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Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles
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AI
DOI：10.26599/TST.2021.9010073.png)
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Abstract
En 中文
With the continuous development of robotics and artificial intelligence, robots are being increasingly used in various applications. For traditional navigation algorithms, such as Dijkstra and A*, many dynamic scenarios in life are difficult to cope with. To solve the navigation problem of complex dynamic scenes, we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present. The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output. The algorithm enhances the robots' ability to actively avoid obstacles while retaining the adaptability of traditional methods.
Keywords:
Simultaneous localization and mapping
Navigation
Heuristic algorithms
Operating systems
Robot control
Clustering algorithms
Reinforcement learning
simultaneous localization and mapping (SLAM)
reinforcement learning
multirobots
dynamic obstacles
robot operating system (ROS)
navigation
Journal
T
IF:
3.5
Papers:
987
Citations:
2.5K
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Cited Papers
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