A Novel Incipient Slip Degree Evaluation Method and Its Application in Adaptive Control of Grasping Force

Grasping is the most basic and important function of robots. However, the grasping performance of existing manipulators is far inferior to that of human hands. Human hands are able to evaluate the degree of incipient slip on contact surface based on tactile information during grasping process and thus regulate grasping force. As a result, humans can use the smallest possible grasping force while ensuring successful grasping, which maximizes the performance of their hands and avoids damaging the grasped object as much as possible. The existing incipient slip degree evaluation methods have certain shortcomings. So this paper proposes a novel method which can be applied to complex contact conditions where the incipient slip degree is not unidirectional and torque exists. Also, there are no restrictions on the material parameters and surface topography of the grasped object, and no need to obtain any information about it in advance. We construct a grasping force control strategy for parallel grippers based on this evaluation method, with the goal of enabling the gripper to achieve the similar grasping performance of human hands. The grasping strategy is verified in simulations and actual experiments, and the difference between the controlled force and minimum grasping force is demonstrated. Note to Practitioners—In unstructured grasping tasks, the information of the grasped object cannot be obtained in advance, which makes it difficult for robotic grasping. Conventional grasping strategies often use the maximum force of grippers, but this can cause damage to fragile objects. In contrast, human hands can flexibly control the size of the grasping force, which will not far exceed the minimum force required to ensure successful grasping. This capability greatly expands the grasping range of human hands. Neuroscience studies have shown that such excellent grasping performance owes to the perception of incipient slip by human’s tactile system. However, existing incipient slip detection methods for manipulators have certain drawbacks. To fill this gap, we propose a novel method that can be applied to contact conditions where tangential forces are reversed and torques exist. Meanwhile the method has no restrictions on the surface morphology and material parameters of the contact object, which has a good range of applications. This paper applies this method to a parallel gripper commonly used in industrial scenarios, and proposes a grasping force control strategy that can automatically adjust the magnitude of the grasping force to achieve a similar grasping performance to human hands - keeping the grasping force within a certain range beyond the minimum value. In the future, we also intend to apply the proposed method to more dexterous manipulators and perform more complex manipulation tasks. Possible application scenarios include: smart prosthesis, virtual reality, remote control, etc. Currently, we assume that the Coulomb’s law of friction is satisfied on the contact surface, and more complex friction models will be introduced in the future to further improve the applicability of this method.

I. Introduction

Grasping, the most basic and important function of robots, is widely used in structured industrial scenarios such as handling and loading in the form of pre-programming. However, with the continuous development of technology, robots are gradually entering people’s daily life. In such unstructured environments, the parameters of the grasped objects are not available in advance, thus posing a challenge for robot grasping. An excellent grasping control strategy should enable the robot to grasp objects with arbitrary characteristics within the range allowed by its own power, which makes the strategy of using maximum force to grasp objects no longer applicable, as it leads to the destruction of some fragile objects. In this sense, the current robotic grasping performance is far inferior to that of human hands, which can flexibly adjust grasping force according to tactile feedback on the finger, ensuring that the applied force does not far exceed the minimum grasping force required [1]. Such capabilities greatly expand the object grasping range of human hands. Studies have shown that fingertip slip detection plays an important role in grasping force control [2]. The human hand can sense the friction information on contact surface based on the slip state of the fingertips to control the grasping force [3], [4]. Therefore, it is important to add slip detection capability to the manipulator to improve its grasping performance.