With the introduction of autonomy into the precision agriculture process, environmental explorati... more With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles to completely cover entire unknown environments. In the previous complete coverage path planning (CCPP) research, however, autonomous vehicles need to consider mapping, obstacle avoidance, and route planning simultaneously during operating in the workspace, which results in an extremely complicated and computationally expensive navigation system. In this study, a new framework is developed in light of a hierarchical manner with the obtained environmental information and gradually solving navigation problems layer by layer, consisting of environmental mapping, path generation, CCPP, and dynamic obstacle avoidance. The first layer based on satellite images utilizes a deep learning method to generate the CCPP trajectory through the position of the autonomous vehicle. In the second layer, an...
Variable Speed Robot Navigation by an ACO Approach
Lecture Notes in Computer Science, 2019
A variable-speed-based navigation and map building method of an autonomous mobile robot is develo... more A variable-speed-based navigation and map building method of an autonomous mobile robot is developed in this paper in cooperation with an ant colony optimization algorithm (ACO). In real-world applications, an autonomous mobile robot is expected to operate at variable speed. It should slow down in vicinity of obstacles, whereas moving at high speed in open areas. A LIDAR-based local navigator algorithm integrated with a variable speed module is implemented for local navigation and obstacle avoidance. A variable speed navigation paradigm is developed in integration with the ACO algorithm to dynamically adapt its speed to the environment scenarios. In addition to the variable speed ACO based navigation, grid-based map representations are imposed for real-time autonomous robot navigation. Simulation and comparison studies demonstrate effectiveness of the proposed real-time variable-speed-based ACO approach of an autonomous mobile robot.
Handbook of Research on Fireworks Algorithms and Swarm Intelligence, 2020
In recent years, computer technology and artificial intelligence have developed rapidly, and rese... more In recent years, computer technology and artificial intelligence have developed rapidly, and research in the field of mobile robots has continued to deepen with development of artificial intelligence. Path planning is an essential content of mobile robot navigation of computing a collision-free path between a starting point and a goal. It is necessary for mobile robots to move and maneuver in different kinds of environment with objects and obstacles. The main goal of path planning is to find the optimal path between the starting point and the target position in the minimal possible time. A new firework algorithm (FWA) integrated with a graph theory, Dijkstra's algorithm developed for autonomous robot navigation, is proposed in this chapter. The firework algorithm is improved by a local search procedure that a LIDAR-based local navigator algorithm is implemented for local navigation and obstacle avoidance. The grid map is utilized for real-time intelligent robot mapping and navig...
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