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Results for "gripper mechanism"

Synthesis of a Six-Bar Gripper Mechanism for Aerial Grasping

arXiv ·

This paper presents the synthesis of a 1-DoF six-bar gripper mechanism for aerial grasping, designed for a task in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The synthesis process involves selecting the mechanism class, determining the number of links and joints using algebraic methods, and optimizing link dimensions via geometric programming. The gripper was modeled in CAD software, additively manufactured, and mounted on a UAV with a DC motor for gripping spherical objects. Why it matters: The research contributes to advancements in robotics and aerial manipulation, with potential applications in various industries, particularly for tasks requiring remote object retrieval and manipulation.

The intelligence of the hand

MBZUAI ·

Lorenzo Jamone from Queen Mary University of London presented on cognitive robotics, focusing on tactile exploration and manipulation by robots. The talk covered combining biology, engineering, and AI for advanced robotic systems. Jamone directs the CRISP group and has over 100 publications in cognitive robotics. Why it matters: This highlights the ongoing research into more sophisticated robotic systems that can interact with complex environments, an area crucial for future applications in manufacturing and human-robot collaboration in the GCC.

Advance Simulation Method for Wheel-Terrain Interactions of Space Rovers: A Case Study on the UAE Rashid Rover

arXiv ·

This paper introduces a virtual wheel-terrain interaction model developed and validated for the UAE Rashid rover to enhance simulation accuracy for space rovers. The model incorporates wheel grouser properties, slippage, soil properties, and interaction mechanics, validated via lunar soil simulation. Experiments tested a Grouser-Rashid rover wheel at slip ratios of 0, 0.25, 0.50, and 0.75. Why it matters: This simulation method advances rover design and control, crucial for the UAE's space exploration program and lunar mission success.

Team NimbRo at MBZIRC 2017: Autonomous Valve Stem Turning using a Wrench

arXiv ·

Team NimbRo's robot Mario won the MBZIRC 2017 Challenge 2 by autonomously manipulating a valve stem using a wrench. The robot uses an omnidirectional base for locomotion and a 3D laser scan detector to find the manipulation panel. A deep neural network detects and selects the correct tool from grayscale images, and motion primitives are adapted to turn the valve stem. Why it matters: This work demonstrates advanced robotic manipulation capabilities relevant for industrial automation and hazardous environment operations in the region.

Autonomous Cooperative Wall Building by a Team of Unmanned Aerial Vehicles in the MBZIRC 2020 Competition

arXiv ·

This paper details an autonomous cooperative wall-building system using UAVs developed for Challenge 2 of the MBZIRC 2020 competition. The system employs scanning, RGB-D detection, precise grasping, and multi-UAV coordination to place bricks on a wall. The CTU-UPenn-NYU approach achieved the highest score in the competition by correctly placing the most bricks. Why it matters: This demonstrates advanced capabilities in robotics and autonomous systems relevant for construction and infrastructure development in challenging environments.

Super-aligned Machine Intelligence via a Soft Touch

MBZUAI ·

Song Chaoyang from the Southern University of Science and Technology (SUSTech) presented research on Vision-Based Tactile Sensing (VBTS) for robot learning, combining soft robotic design with learning algorithms to achieve state-of-the-art performance in tactile perception. Their VBTS solution demonstrates robustness up to 1 million test cycles and enables multi-modal outputs from a single, vision-based input, facilitating applications such as amphibious tactile grasping and industrial welding. The talk also highlighted the DeepClaw system for capturing human demonstration actions, aiming for a universal interaction interface. Why it matters: This research advances embodied intelligence by improving robot dexterity and adaptability through enhanced tactile sensing, which is crucial for complex manipulation tasks in various sectors such as manufacturing and healthcare within the region.

From mobility to movability

KAUST ·

Dr. Jeffrey Schnapp from Harvard University discussed the shift from mobility to movability and human-centric autonomy in robotics at KAUST's 2018 Winter Enrichment Program. He presented Gita, a cargo robot designed to move like humans and support pedestrian lifestyles. Piaggio Fast Forward, Schnapp's company, aims to create robots that coexist with humans and enhance the quality of life in pedestrian-friendly environments. Why it matters: This highlights KAUST's engagement with innovative robotics research and its focus on exploring human-robot interaction for future urban development in Saudi Arabia.

Self-powered dental braces

KAUST ·

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