Path planning is an important step to design autonomous flight system of UAV. This paper proposes method for designing path planning algorithm that can make UAV move autonomously. The algorithm used potential field method and A* Algorithm. A* algorithm will be created in a 3D environment, and developed to become hierarchical A* 3D algorithm, and receding horizon A*3D algorithm. These proposed algorithms will be tested in GUI simulation. The potential field and A* algorithm are combined, which the potential field is used for setting up environment, while A* algorithm is used to search the optimal path. Each proposed algorithm has advantages and weaknesses, in terms of time and distance travelled. In time viewpoint, the most optimal method is hierarchical A*3D algorithm, as it requires a short processing time but requires larger distance than the others, whereas the most optimal distance is the normal method of A* 3D, but it takes processing time longest, so that the most appropriate method to be implemented on the UAV is receding horizon A*3D algorithm, both in terms of processing time and distance, still visible to be implemented.

UAV path planning using potential field and modified receding horizon A* 3D algorithm

Hand gesture recognition using valley circle feature and Hu’s moments technique for robot movement control

Since the strength of a trapped person often declines with time in urgent and dangerous circumstances, adopting a robot to rescue as many survivors as possible in limited time is of considerable significance. However, as one key issue in robot navigation, how to plan an optimal rescue path of a robot has not yet been fully solved. This paper studies robot path planning for multisurvivor rescue in limited survival time using a representative heuristic, particle swarm optimization (PSO). First, the robot path planning problem including multiple survivors is formulated as a discrete optimization one with high constraint, where the number of rescued persons is taken as the unique objective function, and the strength of a trapped person is used to constrain the feasibility of a path. Then, a new integer PSO algorithm is presented to solve the mathematical model, and several new operations, such as the update of a particle, the insertion and inversion operators, and the rapidly local search method, are incorporated into the proposed algorithm to improve its effectiveness. Finally, the simulation results demonstrate the capacity of our method in generating optimal paths with high quality.

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PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

This article presents a novel method for measuring the unevenness of yarn apparent diameter based on yarn sequence images captured from a moving yarn. A dynamic threshold module was designed to gain the global threshold for segmenting yarns in the sequence images. In the module, a K-means clustering algorithm was employed to classify the pixels of each frame in the sequence into two clusters—yarn and background. The cluster center of the current frame was used as the initial value of the cluster center for the next frame in the sequence to expedite the segmentation process. From the segmented yarn image, the yarn core was further extracted utilizing the characteristics of yarn hairiness, and two judgment templates were adopted to remove burrs, isolated points and unrelated small areas in the images. The yarn apparent diameter was measured on the yarn core at a given interval. The same kind of yarns were tested by using this method and Uster Evenness Tester 5. The experimental results show that the proposed method can accurately detect the unevenness of yarn apparent diameter and provide new useful information about yarn unevenness, such as the short-term, the long-term, and the periodic variations of yarn apparent diameters.

http://iopscience.iop.org/article/10.1088/0957-0233/27/1/015404/pdf

Measuring the unevenness of yarn apparent diameter from yarn sequence images

This paper contains presentation of the flexible control system for an autonomous UAV (unmanned air vehicle). The complete description of hardware and software solutions used to realize autonomous flight are presented in this work. Main objective of the research was to develop software which provides ease of adjustment and extendibility to drone system with different equipment. Presented system is utilizable on various hardware platforms and is capable of realizing different missions with minimal adjustments. Described concept significantly simplifies designing complex system by introducing modular architecture. Presented method of dividing software components into modules with single functionality minimizes amount of work necessary to adjust system in case of changes in hardware. Presented general concept of system architecture is backed up with real life working model designed for tracking and landing on moving target. This paper contains detailed description of algorithms used in the project. Landing area detection is accomplished with vision system. Canny's edge detection algorithm with contour shape analysis algorithm is used for marker detection. Lucas-Kanade optical flow algorithm is applied for tracking detected pattern. Mission planning is realized as dedicated state machine developed for this particular task. System design is built with use of ROS (Robot Operating System) and is utilizing its subscriber-publisher method of data exchange between separated software units. Especially designed frame is used as hardware platform. Exemplary system is realized with Raspberry Pi 3 as onboard computer and Pixhawk flight controller. This concept and the exemplary system is a result of preparation for Mohamed Bin Zayed International Robotic Challenge 2017 in Abu Dhabi. Results from experiments performed as trials for the competition and future prospects are presented in this paper.

Autonomous drone control system for object tracking: Flexible system design with implementation example

Development of a low cost yarn parameterisation unit by image processing

In this present work, we present an algorithm for path planning to a target for mobile robot in unknown environment. The proposed algorithm allows a mobile robot to navigate through static obstacles and finding the path in order to reach the target without collision. This algorithm provides the robot the possibility to move from the initial position to the final position (target). The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy and distance. The proposed path planning must make the robot able to achieve these tasks: to avoid obstacles and to make ones way toward its target. The algorithms are implemented in Matlab, afterwards tested with Matlab GUI; whereby the environment is studied in a two dimensional coordinate system. The simulation part is an approach to the real expected result; this part is done using Matlab to recognize all objects within the environment and since it is suitable for graphic problems. Taking the segmented environment issued from Matlab development, the algorithm permit the robot to move from the initial position to the desired position following an estimated trajectory using Maps in Matlab GUI.

Path planning of autonomous mobile robot: A new approach

Mobile robots are integrated into a search and rescue team as tools for searching victims in dangerous areas that is harmful for human, so as to follow the human entity during the mission. Generalized Local Voronoi Diagram (GLVD) algorithm is introduced to model the area, coverage and cooperation. The senor based motion planning is implemented on the robots for navigation of the area and coverage. The focus here is on the rescue of entities present following area coverage. A brief description of the on-going research and the results obtained is also provided.

Autonomous robot coverage in rescue operation

Yarn hairiness or amounts of released fibers from the staple yarn are one of the main aspects to detect the yarn quality. This paper illustrates the method of finding yarn hairiness using image processing techniques with a low-cost USB Web Camera in association with a yarn moving arrangement. The steps involved with the image processing comprises of segmenting the yarn core from the image and fibre extraction. The yarn hairiness found by the proposed method are quantised by different indices, such as yarn hair area index and yarn hair length index and count of hair tips at specific distances from the central line of the yarn core. The future goal of this work is to acquire the image of the yarn from a spinning frame with the help of a high resolution web camera and process the same with advanced software to characterize the yarn which in turn dictates the fabric appearance in terms of hairiness.

Yarn hairiness evaluation using image processing

This paper illustrates the development of a system to measure the variation in yarn parameters using image processing with the help of a low cost USB web camera along with a yarn moving arrangement. The complete system comprises of yarn guides and tension control device, motor with gear box, monitor for displaying yarn parameters etc. The system enables to quantify yarn diameter variation, thick/thin places of the yarn in a single unit. The performance of the developed system is free from any fluctuation of ambient temperature or humidity. Different types of yarn (cotton, jute) with wide range of diameter variation are used to verify the system linearity and ascertain its resolution. The available results are verified with that available from a powerful microscope. The measurement of diameter are performed with cut length from 1 mm to 4 mm. Results are available on the monitor showing instantaneous diameter over average diameter calculated for a specific time, counters displaying the number of thick/thin places. The future goal of this work is to acquire the image of the yarn from a spinning frame with the help of a high resolution web camera and to process the same with advanced software to characterise the yarn which in turn dictate the fabric appearance.

Subhasish Roy

Papers

Development of a low cost yarn parameterisation unit by image processing

Path planning of autonomous mobile robot: A new approach

Autonomous robot coverage in rescue operation

Yarn hairiness evaluation using image processing

Yarn parameterization based on image processing