Ingeniarius, Ltd., R. Nossa Sra. Conceição 146, 4445-147 Alfena, Portugal.
Engineering Department, School of Sciences and Technology, University of Trás-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal.
Sensors (Basel). 2023 Mar 23;23(7):3388. doi: 10.3390/s23073388.
This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency.
本研究提出了一种新方法，以应对人类在动态和非结构化环境（如农业、林业和建筑）中与机器人协作（HRC）时的行为不确定性。这些具有挑战性的任务通常需要大量的时间、劳动力，并且对人类来说很危险，通过与机器人合作，可以有很大的改进空间。然而，由于人类行为的模糊性所带来的不确定性，将人类纳入闭环会带来一些开放性的挑战。这种不确定性使得基于低层次的感官输入数据来表示高层次的人类行为变得困难。所提出的模糊状态长短期记忆（FS-LSTM）方法通过模糊化模糊的感官数据，并使用状态机和 LSTM 深度学习方法开发了一种组合的活动识别和序列建模系统，解决了这一挑战。评估过程将传统的 LSTM 方法与原始感官数据输入、使用模糊输入的模糊 LSTM 方法和提出的 FS-LSTM 方法进行了比较。结果表明，与传统的 LSTM 相比，使用模糊输入可以显著提高准确性，而模糊状态机方法提供的结果与模糊输入相似，但它还具有确保活动之间的可行转换和提高计算效率的额外优势。
