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首页> 外文会议>Youth Academic Annual Conference of Chinese Association of Automation >Robotic Path Planning Based on Episodic Memory Fusion
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Robotic Path Planning Based on Episodic Memory Fusion

机译：基于情节内存融合的机器人路径规划

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Episodic memory provides a mechanism for recalling past experience, which can be used for path planning in complex environments. This paper describes a path planning method based on memory fusion that combines an episodic memory models with the potential path detection network. In traditional path planning methods based on the episodic memory model, paths were planned based on the trajectory that the mobile robot has experienced in the environment, ignoring the surrounding potential paths. Therefore, the planned path is not necessarily globally optimal. In response to this problem, we proposed a path detection network to find potential safe paths in the environment. Our experimental results demonstrated that a better path can be found by fusing the potential path into the original episodic-cognitive map from the perspective of planned path length and number of turns.
机译：插值记忆提供了一种回顾过去经验的机制，可用于复杂环境中的路径规划。 本文介绍了一种基于存储器融合的路径规划方法，该方法将具有潜在路径检测网络的情节存储器模型组合。 在基于焦磁记忆模型的传统路径规划方法中，基于移动机器人在环境中经历的轨迹，忽略周围潜在路径的轨迹进行计划。 因此，计划路径不一定全局最佳。 为了响应这个问题，我们提出了一种路径检测网络，用于在环境中找到潜在的安全路径。 我们的实验结果表明，通过将潜在的路径从计划路径长度和匝数熔断到原始的epiSodic-Cognive地图中，可以找到更好的路径。

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