Efficient Force and Stiffness Prediction in Robotic Produce Handling with a Piezoresistive Pressure Sensor

2025-10-15

AI Summary

In agricultural harvesting, improper grasping forces often damage delicate produce. Method: This paper proposes a real-time force–stiffness co-sensing and closed-loop control framework based on low-cost flexible piezoresistive sensors. By modeling the contact transient response and designing an accelerated steady-state estimation algorithm, it enables millisecond-scale dynamic identification of both contact force and object stiffness. The sensor is compatible with both rigid and pneumatic soft grippers, supporting adaptive grasping of agricultural products with unknown geometry, size, and hardness. Results: Experiments demonstrate joint force–stiffness estimation within 200 ms, stable force-controlled grasping, and downstream applications including ripeness assessment and quality grading. This work establishes a new paradigm for tactile perception and manipulation in agricultural robotics—characterized by high robustness, low cost, and scalability.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Properly handling delicate produce with robotic manipulators is a major part of the future role of automation in agricultural harvesting and processing. Grasping with the correct amount of force is crucial in not only ensuring proper grip on the object, but also to avoid damaging or bruising the product. In this work, a flexible pressure sensor that is both low cost and easy to fabricate is integrated with robotic grippers for working with produce of varying shapes, sizes, and stiffnesses. The sensor is successfully integrated with both a rigid robotic gripper, as well as a pneumatically actuated soft finger. Furthermore, an algorithm is proposed for accelerated estimation of the steady-state value of the sensor output based on the transient response data, to enable real-time applications. The sensor is shown to be effective in incorporating feedback to correctly grasp objects of unknown sizes and stiffnesses. At the same time, the sensor provides estimates for these values which can be utilized for identification of qualities such as ripeness levels and bruising. It is also shown to be able to provide force feedback for objects of variable stiffnesses. This enables future use not only for produce identification, but also for tasks such as quality control and selective distribution based on ripeness levels.

Problem

• Developing low-cost pressure sensors for robotic grasping of delicate agricultural produce
• Predicting object stiffness and size to prevent damage during robotic handling
• Enabling real-time force feedback for quality assessment of variable-stiffness objects

Innovation

• Flexible low-cost pressure sensor integrated with grippers
• Algorithm for real-time force estimation from transient data
• Sensor provides stiffness feedback for produce quality identification