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FowardX将在MODEX 2020上推出视觉自主移动机器人和机器人计算系统

   日期:2020-02-13     浏览:24    评论:0    
核心提示:ForwardX机器人依靠具有深度学习技术的V-SLAM进行定位和导航。 它们将在MODEX 2020的1207展位上展出

2020年2月13日-在MODEX 2020上,ForwardX将推出其视觉自主移动机器人。 ForwardX的机器人不再依赖LiDAR SLAM(同时定位和制图),而是依靠传感器融合解决方案,而Computer Vision(CV)是LiDAR,编码器和IMU数据作为控制的辅助反馈来进行定位和避障的主要来源。环。 LiDAR是2D的,因此3D CV技术提供了适用于仓库,配送中心和蜂窝制造环境的解决方案。

February 13, 2020 - At MODEX 2020, ForwardX will be introducing their Visual Autonomous Mobile Robot. Rather than relying on LiDAR SLAM (Simultaneous Localization And Mapping), ForwardX’s robots depend on a sensor fusion solution with Computer Vision (CV) being the primary source for localization and obstacle avoidance with LiDAR, Encoder, and IMU data as secondary feedback to the control loop. LiDAR is 2D, so 3D CV technology offers a solution suited for warehouse, distribution center, and cellular manufacturing environments.


鉴于仓库,配送中心,直接面向消费者(D2C)的商业模式,AMR(自主移动机器人)的数量激增,质疑创新的有效性值得研究。与往年的与会者要走数天的展示厅不同,许多人使用MHI MODEX应用程序并仅参观一天。因此,ForwardX邀请AMR决策者参加1207号展位。

Given the proliferation of warehouses, distribution centers, direct to consumer (D2C) business models, fleets of AMRs (Autonomous Mobile Robots), questioning the validity of innovation is worthy of examination. Unlike in prior years when attendees would walk the show floor for days, many are using the MHI MODEX app and visiting for just a single day. For that reason, ForwardX invites AMR decision-makers to Booth 1207.


将在MODEX 2020上展出的另一个介绍将是ForwardX的基于Edge / Cloud的架构。在边缘,机器人计算系统(RCS)包括多传感器融合,基于计算机视觉的深度学习,基于V-SLAM的局部路径规划能力和强化学习。机器人流程管理器(RPM)位于云中。 RPM包含一个多代理程序群集调度系统。它从操作员那里接收任务,例如仓库管理系统(WMS),制造执行系统(MES)和ERP(企业资源计划)。

Another introduction that will be on display at MODEX 2020 will be ForwardX’s Edge/Cloud based architecture. At the edge, Robot Computing System (RCS) includes the multi-sensor fusion, computer vision based deep learning, local path planning ability based on V-SLAM and reinforcement learning. In the cloud resides the Robotic Process Manager (RPM). The RPM includes a multi-agent cluster scheduling system. It receives tasks from operators, such as Warehouse Management Systems (WMS), Manufacturing Execution Systems (MES) and ERP (Enterprise Resource Planning).


机器人从整体上为机器人分配全局任务和智能路径。 ForwardX机器人依靠具有深度学习技术的V-SLAM进行定位和导航。 这使机器人可以更轻松地适应基于LiDAR的机器人无法处理的变化环境。 “实时学习”的能力允许使用Deep-Q学习和异步优势演员批评技术收集大数据来预测障碍物的速度和方向。 这在这些机器人中创建了更直观的特性,从而推动了更协作,更有效的本地路径规划。

The robots holistically assign global tasks and intelligent paths for the robots. ForwardX robots depend on V-SLAM with Deep Learning techniques for positioning and navigation. This allows the robot to adapt more readily to changing environments that LiDAR-only based robots cannot handle. The ability to “learn on the fly” allows the big data gathered to predict velocity and direction of obstacles using Deep-Q-Learning and Asynchronous Advantage Actor-Critic techniques. This creates a more intuitive nature in these robots which drives a more collaborative and efficient local path planning.


此外,通过分析设施中通常采用的路径,神经网络变得更加智能。 这意味着机器人将开始预测下一个交付解决方案的外观,因此可以更好地适应高峰和低谷的交付要求。

Furthermore, by analyzing the paths that are typically taken in a facility the neural network becomes more intelligent. This means the robots will start to predict what the next delivery solution might look like and therefore be better prepared to accommodate peaks and troughs of delivery requirements.


 
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