Gated Recurrent Unit Based Short-Term Network for Robot Swarm Motion Forecasting

Belkacem Khaldi, F. Harrou, Dairi Abdelkader, Ying Sun
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Abstract

This work introduces a short term forecasting gated recurrent unites framework for swarm motion speed forecasting. This is motivated by the growing need of addressing such forecasting challenges in order to keep swarm robotic systems executing daily collective operations and accomplishing tasks more successfully in groups. The framework is based on the BiGRU model and its performances is compared to its base GRU model. The framework is built upon sensor measurements collected using a simulated swarm of e-puck robots performing a simple pattern formation task in a free/no-free obstacles scenarios. Findings show how accurate BiGRU is in forecasting swarm motion speed while compared to GRU.
基于门控循环单元的机器人群体运动短期预测网络
本文介绍了一种用于群体运动速度预测的短期预测门控循环单元框架。这是由于越来越需要解决这样的预测挑战,以保持群体机器人系统执行日常集体操作和更成功地完成任务。该框架基于BiGRU模型,并将其性能与基本GRU模型进行了比较。该框架建立在传感器测量数据的基础上,这些测量数据是通过模拟一群电子冰球机器人在自由/无自由障碍的情况下执行简单的模式形成任务而收集的。研究结果表明,与GRU相比,BiGRU在预测群体运动速度方面是多么准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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