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Implementation of A* Shortest Path Finding Algorithm in a Transport Robot with Robust Turning Mechanism
Abstract
Autonomous transportation is a rapidly emerging field of technology with potential applications ranging from safe delivery of goods in disaster sites or hospitals to intelligent warehouse logistics management. However, an overwhelming majority of this emerging process focuses on high-end quality products with prices which deny entrance into the field for most. Herein, we demonstrate the development of a proof-of-concept autonomous line-following robot that could serve as a low-cost budget solution for this domain. Rather than using performance-demanding AI approaches, the utilization of the A* path finding algorithm in combination with a line-following movement approach enables the use of the low-cost single-board computer, raspberry pi. Infrared sensors are used in complement with a grid of black lines to enable the robot's movement. Additionally, this work dabbles in the computational performance of the A * algorithm alongside the development of a highly robust turning mechanism.
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Finding the shortest path in direction effective is essential. To solve this shortest path problem, we usually using Dijkstra or A* algorithm. These two algorithms are often used in routing or road networks. This paper’s objective is to compare those two algorithms in solving this shortest path problem. In this research, Dijkstra and A* almost have the same performance when using it to solve town or regional scale maps, but A* is better when using it to solve a large scale map.
Warehouse robots have been widely used by manufacturers and online retailer to automate good delivery process. One of the fundamental components when designing a warehouse robot is path finding algorithm. In the past, many path finding algorithms had been proposed to identify the optimal path and improve the efficiency in different conditions. For example, A* path finding algorithm is developed to obtain the shortest path, while D* obtains a complete coverage path from source to destination. Although these algorithms improved the efficiency in path finding, dynamic obstacle that may exist in warehouse environment was not considered. This paper presents AD* algorithm, a path finding algorithm that works in dynamic environment for warehouse robot. AD* algorithm is able to detect not only static obstacle but also dynamic obstacles while operating in warehouse environment. In dynamic obstacle path prediction, image of the warehouse environment is processed to identify and track obstacles in the path. The image is pre-processed using perspective transformation, dilation and erosion. Once obstacle has been identified using background subtraction, the server will track and predict future path of the dynamic object to avoid the obstacle.
This paper describes the line following robot using arduino for surveying , inspecting and enhancing the transportation of necessary materials inside the healthcare institutions, industries also. The proposed system spot the black path and proceed in its direction on to the ground . This system eases the work of material conveyance as well as minimizes the manpower. This technology targets on the secured, punctual and constructing transportation of goods. This paper aims to implement controlled movement of robot by tuning control parameters and thus achieve better performance. This robot is predominantly design to proceed in a predefined path. To locate this path two sensors are used. Robots like this are mainly used in industrial plants comprising of pick and place facility. This robot carries components from desired source to destination by following fixed path. Recently lot of research has been done to empower the automation in hospitals as well in industries. This robot is made to supply the essential goods such injections, medicine, etc. This paper is divided into hardware and software modules
(1) (PDF) Line Following Robot Using Arduino for Hospitals. Available from: https://www.researchgate.net/publication/338940296_Line_Following_Robot_Using_Arduino_for_Hospitals [accessed Apr 12 2022].
Mobile robots represent a very attractive and multidisciplinary field with a lot of applications and scientific research possibilities. The development of microcomputers, single board computers and embedded systems has helped to deploy low-cost solutions for this domain. In this paper is proposed such a low cost mobile robot platform with fixed four wheel chassis, commended by Raspberry Pi and Arduino Uno interfaces. The mobile robot has the ability to move into 2D environments as line follower robot with mapping, navigation, and obstacle avoidance features. The platform contains also one degree of freedom (DOF) robotic arm for lifting and transport of the obstacles.
We demonstrate the fully autonomous collaboration of an aerial and a ground robot in a mock-up disaster scenario. Within this collaboration, we make use of the individual capabilities and strengths of both robots. The aerial robot first maps an area of interest, then it computes the fastest mission for the ground robot to reach a spotted victim and deliver a first-aid kit. Such a mission includes driving and removing obstacles in the way while being constantly monitored and commanded by the aerial robot. Our mission-planning algorithm distinguishes between movable and fixed obstacles and considers both the time for driving and removing obstacles. The entire mission is executed without any human interaction once the aerial robot is launched and requires a minimal amount of communication between the robots. We describe both the hardware and software of our system and detail our mission-planning algorithm. We present exhaustive results of both simulation and real experiments. Our system was successfully demonstrated more than 20 times at a trade fair.
Based on many large data set, we calibrate a near-optimal heuristic as a constant function for guiding efficiently A or A* algorithm. We claim that this magic number exists as a universal constant for several kinds of data sets. This is perhaps related to the nature of their data sets though it is not theoretically analyzed. In general A* and Dijkstra algorithms always pick up the optimal (e.g., the shortest) path between two nodes on a given graph. However, because they do not use any good heuristics, they spend much time to calculate. To overcome this drawback, we propose A of A* algorithm with a smart heuristics. The algorithm quickly investigates an optimal or near-optimal path. Unfortunately, the heuristics does not always maintain the admissibility, and thus our algorithm does not sometimes pick up the optimal path. Finally, we ascertain superiority of the proposed algorithm to the classic A* and Dijkstra algorithms by two kinds of extremely different data sets.
We consider a graph with n vertices, all pairs of which are connected by an edge; each edge is of given positive length. The following two basic problems are solved. Problem 1: construct the tree of minimal total length between the n vertices. (A tree is a graph with one and only one path between any two vertices.) Problem 2: find the path of minimal total length between two given vertices.
Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.
Research Challenges and Opportunities in Multi-Agent Path Finding and Multi-Agent Pickup and Delivery Problems
- salzman
O. Salzman and R. Stern, "Research Challenges and Opportunities in
Multi-Agent Path Finding and Multi-Agent Pickup and Delivery
Problems," in Proceedings of the 19th International Conference on
Autonomous Agents and MultiAgent Systems, 2020, pp. 1711-1715.