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Titlebook: Robotics in Natural Settings; CLAWAR 2022 José M. Cascalho,Mohammad Osman Tokhi,Matthias Fun Conference proceedings 2023 The Editor(s) (if

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樓主: 呻吟
51#
發(fā)表于 2025-3-30 09:38:09 | 只看該作者
A Survey of?Wheeled-Legged Robotsng direction. The missing examples in nature make designing templates that capture the underlying locomotion principles cumbersome, making hybrid locomotion challenging. This paper reviews some of the novel locomotion frameworks overcoming these challenges.
52#
發(fā)表于 2025-3-30 14:03:36 | 只看該作者
53#
發(fā)表于 2025-3-30 17:41:13 | 只看該作者
54#
發(fā)表于 2025-3-30 21:40:04 | 只看該作者
Design Optimization of?a?Four-Bar Leg Linkage for?a?Legged-Wheeled Balancing Robote minimum pitch angle correction of the robot’s base while varying the leg extension. Gravity compensation is further achieved through an optimized torsional spring. Finally, we evaluate the performance of the leg linkage and gravity compensation mechanism on real hardware.
55#
發(fā)表于 2025-3-31 04:37:11 | 只看該作者
56#
發(fā)表于 2025-3-31 06:54:48 | 只看該作者
Key Steps Toward Development of Humanoid Robotsbe easily understood. The extra benefit of such clearer explanation is to enable us to identify remaining challenges faced by the development of humanoid robots. Also, the well-designed invention of human-like humanoid robots will help us to discover the secrets behind the mind of human beings.
57#
發(fā)表于 2025-3-31 09:25:41 | 只看該作者
Grasping Characteristics of Flexible Propulsion Unit Using Braid Mechanism for Lunar Exploration Robieve curving excavation for the second target mission, modeled the gripping force of the proposed mechanism, and measured the gripping force of the prototype using the mechanism. The experiment demonstrated the validity of the modeling and the usefulness of the proposed mechanism.
58#
發(fā)表于 2025-3-31 14:44:00 | 只看該作者
59#
發(fā)表于 2025-3-31 20:42:56 | 只看該作者
Control of?Wheeled-Legged Quadrupeds Using Deep Reinforcement Learningf-the-arts in legged locomotion using RL, our preliminary results show that RL is a promising framework for wheeled-legged robots. The policy learns to dynamically switch between driving mode and walking mode in response to the user command and terrain.
60#
發(fā)表于 2025-3-31 22:21:11 | 只看該作者
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