Discover the structure of AI in autonomous vehicles with our informative guide. This simplified overview outlines the key components that enable self-driving cars to perceive, navigate, and make decisions, ensuring safe and efficient transportation. Perfect for those interested in understanding the technology behind autonomous driving. Stay informed with Softlabs Group for more insightful content on cutting-edge advancements in AI.
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Efficient Cache-Supported Path Planning on Roads (Extended Abstract)
Efficient Cache-Supported Path Planning on Roads (Extended Abstract Abstract:Owing to the wide availability of the global positioning system (GPS) and digital mapping of roads, road network navigation services have become a basic application on many mobile devices. Path planning, a fundamental function of road network navigation services, finds a route between the specified start location and…
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/29625/q-learn%C4%B1ng-based-real-t%C4%B1me-path-plann%C4%B1ng-for-mob%C4%B1le-robots/halil-cetin
pharmacy journal, open access journal of engineering, research publication
Decision making and movement control are used for mobile robots to perform the given tasks. This study presents a real time application in which the robotic system estimates the shortest way from robot's current location to target point via Q learning algorithm and makes decision to go the target point on the estimated path by using movement control. Q Learning algorithm is known as a Reinforcement Learning RL algorithm. In this study, it is used as a core algorithm for estimation of the path that is optimum way for mobile robot in an environment. The environment is viewed by a camera. This study includes three phases. Firstly, the map and the locations of all objects including a mobile robot, obstacles and target point in the environment are determined by using image processing. Secondly, Q Learning algorithm is applied for the problem of the estimation of the shortest way from the current location of the robot to target point. Finally, a mobile robot with three omni wheels was developed. Experiments were carried out using this robot. Two different experiments are performed in experimental environment. The results obtained are shared at the end of the paper.
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23696/path-planning-algorithms-for-unmanned-aerial-vehicles/elaf-jirjees-dhulkefl
international journals in engineering, engineering journal, paper publication for engineering
In this paper, the shortest path for Unmanned Aerial Vehicles UAVs is calculated with two dimensional 2D path planning algorithms in the environment including obstacles and thus the robots could perform their tasks as soon as possible in the environment. The aim of this paper is to avoid obstacles and to find the shortest way to the target point. Th e simulation environment was created to evaluate the arrival time on the path planning algorithms A and Dijkstra algorithms for the UAVs. As a result, real time tests were performed with UAVs
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