Modeling of Behavior and Interaction Analysis of Autonomous Robots in Smart Logistics Environment: A Case Study on iLoabot-M

In recent years using autonomous robots for the purposes of loading and unloading objects is highly ubiquitous, especially in the fields of smart-manufacturing and logistics industry. The main purpose of these robots is to increase the efficiency of product management and thereby increase the productivity in logistics sector. However, with the rapid development of big data, artificial intelligence, machine learning, computer vision and internet of things (IoT), one of the main challenges is to evaluate how these robots interact in unpredictable and uncertain environments. In this paper, the experiments conducted on the iLoabot-M (developed by SENAD Inc, Shanghai, China) autonomous and loading and unloading robot is described, and how the user interaction is minimized by understanding the behavior analysis over a period of time are evaluated. Initially, system’s prototyping model with the required components such as sensors, actuators, cameras, and other communication and navigation systems is considered for behavior analysis, and then the interaction capabilities are evaluated by assigning specific tasks. The perception system, decision making system, and execution system form the core system components, and results show that they are directly influence the performance in terms of behavior generation, interaction, and task allocation. The behavior analysis is conducted based on different patterns of movements, task-allocation and number of software, hardware, and communication components involved. With this way, users could able to analyze the behavior of the prototype by evaluating the system incrementally within a short time-frame.