Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Presentation

Robotics in Industry and Intelligent Systems

At the Centre for Robotics and Intelligent Systems, we develop innovative solutions to leverage robotics in the industrial, agricultural, and forestry contexts, driving the digital transformation of the industry.


We take a practical approach - from design to deployment - to test the navigation and localisation of mobile robots, explore advances in 2D/3D industrial vision and advanced detection, while also focusing on industrial and collaborative robotics, as well as human-robot interfaces.


Our TRIBE LAB is fertile ground for innovative ideas about the agriculture of the future; we develop prototypes and promote excellence in agricultural robotics and IoT technology: with prototypes, advanced sensors (LiDAR, AI cameras), and rapid prototyping tools, we accelerate the development of solutions for the agroforestry sector. We are also present at the iiLab, where we combine applied research, technological demonstration, and controlled environment testing, promoting the integration of emerging technologies into industry. From intelligent robotic cells and cyber-physical systems to data analysis and AI, it is an innovation space where companies can experiment with and validate solutions for the factory of the future.


With a multidisciplinary team, and following European agendas, our research work combines fundamental science and application, impacting the design of solutions for Industry 4.0, fostering competitiveness and the digital transformation of the sector.

Interest
Topics
Team
  • a
  • b
  • c
  • d
  • e
  • f
  • g
  • h
  • i
  • j
  • k
  • l
  • m
  • n
  • o
  • p
  • q
  • r
  • s
  • t
  • u
  • v
  • w
  • x
  • y
  • z
Publications

CRIIS Publications

View all Publications

2026

Active learning for industrial defect detection: a study on hybrid sampling strategies

Authors
Garcia Gonzalez, D; Nascimento, R; D. Rocha, C; F. Silva, M; Filipe, V; F. Rocha, L; Gonzaga Magalhães, L; Cunha, A;

Publication
The International Journal of Advanced Manufacturing Technology

Abstract

2026

A review of visual perception for robotic bin-picking

Authors
Cordeiro, A; Rocha, LF; Boaventura-Cunha, J; Figueiredo, D; Souza, JP;

Publication
ROBOTICS AND AUTONOMOUS SYSTEMS

Abstract
Robotic bin-picking is a critical operation in modern industry, which is characterised by the detection, selection, and placement of items from a disordered and cluttered environment, which can be boundary limited or not, e.g. bins, boxes or containers. In this context, perception systems are employed to localise, detect and estimate grasping points. Despite the considerable progress made, from analytical approaches to recent deep learning methods, challenges still remain. This is evidenced by the growing innovation proposing distinct solutions. This paper aims to review perception methodologies developed since 2009, providing detailed descriptions and discussions of their implementation. Additionally, it presents an extensive study, detailing each work, along with a comprehensive overview of the advancements in bin-picking perception.

2026

Economic benchmarking of assisted pollination methods for kiwifruit flowers: Assessment of cost-effectiveness of robotic solution

Authors
Pinheiro, I; Moura, P; Rodrigues, L; Pacheco, AP; Teixeira, JG; Valente, LG; Cunha, M; Neves Dos Santos, FN;

Publication
Agricultural Systems

Abstract
In 2023, global kiwifruit production reached over 4.4 million tonnes, highlighting the crop's significant economic importance. However, achieving high yields depends on adequate pollination. In Actinidia species, pollen is transferred by insects from male to female flowers on separate plants. Natural pollination faces increasing challenges due to the decline in pollinator populations and climate variability, driving the adoption of assisted pollination methods. This study examines the Portuguese kiwifruit sector, one of the world's top 12 producers, using a novel mixed-methods approach that integrates both qualitative and quantitative analyses to assess the feasibility of robotic pollination. The qualitative study identifies the benefits and challenges of current methods and explores how robotic pollination could address these challenges. The quantitative analysis explores the cost-effectiveness and practicality of implementing robotic pollination as a product and service. Findings indicate that most farmers use handheld pollination devices but face pollen wastage and application timing challenges. Economic analysis establishes a break-even point of €685 per hectare for an annual single application, with a first robotic pollination of €17 146 becoming cost-effective for orchards of at least 3.5 hectares and a second robotic solution of €34 293 becoming cost-effective for orchards up to 7 hectares. A robotic pollination service priced at €685 per hectare per application presents a low-risk and a viable alternative for growers. This study provides robust economic insights supporting the adoption of robotic pollination technologies. This study is crucial to make informed decisions to enhance kiwifruit production's productivity and sustainability through precise robotic-assisted pollination. © 2025 Elsevier B.V., All rights reserved.

2026

AI Enabled Robotic Loco-Manipulation

Authors
Li, Q; Xie, M; Tokhi, MO; Silva, MF;

Publication
Lecture Notes in Networks and Systems

Abstract

2026

Crisis or Redemption with AI and Robotics? The Dawn of a New Era

Authors
Silva, MF; Tokhi, MO; Ferreira, MIA; Malheiro, B; Guedes, P; Ferreira, P; Costa, MT;

Publication
Lecture Notes in Networks and Systems

Abstract