Accurate Time-to-Target Forecasting for Autonomous Mobile Robots

The use of Autonomous Mobile Robots in various industries is on the rise. Accurate estimation of payload delivery times poses a significant challenge for efficient planning across different sectors. This paper addresses this challenge by evaluating the effectiveness of four time forecasting methods: Linear Regression, Random Forest Regression, Transformer and Bidirectional Long Short-Term Memory networks. These models are compared to a previous approach that uses Long Short-Term Memory neural networks. Among the evaluated models, Bidirectional Long Short-Term Memory and Transformers were the only methods able to outperform the Long Short-Term Memory implementation, achieving a Mean Average Error of 1.26 seconds and 1.27 seconds, respectively. In particular, the newly proposed Bidirectional Long Short-Term Memory model could train twice as fast as the Long Short-Term Memory network, despite the increased complexity generated by the addition of the backward layer. To our knowledge, this is the first time BiLSTM and Transformers are evaluated with the aim to identify the best approach for forecasting the time left to reach a destination in a dynamic industrial environment, where the presence of numerous potential obstacles adds to the complexity of the task.