With the growing use of mobile robots in urban settings, their interaction with people in shared spaces is increasing. It’s crucial for these robots to navigate in a socially acceptable manner and interact properly with their environment. AI guides robot behavior by interpreting the environment and controlling actions. Poor explainability of robot decisions can lead to mental and physical risks. Proper robot-to-human interfaces can help mitigate these risks. This paper introduces systems and methods for self-explaining mobile robots in common areas using projected information. We analyze data on people’s reactions to the robot, with and without selfexplainable features, across various university campus settings like maker-spaces, hallways, cafeterias, and study areas. Initial analysis shows improved acceptance of the robot based on task performance and user questionnaires.

Abstract--- Intelligent autonomous robotic systems are not limited to industrial applications and are being used for professional service and household domains. The vast amount of these robots are designed to work alongside humans in close proximity which pushes the need for safety-guaranteed control systems. Assuring the safety of humans and the environment while maintaining the performance of the robot is still a challenging problem. We proposed a safe iterative feedback linearization control method for the safe operation of collaborative robots. This proposed approach combines feedback linearization with intelligent predictive algorithms to achieve stable and safe control of the robot's motion. By using real-time feedback and monitoring user interactions, I demonstrate how to improve performance over time while maintaining safety. Our proposed method shows how to safely improve performance in collaborative robotics applications. Experimental results show our designed control system is a promising approach for achieving safe and efficient control of collaborative robots.