Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks

MARLAS: Multi Agent Reinforcement Learning for cooperated Adaptive Sampling

The multi-robot adaptive sampling problem aims at finding trajectories for a team of robots to efficiently sample the phenomenon of interest within a given endurance budget of the robots. In this paper, we propose a robust and scalable approach using decentralized Multi-Agent Reinforcement Learning for cooperated Adaptive Sampling (MARLAS) of quasi-static environmental processes. Given a prior on the field being sampled, the proposed method learns decentralized policies for a team of robots to sample high-utility regions within a fixed budget. The multi-robot adaptive sampling problem requires the robots to coordinate with each other to avoid overlapping sampling trajectories. Therefore, we encode the estimates of neighbor positions and intermittent communication between robots into the learning process. We evaluated MARLAS over multiple performance metrics and found it to outperform other baseline multi-robot sampling techniques. We further demonstrate robustness to communication failures and scalability with both the size of the robot team and the size of the region being sampled. The experimental evaluations are conducted both in simulations on real data and in real robot experiments on demo environmental setup

Human-in-the-loop: Role in Cyber Physical Agricultural Systems

With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion

Safe Learning of Quadrotor Dynamics Using Barrier Certificates

To effectively control complex dynamical systems, accurate nonlinear models are typically needed. However, these models are not always known. In this paper, we present a data-driven approach based on Gaussian processes that learns models of quadrotors operating in partially unknown environments. What makes this challenging is that if the learning process is not carefully controlled, the system will go unstable, i.e., the quadcopter will crash. To this end, barrier certificates are employed for safe learning. The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process. A learning controller is designed to efficiently explore those uncertain states and expand the barrier certified safe region based on an adaptive sampling scheme. In addition, a recursive Gaussian Process prediction method is developed to learn the complex quadrotor dynamics in real-time. Simulation results are provided to demonstrate the effectiveness of the proposed approach.Comment: Submitted to ICRA 2018, 8 page