Study on adaptive fuzzy force control based on food rheology properties

Abstract

With the wide application of service robots in daily life assistance scenarios, the force sensing feedback mechanism that imitates human fine operation through multi-dimensional force perception technology has become an important technology to improve robot grasping performance. However, the existing meal-assisting robots, when gripping meals with complex geometries, nonlinear viscoelastic mechanical properties, and variable friction coefficients, often suffer from a single dimension of force sensing of gripping mechanisms and insufficient robustness of the force tracking control strategy. These limitations result in excessive gripping force or insufficient contact force in the dynamic grasping process, leading to fracture, breakage, or falling off of foods, and ultimately seriously affecting the success rate and the fetching rate. Aiming at the above problems, this paper proposes a gripping mechanism module for the compact integration of multi-dimensional force sensing in meal-assisting robots and an adaptive fuzzy force tracking control strategy based on mechanical properties of foods. Firstly, by analyzing the mounting coupling relationship between the compact gripping force sensor and the three-dimensional wrist force sensor, the mechanical models between the sensors and the end effector of the gripping mechanism are established. Secondly, for the underdrive gripping mechanism of the meal-assisting robotics, the kinematic model is established by the closed-loop vector method and the dynamic model of the gripping mechanism is constructed by combining the Lagrange equation, which provide the theoretical basis for the subsequent control method. Finally, based on the nonlinear mechanical properties of meals and the mechanical model of the gripping mechanism, fuzzy control and proportional integration (PI) are combined to propose the force tracking control strategy based on the combination of fuzzy proportional integration (F-PI) algorithm and gripping motion of solid meals. To verify the performance of the proposed method, the food gripping motion experiments and food fetching-delivering experiments were carried out for bread, sausage, fried chicken nuggets, fried meatballs, rice, scrambled eggs with fungus, stewed pork with potatoes, and broccoli, which have complex physical properties, respectively. The experimental results verify the effectiveness and robustness of the proposed method. This work can provide technical reference value for intelligent robots grasping target objects with complex physical properties and can provide theoretical reference value for the development of automated grasping robots in fields of food engineering.