Intelligent monitoring-based safety system of massage robot

• Published in: Journal of Central South University Vol. 23; no. 10; pp. 2647 - 2658
• Main Authors: Hu, Ning, Li, Chang-sheng, Wang, Li-feng, Hu, Lei, Xu, Xiao-jun, Zou, Yun-peng, Hu, Yue, Shen, Chen
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.10.2016; Springer Nature B.V
• Subjects: Control Science and Information Engineering; Decision making; Engineering; Environmental monitoring; Hazards; Life cycle engineering; Mechanical Engineering; Metallic Materials; Robots; Safety; Safety management; Safety standards; robot; intelligent safety; safety strategies for robots; fault tree
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-016-3326-3

As an important attribute of robots, safety is involved in each link of the full life cycle of robots, including the design, manufacturing, operation and maintenance. The present study on robot safety is a systematic project. Traditionally, robot safety is defined as follows: robots should not collide with humans, or robots should not harm humans when they collide. Based on this definition of robot safety, researchers have proposed ex ante and ex post safety standards and safety strategies and used the risk index and risk level as the evaluation indexes for safety methods. A massage robot realizes its massage therapy function through applying a rhythmic force on the massage object. Therefore, the traditional definition of safety, safety strategies, and safety realization methods cannot satisfy the function and safety requirements of massage robots. Based on the descriptions of the environment of massage robots and the tasks of massage robots, the present study analyzes the safety requirements of massage robots; analyzes the potential safety dangers of massage robots using the fault tree tool; proposes an error monitoring-based intelligent safety system for massage robots through monitoring and evaluating potential safety danger states, as well as decision making based on potential safety danger states; and verifies the feasibility of the intelligent safety system through an experiment.