Measurable Augmented Reality for Prototyping Cyberphysical Systems: A Robotics Platform to Aid the Hardware Prototyping and Performance Testing of Algorithms

Planning, control, perception, and learning are current research challenges in multirobot systems. The transition dynamics of the robots may be unknown or stochastic, making it difficult to select the best action each robot must take at a given time. The observation model, a function of the robots' sensor systems, may be noisy or partial, meaning that deterministic knowledge of the team's state is often impossible to attain. Moreover, the actions each robot can take may have an associated success rate and/or a probabilistic completion time. Robots designed for real-world applications require careful consideration of such sources of uncertainty, regardless of the control scheme or planning or learning algorithms used for a specific problem. Understanding the underlying mechanisms of planning algorithms can be challenging due to the latent variables they often operate on. When performance testing such algorithms on hardware, the simultaneous use of the debugging and visualization tools available on a workstation can be difficult. This transition from experimentation to implementation becomes especially challenging when the experiments need to replicate some feature of the software tool set in hardware, such as simulation of visually complex environments. This article details a robotics prototyping platform, called measurable augmented reality for prototyping cyberphysical systems (MAR-CPS), that directly addresses this problem, allowing for the real-time visualization of latent state information to aid hardware prototyping and performance testing of algorithms.