Skip to main content

4 posts tagged with "use case"

View All Tags

· 9 min read
Nina Bażela

Unmanned Aerial Vehicles (UAVs), also known as drones, are used nowadays in a wide variety of applications, exploring zones affected by natural disasters, surveilling, or transporting packages in urban areas. While drones provide opportunities previously unavailable, such as the ability to easily traverse rough terrain, quickly and efficiently explore unknown areas, and possess excellent communication capabilities, they also come with their own limitations. UAVs have a reduced flight time due to low battery capacity and cannot carry heavy payloads, including more powerful computers, heavy sensors, or manipulators.

These constraints can be overcome by developing cooperation between drones and Unmanned Ground Vehicles (UGVs), which offer higher payload and battery capacity. Simultaneously, UGVs can benefit from drones' greater range of sensors and higher traversability of difficult terrain. In scenarios where the drone and mobile robot cooperate to map and navigate unknown terrain, there is potential for achieving much higher efficiency in specific tasks, creating a whole new realm of possible applications.

· 6 min read
Nina Bażela

Wouldn’t it be great to have a robot that simply follows you around, carrying your heavy backpack, groceries or whatever else you need to transport? And what if such solutions were available for industry, allowing construction workers and operators in logistics to be relieved from transporting heavy cargo and freeing their hands to perform more meaningful tasks?

Founded in 2015 by the Piaggio Group, Piaggio Fast Forward (or PFF for short) is a company that recognizes this challenge, developing cutting-edge technology for human following, aiming to improve mobility for the entire human-built environment, from workplaces to pedestrian use.


· 5 min read
Nina Bażela

We all know that limiting CO2 emissions is one of the most urgent goals we face today. But what if I tell you that, at this point, limiting emissions simply isn't enough, and we must also start actively removing CO2 from the atmosphere? What’s more, with regulations introduced in recent years, carbon removal is no longer just a matter of social responsibility, but it has become a business necessity for many companies. The price of emitting one tonne of CO2 is almost €95 in the European Union and $51 in the USA, creating both an ecological and business need to develop efficient carbon removal techniques. Unfortunately, most carbon removal techniques are expensive, do not generate financial profit for the companies and produce waste instead of a useful outcome.

But what if companies could remove carbon, thus reducing CO2 emissions, without incurring costs? What if they could actually use the removed carbon for something useful and profitable instead of discarding it? This idea stands behind the project of Rock-Farm, a business that provides services of carbon removal by… building rock walls. What’s even more interesting, they do it fully autonomously, using Panther as a base for their masonry robot.

· 6 min read
Nina Bażela

Designing autonomous solutions for agriculture is not an easy task. While there are successful examples of robots autonomously planting, inspecting or picking more accessible crops, there is still a wide variety of fruits and vegetables that remain out of reach when it comes to autonomous harvesting. And there are reasons for that - agricultural environments can be harsh and extremely variable, posing the challenges both to the mechanical robustness of the robot platform and its abilities for autonomous navigation. Another problem to solve is implementing crop picking, which requires algorithms for efficient fruit localization, assessment of its ripeness and plucking without causing damage to the crop, which is especially demanding in case of fragile fruits.

All these challenges didn’t discourage members of EPFL CREATE Lab, who recently developed a proof-of-concept solution for harvesting raspberries, using Panther as a base for their mobile robot. Their novel approach allowed them to achieve 80% successful harvesting rate during the first field test. What was their key to success?