Active Engineering Computing & AI

Development of Intelligent System for Grasping a Soft Object using Mobile Robots

Funder EPSRC (part of UKRI)
Recipient organisation University of Birmingham
Funding Not disclosed
Period Jan 2025 — Jul 2028

In plain English

AI plain-English summary

A mobile robot learns to pick up a soft tomato without crushing it. Factories and warehouses increasingly rely on robots to handle objects, but most struggle with delicate items—fruit, glassware, electronic components—that can be damaged by standard grippers. Current systems lack the precision to adapt grip strength and angle to each object’s shape and fragility. This project combines mobile robots with computer vision and machine learning so that a robot can recognise an object, plan its approach, and adjust its grip in real time. If successful, the system could automate tasks now done by hand in food processing, pharmaceuticals, and electronics assembly. Mobile robots that navigate factory floors and gently grasp variable objects would reduce waste, speed up production, and allow human workers to focus on higher-level tasks. The work also advances fundamental knowledge in how vision and touch data can be fused to control robotic manipulation—a challenge that underpins everything from surgical robots to automated recycling.

View original technical description
Industry 4.0 is driving significant transformation across a wide range of industrial domains. The integration of automated technologies, particularly mobile robotics and Artificial Intelligence (AI), is enhancing traditional manual procedures by improving accuracy and ensuring greater repeatability. To meet the growing demand for complex manipulation capabilities, the development of automated systems in combination with advanced grippers is essential, particularly for handling delicate objects in diverse working environments. This project aims to develop innovative methodologies for an automated solution that integrates advanced robotics with computer vision and machine learning algorithms. The deployment of mobile robots further strengthens operational efficiency, requiring advanced expertise in path planning and control. This project will focus on the development of advanced programming techniques to optimise robotic navigation and approach strategies, followed by the integration of vision-based recognition systems and, ultimately, the implementation of grippers for efficient manipulation of delicate objects.

View the original record at the funder ↗

Researchers

Reuben Blakeway (Student)

Related Research

Grants with similar aims, by meaning.

Grasping Grocery Items with Sensorised Robotic Manipulators and Reinforcement Learning
Developing an Intelligent Soft Robotic Manipulator for High Efficient and Automated Cross - Breeding and Phenotypic Analysis in Agriculture
Advanced AI and RobotIcS for autonomous task pErformance
A Vision-driven Robotic Dynamic Grasping System for Anomaly Objects in Dynamic Production Scenarios
Perception-guided robust and reproducible robotic grasping and manipulation

Original classification

Studentship

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.