Message-oriented and robotics middleware play an important role in facilitating robot control, as well as abstracting complex functionality and unifying communication patterns across networks of sensors and devices. However, the use of multiple middleware frameworks presents a challenge in integrating different robots within a single system. To address this limitation, we present Wrapyfi, a Python wrapper supporting multiple message-oriented and robotics middleware, including ZeroMQ, YARP, ROS, and ROS~2. Using Wrapyfi eases the development of scripts that run on multiple machines, thereby enabling cross-platform communication and workload distribution. We evaluated Wrapyfi in practical settings by conducting two user studies, using multiple sensors transmitting readings to deep learning models and robots such as the iCub and Pepper via different middleware. The results demonstrated Wrapyfi's usability in practice allowing for multi-middleware exchange and controlled process distribution in a real-world setting. More importantly, we showcased Wrapify's most prominent features by bridging interactions between sensors, deep learning models, and robots.
翻译:信息导向器和机器人中继器在推动机器人控制、提取复杂的功能和跨传感器和装置网络的通信模式方面发挥着重要作用。 但是,多个中继器框架的使用在将不同机器人整合到一个单一系统方面提出了挑战。为了应对这一限制,我们介绍了支持多信息导向器和机器人中继器的Python包装器,包括ZeroMQ、YARP、ROS和ROS~2。 使用 Paxyfi方便了多台机器运行的脚本的开发,从而使得跨平台通信和工作量分配成为可能。 我们通过开展两个用户研究,利用多个传感器向深层学习模型和机器人,例如iCub和通过不同中继器传输阅读的iCub和Pepep等,评估了在实际环境中的Apressyfi。 结果表明,在实际操作中,Maxyfi的可用性允许多中继器交换和控制流程分配。 更重要的是,我们通过连接传感器、深学习模型和机器人之间的交互作用,展示了最突出的特征。