Recent advances in AI-based mobile robots for human companionship: survey

Human companionship is an essential capability for mobile robots operating in dynamic, human-centered environments. It enables robots to perform tasks such as guidance, assistance, surveillance, and service delivery across various domains, including healthcare, logistics, and public safety. The recent advances in artificial intelligence (AI), particularly in computer vision, deep learning, and sensor fusion, have significantly improved the reliability, adaptability, and contextual understanding of robotic companionship systems. This review presents a comprehensive analysis of recent advances in AI-based approaches to human companionship by mobile robots. It focuses on critical components such as sensing modalities, human detection and re-identification, trajectory prediction, motion planning, control strategies, and human-robot interactions. It presents a structured taxonomy of current methods, highlights commonly used control architectures, and explores the key challenges involved in real-world deployment. Despite significant progress, several issues remain unresolved, including occlusion handling, identity switching, socially compliant navigation, and scalability. The review concludes by identifying prominent research gaps and proposing future directions aimed at advancing the development of robust, socially aware, and context-adaptive robotic companions.