Asparagus is a time-consuming and labour-intensive crop to harvest as it must be selectively harvested multiple times per day. Junior is an autonomous asparagus harvesting robot capable of performing selective harvesting of ripe asparagus spears. This is accomplished using a tread-based drive system to navigate an asparagus field, a camera and vision program to identify and locate ripe asparagus spears, and a cutter/gripper tooling arm to pick the spears. With Junior, asparagus can be harvested more efficiently and reliably.

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Our goal is to design a cheap and universal collaborative robotics solution, which can be easily integrated with existing industrial robotic manipulators in use, and make them collaborative and safe enough for humans to work alongside them. The proposed solution uses Computer vision for object detection, using depth and vision information from cameras. These cameras are placed upon the robot that covers the entire field of motion. The system output instructions from obtained data that creates collision-free pathing for the robot without affecting its performance.

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Performance in the drone technology space is currently limited. Exchanging materials such as payloads and batteries could lengthen mission times and broaden mission scope. Novel applications like these are only the start of what’s possible with autonomous drone docking technology. Included in our solution is the capability of docking onto a moving platform, thus allowing for easy drone redeployment, all in a lightweight and robust package. Solutions start here!

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Many people take communication for granted in their everyday lives. Most people wouldn’t think twice about walking into a Starbucks and ordering a drink, but this is a difficult task for those who use ASL as their primary form of communication. Traduttore is an ASL-to-English communication device that leverages machine learning to bridge the communication gap between ASL users and those who do not know sign language in service environments.

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Autonomous vehicles will lead to increased safety and productivity but current sensor suites are not cost effective and scalable for large vehicle fleets. RT-City provides Vehicle-to-Infrastructure capabilities for connected vehicle fleets by developing a distributed perception and localization network installed along roads. Partnering with the MVS Lab at the University of Waterloo, RT-City utilizes nodes made of cameras and LiDARs to perform deep learning, global sensor fusion, cloud orchestration and 5G communication for safe and effective navigation of connected vehicle systems.

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On the ISS, astronauts must constantly engage in a variety of exercises to prevent physiological deconditioning in microgravity environments. Currently, there are gaps in astronauts’ training regimens that don’t address plyometrics – the human body’s ability to generate explosive forces. Consequently, astronauts need extensive rehab to achieve simple tasks like getting up from a chair after long term missions. Plyo-Metrics addresses this problem by applying inverted Earth forces to astronauts in space so they can maintain their plyometric strength as humans foray deeper into the cosmos!

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Storage space is important in homes, but residential space is often wasted due to difficulty and safety. Many people pay hundreds of dollars each month renting storage lockers, yet there is unused potential storage space in their own home. Autoshelf aims to solve these issues by using an automated robot to store and retrieve items. Our automated system extends to places deemed unreachable to safely utilize the maximum amount of space in your home.

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