.

An Improved Complete Coverage Path Planning Method for Intelligent Agricultural Machinery Based on Backtracking Method

The advancement of society and technology has promoted the development of modern agriculture. It has become a trend to replace traditional manpower with intelligent agricultural machinery that operates independently. As the core technology of intelligent agricultural machinery, complete coverage path planning technology has become more important. At present, the complete coverage path planning algorithms still suffer from problems such as sacrificing the coverage rate to obtain the minimum energy consumption, taking a long time to calculate the algorithm, and destroying crops across the covered region. In view of the above problems, an improved complete coverage path planning algorithms based on backtracking is proposed combined with the actual needs of intelligent agricultural machinery for planting seedlings to improve four aspects: repeated coverage, search efficiency, path planning, and sub-regional crossing. Firstly, the Morse decomposition method is used to divide a complex farmland region into simple sub-regions. Then an improved backtracking method based on a greedy algorithm is proposed in order to reduce the computational efficiency of the current region connection algorithms. The priority principle and the strategy of moving along the boundary are used to solve the problems of region crossing and sacrificing the coverage rate, thereby improving the performance of the current complete coverage path planning method. Compared with the traditional backtracking method, the experimental results show that the number of backtracking points is decreased by about 70% and the occurrence of crossing sub-regions has been significantly reduced. This proposed method can improve the coverage and operating efficiency of intelligent agricultural machinery operations and provide technical support for agricultural operations such as sowing, tillage, and harvesting, thus improving the quality and efficiency of agricultural production.

Multi-UAV Coverage Path Planning for Gas Distribution Map Applications

Aerial coverage for Gas Distribution Map (GDM) using Unmanned Aerial Vehicles (UAVs) with olfaction capabilities has become a key for effectiveness and cost improvements. In this work, the main contribution is the application of Coverage Path Planning (CPP) approaches adapted to survey gas plumes using an autonomous fleet of UAVs. Indeed, the mission is carried out in four steps. First, a gas plume model was integrated to simulate the environment. Then, based on the sensor’s footprint model and the Voronoï diagram, an exact cellular decomposition method is proposed to generate and distribute measurement points or Points Of Interests (POIs) effectively. To take measurements from all these POIs, two planning approaches were proposed in the third step. One is based on the separation of missions between UAVs using the K-means algorithm principle and modeling each path as a Traveling Salesman Problem (TSP). While the second approach models the global mission for all UAVs as a Vehicle Routing Problem (VRP). The resolution of the two mathematical models is achieved using Genetic Algorithms (GAs) and Simulated Annealing (SA) algorithms, respectively. Eventually, the GDM is reconstituted based on triangulation-based cubic interpolation. The evaluation of the proposed simulation techniques based on comparative analyses shows their efficiency, speed and flexibility. The proposed methods take into account the UAV’s flight autonomy and the desired coverage quality, and they provide solutions for a variety of coverage applications, particularly for GDM.