Safety constitutes the fundamental bottleneck in human–robot integration, particularly for service robots operating in unstructured environments. This survey systematically reviews the research progress in the field of robot safety control, focusing on injury prevention and resolution mechanisms in physical human–robot interaction scenarios. Firstly, by deconstructing the safety boundaries of robot behavior, the concept of robot safety domain was established, and safety control objectives for three distinct operational states are briefly summarized. For each hierarchical objective, the article deeply analyzes corresponding key control strategies and their efficacy. This survey not only summarizes the existing methods, but also highlights the challenges in the future, providing guidance for subsequent research in service robot safety and laying a foundation for advancing trustworthy human–robot coexistence amid escalating autonomy demands.

This paper focuses on the reachable set estimation for Markovian jump neural networks with time delay. By allowing uncertainty in the transition probabilities, a framework unifies and enhances the generality and realism of these systems. To fully exploit the unified uncertain transition probabilities, an equivalent transformation technique is introduced as an alternative to traditional estimation methods, effectively utilizing the information of transition probabilities. Furthermore, a vector Wirtinger-based summation inequality is proposed, which captures more system information compared to existing ones. Building upon these components, a novel condition that guarantees a reachable set estimation is presented for Markovian jump neural networks with unified uncertain transition probabilities. A numerical example is illustrated to demonstrate the superiority of the approaches.