AI-Powered Motion Prediction Models for Mobile Robots

Abstract

Mobile robot motion prediction has become one of the most important research topics, and autonomous systems can now foresee the future behavior of dynamic systems and take decisions about proper navigation. This paper involves a detailed investigation of motion prediction models based on artificial intelligence (AI), optimized to work with mobile robots, and concentrates on models created before 2018. The paper examines classical probability models, machine learning models, and early deep learning models that are employed to predict the trajectories of dynamic agents like pedestrians, vehicles, and other robots. Motion prediction is important as it directly influences collision avoidance, path planning, and human-robot interaction. The classical methods used were very much dependent on the kinematic and dynamic models that could not cope with complicated and uncertain surroundings. As AI methods, especially supervised learning and probabilistic graphical models, were integrated, robots learned to provide patterns based on the data and extrapolate into new situations. The paper will examine the Hidden Markov Models (HMM), the Kalman Filters, Particle Filters, Gaussian Processes, and early neural network architectures, based on their advantages and shortcomings. In addition, the paper also talks about methods of feature representation, such as spatial-temporal encoding, behavioral modeling, and context-aware prediction. The methodology section describes a hybrid architecture that combines probabilistic filtering with regression based on neural networks to get better accuracy. The results of the experiments are summarized in comparative performance tables that are based on percentages to assess the accuracy of predictions, computational efficiency, and strength. The results reveal that even though models provided a solid basis to motion prediction, issues of scalability, real-time processing, and flexibility persist. The paper sums up with the need to consider contextual awareness and modeling of multi-agent interaction to improve prediction.