Qbo’s concept and design

作者讲述了如何从日常物品中获取灵感,设计并实现一款家用小型机器人Qbo的过程。该机器人具备人工智能视觉、语音识别与合成、自主充电等功能,旨在提供与人类互动的智能体验,并通过开源软件促进社区发展。

项目:Qbo’s concept and design

When I started I was sure that this robot had to be “realistic”. I did not want to develop a biped robot or a robot with arms because I knew I would fail due to the lack of resources. Once I had that point clear, I focused my efforts on thinking what I would like to have in my own house. Some of my first ideas were that it should be small, something similar to a cat or a dog, it would move quickly in order to interact with me in a logical way and it would look nice to fit in my environment. However, the most important thing was being able to experiment in my own robot with all the information about artificial vision, speech recognition and synthesis which was under an open source license on the Internet and was used in research robots. It was also important that as the hardware improved, my robot would improve along with it and that every component was as much standard as possible (aim nearly achieved), that is to say components which were easily found in shops for two obvious reasons: first one because they were already made so I did not have to make them and second one because their price is generally low.

My other goal was being able to sell the robot in the market at a reasonably low price in order to be affordable for as many users as possible and thus create a powerful community around it. On the other hand, as the software, diagrams and firmware of the boards which control the components of the robot would be distributed under an open source license (not determined yet) any user could decide to create his own.

Considering all the information above, it seemed easy to start but that was not the case. How would the design be? How would I turn that design into a 3D software? What about the hardware components? Where would I find people to help me? I started to realize that finishing the project would not be easy at all because I would have to deal with significant expenses to do research, create prototypes, develop software, design and make controller boards in order to release it into the market at a price as much reduced as possible. Many designs went through my head but none was convincing enough. However, one day, while I was in a shopping centre, I laid my eyes on a vacuum cleaner (yes, that is right!) which was displayed horizontally with all its accessories and it caught my attention. I opened it and I realized it was perfect to fit all the components I had in my mind (motors, PC board, sensors, performers and even a battery). Then I thought that if it was in a vertical position with the wheels behind and if I added an idler wheel in front it would have a really attractive body and I would only have to think about the head. “Before I go on and to avoid misunderstandings, after 5 years and hundreds of changes in the 3D design, the current look of the robot is far away from that vacuum cleaner I saw in the shopping centre, actually that vacuum cleaner did not have more than 6 plastic parts and Qbo is esthetically made up of more than 30 plastic parts.

Qbo design & Concept

Thanks to this unexpected discovery I could picture the body of the robot but I still had to think about the head. I was sure about two things: it would be big because with no arms or hands it would be the only element which would interact and it had to be attractive but completely different from a real human face. Personally, I do not like creating robots which look like humans (I will talk about this in future posts). Considering those aspects I had to decide what components would be part of the head since they would affect its design. It was obvious that the robot had to be able to interact with a human being so it was necessary to set microphones and web cams to receive data from the outside, some speakers to transfer information and some elements which could show some kind of emotion (the most difficult part).

Some of Qbo’s “current” skills:

Stereoscopic vision: webcam calibration (2), depth, face, objects and colours recognition, face and object tracking, map generating (under development)
Speech Recognition System
Speech Synthesis System:
it offers a general framework for building speech synthesis systems. Only available in English at the moment.
Thecorpora’s API: Developed to interact with the hardware components of the robot and third parties’ software.
WEB control panel: the robot is accessible through web explorer.
Internet connection through a WIFI controller placed in the head. Real-time software and firmware update.
Obstacles: the robot avoids crashes and falls thanks to ultrasound sensors.
Autocharging: auto-charge battery (testing and developing phase)

一、基础信息 数据集名称:Bottle Fin实例分割数据集 图片数量: 训练集:4418张图片 验证集:1104张图片 总计:5522张图片 分类类别: - 类别0: 数字0 - 类别1: 数字1 - 类别2: 数字2 - 类别3: 数字3 - 类别4: 数字4 - 类别5: 数字5 - 类别6: Bottle Fin 标注格式:YOLO格式,包含多边形坐标,适用于实例分割任务。 数据格式:图片格式常见如JPEG或PNG,具体未指定。 二、适用场景 实例分割AI模型开发:数据集支持实例分割任务,帮助构建能够精确识别和分割图像中多个对象的AI模型,适用于对象检测和分割应用。 工业自动化与质量控制:可能应用于制造、物流或零售领域,用于自动化检测和分类物体,提升生产效率。 计算机视觉研究:支持实例分割算法的学术研究,促进目标检测和分割技术的创新。 教育与实践培训:可用于高校或培训机构的计算机视觉课程,作为实例分割任务的实践资源,帮助学生理解多类别分割。 三、数据集优势 多类别设计:包含7个不同类别,涵盖数字和Bottle Fin对象,增强模型对多样对象的识别和分割能力。 高质量标注:标注采用YOLO格式的多边形坐标,确保分割边界的精确性,提升模型训练效果。 数据规模适中:拥有超过5500张图片,提供充足的样本用于模型训练和验证,支持稳健的AI开发。 即插即用兼容性:标注格式直接兼容主流深度学习框架(如YOLO),便于快速集成到各种实例分割项目中。
先展示下效果 https://pan.quark.cn/s/ed751fc35e7f 在本资源中,我们提供的是一款以"3D小人构建商务场景现代都市高楼背景工作汇报通用商务ppt模板.rar"命名的压缩包文件。 这个压缩包主要应用于制作专业且具备视觉吸引力的商务演示文稿,特别适用于工作汇报和规划工作。 接下来将具体说明这款PPT模板的特质以及可能关联的IT知识点:1. **3D小人与商务场景**:3D小人作为现代PPT设计中常见的一种元素,能够形象地模拟实际工作环境,从而协助观众更透彻地把握演示内容。 这种技术涉及3D建模和渲染,一般借助Blender或3DS Max等软件来构建,并经由Photoshop进行后期处理,以使其与背景无缝对接。 2. **现代都市高楼背景**:此类背景图像为演示注入专业且前沿的氛围,体现了现代商务环境的高效运作和全球化趋势。 背景图像或采用高清摄影,或通过3D渲染技术制作,突出了城市的繁荣与进步,与商务主题高度契合。 3. **绿灰配色**:色彩心理学在设计领域扮演着关键角色。 绿色通常象征创新与环保,而灰色则代表专业与稳重。 这种色彩组合旨在形成一种平衡且和谐的视觉感受,既不会显得过于激进,也不会过于保守,非常适合商务场合。 4. **工作汇报与工作计划**:该模板的设计充分考量了商务环境中常见的两种需求——工作汇报与工作计划。 工作汇报部分可能涵盖图表、数据可视化及关键业绩指标,而工作计划部分则可能包括时间线、任务分配和目标确立。 这需要PPT软件的高级功能,例如Microsoft PowerPoint中的SmartArt图形、图表工具以及动画和过渡效果。 5. **通用商务PPT模板**:这表明模板的设计具有广泛的适用性,能够适应不同种类的商务演示,从...
一、基础信息 数据集名称:工业设备与工具目标检测数据集 图片数量: 训练集:11095张图片 验证集:2667张图片 总计:13762张图片 分类类别: - Control Panel(控制面板) - Cooling Towers(冷却塔) - Forklift(叉车) - Gasoline Can(汽油罐) - Hammer(锤子) - Laptop(笔记本电脑) - Nuclear reactor(核反应堆) - Pebbels(鹅卵石) - Pressure Gauges(压力表) - Pressure vessel(压力容器) - Rope(绳子) - Screw(螺丝) - Screw Driver(螺丝刀) - Toolbox(工具箱) - Turbine Generator(涡轮发电机) - Warning signs(警告标志) - Wrench(扳手) - clamps(夹子) - cranes(起重机) - doors(门) - electrical pylon(电线塔) - pipes(管道) - pliers(钳子) - safety helmet(安全头盔) 标注格式:YOLO格式,包含边界框和类别标签,适用于目标检测任务。 数据格式:图片数据,来源于工业环境。 二、适用场景 工业自动化与机器人视觉:用于在工业环境中自动识别和定位设备、工具和结构,支持机器人操作和自动化系统开发。 安全监控与风险评估:检测安全装备如安全头盔和警告标志,帮助监控工作场所安全,预防事故和进行风险评价。 设备维护与检查:识别压力容器、涡轮发电机等关键设备,用于预测性维护和故障检测应用。 物流与仓储管理:叉车、工具箱等物体的检测,优化仓库操作、库存管理和物流流程。 学术研究与算法开发:支持计算机视觉和人工智能在工业应用中的研究,促进目标检测算法的创新与优化。 三、数据集优势
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