Hybrid MIRL and ACO-based Approach for Real-Time Path Planning in Visual SLAM
Abstract
For autonomous robotic systems, real-time establishing paths in dynamic situations is still a major difficulty. Current Visual SLAM-integrated planners, including A\ and Dijkstra, frequently perform poorly in uncertain situations due to a lack of flexibility and collaborative intelligence. In order to improve navigation, this study presents MIRL-ACO-SLAM, a hybrid framework that combines Ant Colony Optimization (ACO) and Multi-Intelligence Reinforcement Learning (MIRL). Real-time spatial maps are created by the network using Visual SLAM (ORB-SLAM3), which allows agents to make decisions locally through reinforcement learning. Pheromone-based optimization guarantees global convergence to the best solutions. Pheromone-guided pruning and selective agent activation improve scalability on bigger maps while lowering computing costs. In contrast with traditional SLAM-based planners, MIRL-ACO-SLAM delivers 18.6% shorter route length, 24.3% quicker scheduling speed, with a 94% route completion rate in dynamic situations, according to empirical assessments conducted utilizing the Zurich MAV dataset. The suggested system opens the door for implementation in mission-critical applications like transportation and search-and-rescue by offering a dependable, scalable, and adaptable answer for real-time robotic navigation.References
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https://doi.org/10.31449/inf.v50i11.9866Downloads
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