The TeamRob Synergy addresses the following question: “How to realize teams of intelligent robots that are capable of working for and with humans?” This core question is addressed by four sub-projects: Instructing robots, Skill transfer among robots, Mixed human-robot teams, and Intention understanding and communication. An overarching Synergy project is dedicated to addressing scientific challenges across sub-projects, and combining the results of sub-projects to realize teams of intelligent robots that are capable of working for and with humans. The research is anchored in the future needs of the industry, of which four prominent actors are co-funding partners in this project.
Funders: The Swedish Knowledge Foundation (KKS), Örebro University
Partners: Scania, Volvo Construction Equipment, Epiroc, and Volvo Group Truck Operations
Intelligent Management of Mixed Traffic in Mines
This project aims to help those who are involved in the process of deploying and managing fleets of transport vehicles in underground mines. The project will develop tools for designing and realizing mixed traffic management solutions, including methods for optimizing vehicle routing and coordination that account for the larger context of mine infrastructure and operation. The expected impact of the project is to enable the seamless integration of autonomous, human-driven and tele-operated transport vehicles in mines.
Funders: SUM Academy (LKAB)
Partners: LKAB, Epiroc
The Semantic Robots research profile employs a dozen post-docs and senior researchers at AASS, and involves tight collaborations with major Swedish industrial partners. Its objective is to create sustainable world-class research at AASS in the area of Robotics and AI. Research activities are grounded on industrial case-studies elicited from major Swedish companies.
Funders: The Swedish Knowledge Foundation (KKS), Örebro University
Partners: Epiroc, Volvo Construction Equipment, Volvo Trucks, Husqvarna, Kollmorgen Automation, Saab, CNet, Fotonic
The aim of this project is to develop AI-based software tools for automated online Load-Haul-Dump planning in underground mines. The objective is to render mining operations more flexible and effective. This is achieved via a combination of automated planning, optimization and multi-robot coordination algorithms.
Funder: Vinnova, Swedish Mining Innovation program
Partners: Örebro Unievrsity, Epiroc, Newcrest Mining Pty Ltd
The project seeks to generalize the multi-robot planning and control developed in the lab to robot arms and, eventually, mobile manipulators. The project is centered on a use-case in underground mining, where the manipulators to be coordinated are multi-jointed drilling booms. A research platform consisting of three Franka Emika Pandas was built as a development platform.
Funder: Vinnova, Swedish Mining Innovation program
Partners: Örebro Unievrsity, Epiroc, ZinkGruvan Mining, Alfred Nobel Science Park
The iQMobility project deals with developing fleets of autonomous buses. The project is led by Scania and also involves KTH and the company INIT. Our role in the project is to develop motion planning and coordination algorithms for use in automated bus depots, with the specific challenge of integrating motion planning and coordination.
Funder: Vinnova, Strategic vehicle research and innovation programme (FFI)
Partners: Scania, Örebro University, KTH, INIT
ILIAD aims to develop highly flexible, rock-solid reliable, self-optimizing, quickly deployable, safe and efficient warehouse automation solutions. Our lab leads the workpackage dealing with automated fleet management, and collaborates with partners to develop integrated coordination, motion planning and control methods for fleets of autonomous forklifts.
Funder: EU H2020
Partners: Örebro University, University of Lincoln, University of Pisa, Technical University of Munich, Bosch, Kollmorgen Automation, ACT Operations Research, Orkla Foods Sverige, Logistic Engineering Services
In January 2019, the AI4EU consortium was established to build the first European Artificial Intelligence On-Demand Platform and Ecosystem with the support of the European Commission under the H2020 programme. The project supports research and road-mapping activities in five key areas arising from the application of AI in real-world scenarios, namely, Explainable AI, Physical AI ,Verifiable AI, Collaborative AI, and Integrative AI. The Multi-Robot Planning and Control lab is involved mainly in the area of Integrative AI. The lab has also contributed the Meta-CSP framework and the coordination_oru library to the AI4EU platform.
Funder: EU H2020
Partners: please see project fact sheet on Cordis.
AI planning and scheduling methods are fundamental in industrial transport applications, automating key stages in the overall process of assigning tasks and ensuring that plans remain feasible over time. These methods typically rely on manually-specified knowledge to derive plans, many aspects of which are only known to human planning experts (e.g., impenetrable forest roads, icy terrain, etc). This project aims to enhanced AI planning and scheduling methods with the ability to learn from human planning experts and from experience. The primary class of use-cases for this project is that of autonomous transport domains.
Funder: Swedish Foundation for Strategic Research (SSF)
Partners: Örebro University, Scania
The AIPlan4EU project aims to bring AI planning into the European AI On-Demand (AI4EU) Platform. The project will integrate state of the art automated planning systems into an API that can be used to realize automated decision-making solutions for the industry. The project is driven by use-cases provided by industrial actors. AIPlan4EU also provides cascade funding aimed at integrating new use cases as well as new planning engines.
Funder: EU H2020
Partners: please see project website.