The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this article, we, therefore, present the MAPE-K HMT framework, which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-K HMT in both a simulated and physical environment, and we discuss how the various HMT models are connected and can be integrated into a MAPE-K solution.

Temporal task planning guarantees a robot will succeed in its task as long as certain explicit and implicit assumptions about the robot’s operating environment, sensors, and capabilities hold. A robot executing a plan can silently fail to fulfill the task if the assumptions are violated at runtime. Monitoring assumption violations at runtime can flag silent failures and also provide mitigation and remediation opportunities. However, this requires means for describing assumptions combining temporal and quantitative data, automatic construction of correct monitors and ensuring a correct interplay between the planning execution and monitors. In this paper we propose combining temporal planning with stream runtime verification, which offers a high-level language to describe monitors together with guarantees on execution time and memory usage. We demonstrate our approach both in real and simulated flights for some typical mission scenarios.

The design of systems that can change their behaviour to account for scenarios that were not foreseen at design time remains an open challenge. In this article, we propose an approach for adaptation of mobile robot missions that is not constrained to a predefined set of mission evolutions. We implement an adaptive software architecture and show how controller synthesis can be used both to guarantee correct transitioning from the old to the new mission goals with runtime architectural reconfiguration to include new software actuators and sensors if necessary. The architecture brings together architectural concepts that are commonplace in robotics such as temporal planning, discrete, hybrid and continuous control layers together with architectural concepts from adaptive systems such as runtime models and runtime synthesis. We validate the architecture flying several missions taken from the robotic literature for different real and simulated UAVs.

Temporal logic task planning for robotic systems suffers from state explosion when specifications involve large numbers of discrete locations. We provide a novel approach, particularly suited for task specifications with universally quantified locations, that has constant time with respect to the number of locations, enabling synthesis of plans for an arbitrary number of them. We propose a hybrid control framework that uses an iterator to manage the discretised workspace hiding it from a plan enacted by a discrete event controller. A downside of our approach is that it incurs in increased overhead when executing a synthesised plan. We demonstrate that the overhead is reasonable for missions of a fixed-wing Unmanned Aerial Vehicle in simulated and real scenarios for up to 700000 locations.