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DribbleBot Soccer Playing Robot In Action

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Remember the DribbleBot? It is a soccer playing robot developed by MIT’s Improbable Artificial Intelligence Lab, part of the Computer Science and Artificial Intelligence Laboratory (CSAIL) that can dribble a soccer ball like humans. It uses onboard sensing and computing to take on sand, gravel, and other surfaces. The below video shows these robots in action:

MIT’s DribbleBots vs. The New England Revolution

[HT]

Orio Seeding & Weeding Robot

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This is the Orio: a seeding and weeding robot with a 4-wheel drive system that helps farmers become more productive. It uses Lithium Iron Phosphate batteries that last up to 8 hours. This robot has max speed of 5.5km/h. It features autonomous navigation with GNSS RTK.

ANYMal Robot Modified for Steep Planetary Terrain Exploration (ETH Zurich)

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Robots are going to do many things for us in the future, including most of our space exploration. Legged robots such as ANYmal could struggle on slopes and slippery surfaces. Researchers have now modified and ANYmal robot with wheels at its knees to enable it to overcome steep sandy terrains.

Towards Legged Locomotion on Steep Planetary Terrain

[HT: Credits: ETH Zurich, Beyond Gravity, ANYmal Research, ESA]

Make IT Happen Drone w/ MKN-800 Flexible Robotic Arm

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Here is another drone with a flexible arm that can take on a variety of tasks. The Make IT Happen Drone has a MKN-800 Pliable Arm with max speed of 15m/s and payload capacity of 10kg. It can grab items from multiple angles. This drone can be used for inspection, air/sea rescue, and other challenging tasks.

Flying with Hoverboard Air by Flying Kyxz

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This is the Hoverboard Air: a drone designed to take you off the ground and move you around. It can fit in the back of a truck, so it can be taken anywhere for a safe flight. The below video shows this multi-rotor system in action:

Maiden flight with my new HOVER BOARD AIR

[HT]

Reachy Open Source Robot with Face Tracking Feature

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Reachy is an open source interactive robot from Pollen Robotics that can be programmed to handle a wide variety of tasks. It uses cameras to perceive its environment. You can program it to serve drinks, cook food, and do a whole lot more. This video shows its face tracking feature.

Reachy's Face tracking feature.

With its face tracking app, the robot can locate people in its field of view. It uses an AI face detection module on its Edge TPU. It moves its head to follow the closest person.

[HT]

Lynxmotion SES-PRO 550mm Modular Robot Arm

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Lynxmotion AL5 Robotic Arm by RobotShop.com

This is the Lynxmotion SES-PRO: a modular robot arm with 550m horizontal reach, 2kg payload and 0.1mm accuracy that you can use on a fixed surface or mobile platform. It is re-configurable, so you can add more degrees of freedom. It uses NEMA stepper motors and strain wave gearing.

The Lynxmotion SES-PRO system has an aluminum construction.

[where to get it]

Loti-Bot Programmable Coding Robot

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Here is another coding robot that teaches kids programming concepts. The Loti-Bot has sensors that enable it to respond to its environment. It also has programmable LEDs. It supports Scratch block-based programming. The Loti has drawing capability, so it can draw complex 2D shapes.

XAG M2000 Remote Sensing Drone for Aerial Survey Tasks

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Meet the XAG M2000 Remote Sensing Drone: a smart drone designed for aerial survey tasks. It has max battery life of 90 minutes and can fly for 86 km. It features 4G data transmission. This drone combines with the XPilot app for aerial mapping. The XAG M2000 has cruise speed of 15m/s. It can resist winds up to 8m/s.

This Robot Hand Rotates Without Seeing

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Rotating an object in our hand is something that we do naturally without a whole lot of thinking. It is much more challenging to robots. This video from HKUST UC San Diego shows what it takes to build a robot hand that can rotate objects without vision.

Rotating without Seeing: Towards In-hand Dexterity through Touch

This in-hand object rotation system uses “dense binary force sensors (touch or no touch) overlaying one side of the whole robot hand (palm, finger links, fingertips).” Its in-hand policy was trained using “Reinforcement Learning on diverse objects in simulation.” This video by Binghao Huang shows this robot in action.

[HT]

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