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.

Concentrated Coverage Path Planning Algorithm of UAV Formation for Aerial Photography

Portable unmanned surface vehicle that automatically measures flow velocity and direction in rivers

Coverage path planning (CPP) of multiple Dubins robots has been extensively applied in aerial monitoring, marine exploration, and search and rescue. Existing multi-robot coverage path planning (MCPP) research use exact or heuristic algorithms to address coverage applications. However, several exact algorithms always provide precise area division rather than coverage paths, and heuristic methods face the challenge of balancing accuracy and complexity. This paper focuses on the Dubins MCPP problem of known environments. Firstly, we present an exact Dubins multi-robot coverage path planning (EDM) algorithm based on mixed linear integer programming (MILP). The EDM algorithm searches the entire solution space to obtain the shortest Dubins coverage path. Secondly, a heuristic approximate credit-based Dubins multi-robot coverage path planning (CDM) algorithm is presented, which utilizes the credit model to balance tasks among robots and a tree partition strategy to reduce complexity. Comparison experiments with other exact and approximate algorithms demonstrate that EDM provides the least coverage time in small scenes, and CDM produces a shorter coverage time and less computation time in large scenes. Feasibility experiments demonstrate the applicability of EDM and CDM to a high-fidelity fixed-wing unmanned aerial vehicle (UAV) model.

/pdf/exact-and-heuristic-multi-robot-dubins-coverage-path-3gv04xwe.pdf

Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environments

In this paper, we focus on path planning of drone teams deployed for search and rescue missions. The goal of the mission is to detect a target, inform the rescue personnel at the ground base station (BS), and form a communication relay chain between the target and the BS as fast as possible. Such missions where both detection and connectivity requirements need to be met can be planned by formulating (i) a single objective optimization problem with connectivity constraints; (ii) a multi-objective optimization problem where mission and connectivity needs are jointly optimized or (iii) mission and connectivity tasks are optimized decoupled from each other. Both joint and decoupled approaches have merit in terms of mission times, connectivity, cost and/or implementation. In this paper, we compare selected joint and decoupled multi-drone path planning approaches from mission and connectivity perspectives. We illustrate the trade-off between performance metrics from both viewpoints and show that depending on the available resources (e.g., number of drones) and the search area most suitable planner can change. We then propose a hybrid planner that utilizes joint optimization for the search drones and decoupled optimization for the relay drones. Hence, the proposed scheme has a flexibility due to allowing different search path planners to be used and a connectivity-wise better pre-mission plan. Our analysis shows that the hybrid scheme results in a better connectivity and total mission time if there are enough drones, but for very small number of search drones, hybrid scheme leads to a higher mission time than the joint scheme due to reservation of drones for relaying purposes. 

Joint or decoupled optimization: Multi-UAV path planning for search and rescue

A review of recent trend in motion planning of industrial robots

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.

/pdf/a-comprehensive-review-of-coverage-path-planning-in-robotics-6u94w2far3.pdf

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

This paper presents a navigation system for an Autonomous Surface Vessel (ASV) capable of maneuvering in riverine environments. A real time vision-based navigation system is used for ASV to travel along the river. The ASV is equipped with propeller and rudder to control the speed and heading direction, respectively. Continuous stream of images are captured by camera that mounted on ASV and sent to the base station over network in real time. An image segmentation algorithm based on Hough Transform technique is implemented for waterline detection. The purpose of using Hough Transform is to perform visual based navigation that track along with the river. Besides, a high accuracy GPS receiver is installed on the ASV to provide the latitude and longitude coordinates. Data logger system helps to save the data and important information for further analysis and processing. The optical flow algorithm is implemented to detect and avoid obstacle. The ASV's navigation, control and task specific vision have been evaluated through experiments with results presented to demonstrate its capabilities.

River navigation system using Autonomous Surface Vessel

Most of the underwater applications such as underwater acoustic communication require usage of high cost hardware devices. This constraint limits the research activities within this field. In due to cost limitation, there is a need to develop a low cost underwater communication device but still able to provide good performance to achieve the objectives. In this paper, underwater acoustic distance measurement system based on fast Fourier transform (FFT) overlap using low cost hardware platform is presented. Time-of-arrival (TOA) estimation is derived based on the intensity of the received signal with frequency of interest after computing forward FFT. The extraction of the signal from the receiver is performed by using digital signal processing algorithms. Strength of the received signal and its distance from the receiver are measured during the experiments. The experimental results are presented to demonstrate its capabilities.

Underwater acoustic distance measurement using fast fourier transform overlap

This paper presents an underwater acoustic distance measurement system based on Fast Fourier Transform (FFT) overlap. The low-cost underwater communication device is developed to achieve the objective. The time-of-arrival (TOA) technique is used to estimate the distance between the transmitter and receiver. The TOA estimation is derived based on the intensity of the received signal with frequency of interest. The extraction of the signal from the receiver is performed by using digital signal processing algorithms. FFT is used to convert the receiving signal from time domain into frequency domain for extracting the information of signal (relevant frequency and amplitude). Hence, TOA estimation can be obtained from the frequency information using overlap transform processing. This enables the increase of the TOA measurement resolution with the overlap technique. Trilateration algorithm is also employed as to determine the relative position of underwater target based on distance measurement. This approach is tested on shallow underwater to estimate the position of target. The experimental results are presented to demonstrate its capabilities.

http://nopr.niscair.res.in/bitstream/123456789/48859/3/IJMS%2048%287%29%201070-1080.pdf

Time of arrival estimation using fast Fourier transform overlap for underwater distance measurement

The most challenging problem in inspection planning is the structural coverage in an environment with obstacles. This paper presents a coverage path planning framework based on reinforcement learning using an autonomous underwater vehicle (AUV). This approach exploits the knowledge from the model and generates an optimal path to move from the initial position to the nearest area of interest (AOI). Then, it starts to perform a sweep of the exterior boundary of a three-dimensional (3D) structure in the workspace, including concerning the complete coverage of the given AOI and avoiding obstacles. In this model, a non-linear action selection strategy is used to provide a meaningful exploration, contributing to more stability in the learning process. A reward function is designed by taking into consideration multiple objectives to satisfy the sub-goal requirements. The simulation result indicates the effectiveness of the approach in planning the inspection path. The AUV behaves as a boustrophedon motion when covering the AOI and can achieve maximum cumulative reward while reaching the learning goal.

Chee Sheng Tan

Papers

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

River navigation system using Autonomous Surface Vessel

Underwater acoustic distance measurement using fast fourier transform overlap

Time of arrival estimation using fast Fourier transform overlap for underwater distance measurement

Reinforcement Learning based Underwater Structural Pole Inspection