🎓 Discover PhD and Master's programmes at leading universities worldwide — Sign up free to save searches and get email alerts
TUO

Multi-arm and Constrained Manipulation in Robotics

The University of Manchester Department of Electronic and Electrical Engineering
✓ Funded (Competition) 🎓 Robotics robot control constrained manipulation multi-arm robots model-based optimisation data-driven control robotic arms dynamic environments dexterous manipulation

Explore how to safely control multiple robotic arms performing dexterous manipulation in constrained and changing environments. Develop and integrate model-based and data-driven methods to enhance robot adaptability and safety in critical applications such as surgery and space operations.

AI-generated overview

🌍
Why This Research Matters

This research addresses critical safety challenges in robotic manipulation within constrained and dynamic environments such as surgical or nuclear settings. Enhancing robotic dexterity and safety reduces the risk of catastrophic failures, protecting human operators and improving operational success. Advancements enable broader adoption of robotics in delicate and high-stakes applications.

Robotics Human-Robot Interaction Surgical Robots

Project Description

Project Overview

This project focuses on the fundamental aspects of constrained manipulation using multiple robotic arms in critical applications such as surgical, space, scientific, and nuclear domains. Dexterous manipulation in constrained spaces with guaranteed safe control under changing environments is essential to avoid catastrophic failures.

Solutions like control-barrier functions exist for safe robot control; however, reactive capabilities are required to handle deformable or biological materials and workspace topology changes due to object or surface removal. The robotic system may be fixed, mobile, or legged but must possess arms for efficient manipulation.

What You Will Do

  • Develop effective model-based constrained optimisation approaches for dexterous manipulation in constrained workspaces using multiple robotic arms.
  • Explore data-driven approaches to represent dynamic environments and integrate this information into model-based control methods.
  • Work primarily at the University of Manchester, with access to advanced robotics facilities including UR30, UR3e, KUKA R820 Med, Kinova arms, and a variety of aerial, mobile, and legged robots.

Expected Outcomes

Creation of novel algorithms and control strategies that enable safe, adaptive, and efficient manipulation in cluttered and dynamic environments. Tools developed may advance robotic deployment in delicate, high-stakes domains, enhancing safety and operational capabilities.

Why This Matters

Robotics in constrained, critical environments demands precision and safety to prevent harm and mission failure. The project's advances improve robotic autonomy and reliability in complex real-world settings, unlocking new potential for robotic applications in surgery, space exploration, nuclear operations, and scientific research.

Entry Requirements

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

How to Apply

Apply online through the University of Manchester website at https://uom.link/pgr-apply-2425. Include full project title, supervisor name, funding status, previous study details, and two referee contacts. Submit final and interim transcripts, CV, supporting statement (mandatory), referee contact details, and English language certificate if applicable. Contact the admissions team at FSE.doctoralacademy.admissions@manchester.ac.uk for inquiries.

Eligibility

UK/Home
EU
International

Supervisor Profile

DM
Dr Murilo Marinho
The University of Manchester, Department of Electronic and Electrical Engineering
877 Citations
17 h-index
Google Scholar

Dr Murilo Marinho works within the Department of Electronic and Electrical Engineering at The University of Manchester, focusing on robot control and constrained manipulation for multi-arm robotic systems. His research addresses safety and adaptability in robotics across critical domains, including surgical and space robotics. He is recognized for integrating control-barrier functions and model-based optimisation techniques with data-driven approaches to tackle real-world robotic challenges.

Key Publications

2019 120 citations
Dynamic Active Constraints for Surgical Robots Using Vector Field Inequalities
Introduced dynamic active constraints for surgical robots using vector field inequalities to improve surgical safety.
2021 79 citations
DQ Robotics: A Library for Robot Modeling and Control
Provided a software library for efficient robot modeling and control facilitating research and development in robotics.
2019 56 citations
A Unified Framework for the Teleoperation of Surgical Robots in Constrained Workspaces
Presented a framework to enable teleoperation of surgical robots in constrained spaces enhancing precision and usability.
2020 55 citations
SmartArm: Integration and Validation of a Versatile Surgical Robotic System for Constrained Workspaces
Demonstrated integration and validation of a versatile surgical robotic system specialized for constrained workspace applications.
2020 52 citations
Virtual Fixture Assistance for Suturing in Robot-Aided Pediatric Endoscopic Surgery
Developed virtual fixtures to assist suturing in robotic pediatric endoscopic surgery, improving precision and outcomes.

Research Contributions

Developed dynamic active constraints using vector field inequalities to enhance safety and control in surgical robotics.
This advancement improves surgical robot performance by preventing unintended movements and enhancing patient safety.
Created DQ Robotics, a library for robot modeling and control using dual quaternion algebra.
This provides researchers and engineers with a powerful tool for efficient and accurate robotic system development.
Established frameworks and systems to enable teleoperation and integration of surgical robots in constrained workspaces.
These contributions facilitate minimally invasive surgical procedures, increasing accessibility and precision in complex surgeries.

Related Opportunities

PhD in Robotics and AI for Construction Safety and Worker Performance
University of Kansas Dr. Amit Ojha 🎓 Artificial Intelligence 🎓 Civil Engineering

Explore robotics and AI to transform construction safety and worker performance. Develop systems that enable intelligent automation and immersive training, addressing critical safety challenges on construction sites.

This research tackles significant safety risks in the construction industry by leveraging AI and robotics to reduce accidents and improve w…

660+ citations · h14
Construction Robotics Construction Automation Human Robot Collaboration VR/AR Technologies
PhD Position in Intelligent Robotic Manufacturing Systems at University of Guelph
University of Guelph Dr. Sheng Yang 🎓 Artificial Intelligence 🎓 Mechanical Engineering

Explore intelligent robotic manufacturing systems integrating autonomous manipulation, AI, and digital twin technology. Collaborate with industry to produce next-gen smart factory platforms. Develop practical robotic ap…

This research aims to revolutionize manufacturing by creating smart, adaptive robotic systems that enhance productivity, flexibility, and s…

Additive Manufacturing Sustainable Manufacturing Digital Twin Industry AI
AI and Robotics for Digital Twins in Civil Infrastructure
University of Alabama 🎓 Artificial Intelligence 🎓 Civil Engineering Deadline: 01 Jun 2026

Explore the frontier of AI and robotics applied to civil infrastructure diagnostics and retrofitting using digital twin technology. Develop autonomous systems and simulation analytics to improve infrastructure resilienc…

This research addresses critical infrastructure challenges posed by urbanization and climate change through innovative AI and robotics solu…

Artificial Intelligence Robotics Digital Twins Civil Infrastructure
Foundation-model-guided world models and predictive control for autonomous remote handling in extreme environments
University of Sheffield Prof Amir Ghalamzan 🎓 Engineering 🎓 Mechatronics

Explore autonomous robotic manipulation using foundation models to improve decision-making under uncertainty. Engage with cutting-edge predictive control and world model methods for hazardous, unstructured environments.

This research advances autonomous robotic capabilities for safe and robust operation in extreme environments like fusion and nuclear plants…

1342+ citations · h22
Robotics Robotic Manipulation data-driven control foundational models