Man against machine: Debates in the expert community on the lethal autonomous systems

In the recent years debates surrounding the autonomous weapons systems development and regulation have gained a new momentum. Despite the fact that the development of such type of weapons continues since the twentieth century, recent technological advances open up new possibilities for development of completely autonomous combat systems that will operate without human in-tervention. In this context, international community faces a number of ethical, legal, and regulatory issues. This paper examines the ongoing debates in both the Western and the Russian expert community on the challenges and prospects for using lethal autonomous systems. The author notes that Russian and Western discourses on most of the issues have very much in common and diff erences are found mainly in the intensity of debates — in the West they are much more ac-tive. In both cases the most active debates focus around two issues: the potential implications of fully autonomous weapons systems including the unclear line of accountability, and the prospects for international legal regulation of the use of lethal autonomous weapons. Both the Russian and the Western experts agree that the contemporary international humanitarian law is unable to handle the challenges posed by aggressive development of the lethal autonomous weapons. All this points to the need to adapt the international humanitarian law to the new realities, which, in turn, requires concerted actions from leading states and international organizations.

Partially Observable Markov Decision Processes and Robotics

Planning under uncertainty is critical to robotics. The partially observable Markov decision process (POMDP) is a mathematical framework for such planning problems. POMDPs are powerful because of their careful quantification of the nondeterministic effects of actions and the partial observability of the states. But for the same reason, they are notorious for their high computational complexity and have been deemed impractical for robotics. However, over the past two decades, the development of sampling-based approximate solvers has led to tremendous advances in POMDP-solving capabilities. Although these solvers do not generate the optimal solution, they can compute good POMDP solutions that significantly improve the robustness of robotics systems within reasonable computational resources, thereby making POMDPs practical for many realistic robotics problems. This article presents a review of POMDPs, emphasizing computational issues that have hindered their practicality in robotics and ideas in sampling-based solvers that have alleviated such difficulties, together with lessons learned from applying POMDPs to physical robots. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Increasingly Intelligent Micromachines

Intelligent micromachines, with dimensions ranging from a few millimeters down to hundreds of nanometers, are miniature systems capable of performing specific tasks autonomously at small scales. Enhancing the intelligence of micromachines to tackle the uncertainty and variability in complex microenvironments has applications in minimally invasive medicine, bioengineering, water cleaning, analytical chemistry, and more. Over the past decade, significant progress has been made in the construction of intelligent micromachines, evolving from simple micromachines to soft, compound, reconfigurable, encodable, multifunctional, and integrated micromachines, as well as from individual to multiagent, multiscale, hierarchical, self-organizing, and swarm micromachines. The field leverages two important trends in robotics research—the miniaturization and intelligentization of machines—but a compelling combination of these two features has yet to be realized. The core technologies required to make such tiny machines intelligent include information media, transduction, processing, exchange, and energy supply, but embedding all of these functions into a system at the micro- or nanoscale is challenging. This article offers a comprehensive introduction to the state-of-the-art technologies used to create intelligence for micromachines and provides insight into the construction of next-generation intelligent micromachines that can adapt to diverse scenarios for use in emerging fields. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Average Consensus and Stability Analysis in Networked Dynamic Systems

This paper provides protocols for finitetime average consensus and finitetime stability of systems with controlled nonlinear dynamics innetwork under undirected fixed topology. Each node’s state is a high dimensional vector as a solution of the highly nonlinear first order dynamics with and without drift terms. This paper provides protocols for finitetime average consensus and finitetime stability of systems with controlled nonlinear dynamics innetwork under undirected fixed topology. Each node’s state is high Under the proposed interaction rules, agreements as a common average value or an average trajectory are reached, solving finitetime average consensus and the multisystem equilibrium is controlled leading to the finitetime stability of each system origin. Sufficient conditions are achieved using the Lyapunov techniques and the graph theory. In networked dynamic systems, the theoretical results of the paper cover a large class of underactuated autonomous systems as formation flight, multivehicle coordination, and heterogeneous multisystem behaviors. Some examples are introduced in simulation which approves the proposed protocols.