We show that a distributed network of robots or other devices which make measurements of each other can collaborate to globally localise via efficient ad-hoc peer to peer communication. Our Robot Web solution is based on Gaussian Belief Propagation on the fundamental non-linear factor graph describing the probabilistic structure of all of the observations robots make internally or of each other, and is flexible for any type of robot, motion or sensor. We define a simple and efficient communication protocol which can be implemented by the publishing and reading of web pages or other asynchronous communication technologies. We show in simulations with up to 1000 robots interacting in arbitrary patterns that our solution convergently achieves global accuracy as accurate as a centralised non-linear factor graph solver while operating with high distributed efficiency of computation and communication. Via the use of robust factors in GBP, our method is tolerant to a high percentage of faults in sensor measurements or dropped communication packets.
翻译:我们显示,一个分布式机器人或其他装置的网络可以相互测量,通过高效的特设对等点和同行通信,合作实现全球本地化。我们的机器人网络解决方案基于基本的非线性系数图上的高西亚信仰推介,描述所有观测机器人内部或彼此之间产生的概率结构,对于任何类型的机器人、运动或传感器都具有灵活性。我们定义了一个简单而高效的通信协议,可以通过出版和阅读网页或其他不同步的通信技术来实施。我们在模拟中展示了多达1000个在任意模式下互动的机器人,我们的解决方案在以高分布式计算和通信效率运行的集中型非线性系数图形求解器的同时,实现了全球准确性。通过使用硬性系数,我们的方法可以容忍传感器测量或丢弃通信包中的高比例错误。