Robot learns from demonstration in car battery dismantling
Efficient and safe disassembly of electric vehicle batteries is crucial for meeting the growing demand for sustainable transportation. By integrating automation, machine learning, and teleoperation, the process can be optimized to ensure worker safety, precision, and adaptability to diverse battery designs. These innovations not only reduce risks and environmental impact but also accelerate recycling efforts, paving the way for a more sustainable and scalable electric vehicle ecosystem.
Battery Recycling: How to Handle the Battery Pack Disassembly Process
ABEE is leading the way in sustainable battery recycling, focusing on the efficient disassembly of electric vehicle (EV) battery packs. Through the integration of robotics and automation, ABEE enhances safety and operational efficiency in the recycling process, while contributing to a circular economy by repurposing or recovering valuable materials for future use in energy solutions.
Smart handling of contact lenses in automated manufacturing
The SMARTHANDLE project focuses on developing advanced handling systems for automated manufacturing processes. The project brings together partners specialising in hardware, software, and specific manufacturing needs to innovate and improve automated handling technologies. The authors of this blog participate in the Menicon use case.
ALUMIL participates in the European Research Project SMARTHANDLE
ALUMIL is pleased to announce its participation as a pilot case in the European Research Project SMARTHANDLE which aims to address the modern challenges of the manufacturing sector in the European Union by enhancing productivity, flexibility, and sustainability in the production process.
SMARTHANDLE - Advancing Human-Centric Robotics for Future Work Environments
Advancements in AI and robotics are reshaping our work environments. Today, robots are integrated into workplaces to support employees, lighten their workload, and increase efficiency. Despite these benefits, the introduction of robots into the workforce can cause disruptions in the way we work.
Revolutionizing Robotic Automation: The Role of Machine Learning in 6D Pose Estimation
In the ever-evolving landscape of robotics and automation, achieving accurate 6D pose estimation has become a fundamental requirement for advancing various handling tasks. From depalletizing to pick-and-place, the ability of robots to accurately understand the spatial orientation and position of objects is essential. However, traditional hard-coded solutions have proven inadequate, often failing in the face of even minor changes in setup, leading to downtime and significant costs associated with constant readjustment.