Explore the decision-making process in autonomous vehicle navigation with our informative flowchart. Follow steps including perception, localization, mapping, path planning, and control. Simplify the complex process of ensuring safe and efficient navigation for autonomous vehicles. Perfect for engineers, researchers, and enthusiasts in autonomous driving technology. Stay informed with Softlabs Group for more insights into the future of transportation!
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The final project for this class required our robot to navigate a randomly generated course to 5 predetermined destinations on the field as fast as possible, avoiding obstacles. Along the way, the robot identified colored “weeds,” which it would hover over for two seconds to “exterminate.” Then, it counted the weeds found, recording the number of weeds by color and number of weeds total. The robot stored the weeds’ locations. This information and the planned path was plotted against the actual path via LabView with obstacles, as the robot navigated the course.
The final project for this class required our robot to navigate a randomly generated course to 5 predetermined destinations on the field as fast as possible, avoiding obstacles. Along the way, the robot identified colored “weeds,” which it would hover over for two seconds to “exterminate.” Then, it counted the weeds found, recording the number of weeds by color and number of weeds total. The robot stored the weeds’ locations. This information and the planned path was plotted against the actual path via LabView with obstacles, as the robot navigated the course.
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21642/path-navigation-in-aco-using-mobile-robot/p-hema-suganthi
open access journal of engineering, ugc approved journals for engineering, call for paper engineering
Ant colony algorithm suffers drawbacks such as slow convergence and easy to trap into local optimum, therefore the path planning for mobile robot based on an improved ant colony optimization algorithm is proposed. The workspace for mobile robot is established with grid method. A hybrid ant colony which is composed of common ants and exploratory ants is utilized to avoid trapping into local optimum. To increase the convergence speed, the pheromone update mechanism is improved by enhancing the sensitivity of the ants to the optimal path with reserving the elite ants. The optimal collision free path can be planned rapidly in the workspace with multiple obstacles. Simulation and experiment results show that the algorithm is practical and effective.