Loitering Munitions and Unpredictability: Autonomy in Weapon Systems and Challenges to Human Control

This report, published by the Center for War Studies, University of Southern Denmark and the Royal Holloway Centre for International Security, highlights the immediate need to regulate autonomous weapon systems, or ‘killer robots’ as they are colloquially called. Written by Dr. Ingvild Bode and Dr. Tom F.A. Watts, authors of an earlier study of air defence systems published with Drone Wars UK, the “Loitering Munitions and Unpredictability” report examines whether the use of automated, autonomous, and AI technologies as part of the global development, testing, and fielding of loitering munitions since the 1980s has impacted emerging practices and social norms of human control over the use of force. It is commonly assumed that the challenges generated by the weaponization of autonomy will materialise in the near to medium term future. The report’s central argument is that whilst most existing loitering munitions are operated by a human who authorizes strikes against system-designated targets, the integration of automated and autonomous technologies into these weapons has created worrying precedents deserving of greater public scrutiny. Loitering munitions – or ‘killer drones’ as they are often popularly known – are expendable uncrewed aircraft which can integrate sensor-based analysis to hover over, detect and explode into targets. These weapons are very important technologies within the international regulatory debates on autonomous weapon systems – a set of technologies defined by Article 36 as weapons “where force is applied automatically on the basis of a sensor-based targeting system”. The earliest loitering munitions such as the Israel Aerospace Industries Harpy are widely considered as being examples of weapons capable of automatically applying force via sensor-based targeting without human intervention. A May 2021 report authored by a UN Panel of Experts on Libya suggests that Kargu-2 loitering munitions manufactured by the Turkish defence company STM may have been “programmed to attack targets without requiring data connectivity between the operator and the munition”. According to research published by Daniel Gettinger, the number of states producing these weapons more than doubled from fewer than 10 in 2017 to almost 24 by mid-2022. The sizeable role which loitering munitions have played in the ongoing fighting between Russia and the Ukraine further underscores the timeliness of this report, having raised debates on whether so called “killer robots are the future of war?” Most manufacturers of these weapons characterize loitering munitions as “human in the loop” systems. The operators of these systems are required to authorize strikes against system-designated targets. The findings of this report, however, suggest that the global trend toward increasing autonomy in targeting has already affected the quality and form of control over the use of force that humans can exercise over specific targeting decisions. Loitering munitions can use automated, autonomous, and to a limited extent, AI technologies to identify, track, and select targets. Some manufacturers also allude to the potential capacity of the systems to attack targets without human intervention. This suggests that human operators of loitering munitions may not always retain an ability to visually verify targets before attack. This report highlights three principal areas of concern: Greater uncertainties regarding how human agents exert control over specific targeting decisions. The use of loitering munitions as anti-personnel weapons and in populated areas. Potential indiscriminate and wide area effects associated with the fielding of loitering munitions. This report’s analysis is drawn from two sources of data: first, a new qualitative data catalogue which compiles the available open-source information about the technical details, development history, and use of autonomy and automation in a global sample of 24 loitering munitions; and second, an in-depth study of how such systems have been used in three recent conflicts – the Libyan Civil War (2014-2020), the 2020 Nagorno-Karabakh War, and the War in Ukraine (2022-). Based on its findings, the authors urge the various stakeholder groups participating in the debates at the United Nations Convention on Certain Conventional Weapons Group of Governmental Experts and elsewhere to develop and adopt legally binding international rules on autonomy in weapon systems, including loitering munitions as a category therein. It is recommended that states: Affirm, retain, and strengthen the current standard of real-time, direct human assessment of, and control over, specific targeting decisions when using loitering munitions and other weapons integrating automated, autonomous, and AI technologies as a firewall for ensuring compliance with legal and ethical norms. Establish controls over the duration and geographical area within which weapons like loitering munitions that can use automated, autonomous, and AI technologies to identify, select, track, and apply force can operate. Prohibit the integration of machine learning and other forms of unpredictable AI algorithms into the targeting functions of loitering munitions because of how this may fundamentally alter the predictability, explainability, and accountability of specific targeting decisions and their outcomes. Establish controls over the types of environments in which sensor-based weapons like loitering munitions that can use automated, autonomous, and AI technologies to identify, select, track, and apply force to targets can operate. Loitering munitions functioning as AWS should not be used in populated areas. Prohibit the use of certain target profiles for sensor-based weapons which use automated, autonomous, and AI technologies in targeting functions. This should include prohibiting the design, testing, and use of autonomy in weapon systems, including loitering munitions, to “target human beings” as well as limiting the use of such weapons “to objects that are military objectives by nature” (ICRC, 2021: 2.). Be more forthcoming in releasing technical details relating to the quality of human control exercised in operating loitering munitions in specific targeting decisions. This should include the sharing, where appropriate, of details regarding the level and character of the training that human operators of loitering munitions receive.  Funding: Research for the report was supported by funding from the European Union’s Horizon 2020 research and innovation programme (under grant agreement No. 852123, AutoNorms project) and from the Joseph Rowntree Charitable Trust. Tom Watts’ revisions to this report were supported by the funding provided by his Leverhulme Trust Early Career Research Fellowship (ECF-2022-135). We also collaborated with Article 36 in writing the report. About the authors: Dr Ingvild Bode is Associate Professor at the Center for War Studies, University of Southern Denmark and a Senior Research Fellow at the Conflict Analysis Research Centre, University of Kent. She is the Principal Investigator of the European Research Council-funded “AutoNorms” project, examining how autonomous weapons systems may change international use of force norms. Her research focuses on understanding processes of normative change, especially through studying practices in relation to the use of force, military Artificial Intelligence, and associated governance demands. More information about Ingvild’s her research is available here. Dr Tom F.A. Watts is a Leverhulme Trust Early Career Researcher based at the Department of Politics, International Relations, and Philosophy at Royal Holloway, University of London. His current project titled “Great Power Competition and Remote Warfare: Change or Continuity in Practice?” (ECF-2022-135) examines the relationship between the use of the strategic practices associated with the concept of remote warfare, the dynamics of change and continuity in contemporary American foreign policy, and autonomy in weapons systems. More information about Tom’s research is available here

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Automatic Pipeline Surveillance Air-Vehicle

This thesis presents the developments of a vision-based system for aerial pipeline Right-of-Way surveillance using optical/Infrared sensors mounted on Unmanned Aerial Vehicles (UAV). The aim of research is to develop a highly automated, on-board system for detecting and following the pipelines; while simultaneously detecting any third-party interference. The proposed approach of using a UAV platform could potentially reduce the cost of monitoring and surveying pipelines when compared to manned aircraft. The main contributions of this thesis are the development of the image-analysis algorithms, the overall system architecture and validation of in hardware based on scaled down Test environment. To evaluate the performance of the system, the algorithms were coded using Python programming language. A small-scale test-rig of the pipeline structure, as well as expected third-party interference, was setup to simulate the operational environment and capture/record data for the algorithm testing and validation. The pipeline endpoints are identified by transforming the 16-bits depth data of the explored environment into 3D point clouds world coordinates. Then, using the Random Sample Consensus (RANSAC) approach, the foreground and background are separated based on the transformed 3D point cloud to extract the plane that corresponds to the ground. Simultaneously, the boundaries of the explored environment are detected based on the 16-bit depth data using a canny detector. Following that, these boundaries were filtered out, after being transformed into a 3D point cloud, based on the real height of the pipeline for fast and accurate measurements using a Euclidean distance of each boundary point, relative to the plane of the ground extracted previously. The filtered boundaries were used to detect the straight lines of the object boundary (Hough lines), once transformed into 16-bit depth data, using a Hough transform method. The pipeline is verified by estimating a centre line segment, using a 3D point cloud of each pair of the Hough line segments, (transformed into 3D). Then, the corresponding linearity of the pipeline points cloud is filtered within the width of the pipeline using Euclidean distance in the foreground point cloud. Then, the segment length of the detected centre line is enhanced to match the exact pipeline segment by extending it along the filtered point cloud of the pipeline. The third-party interference is detected based on four parameters, namely: foreground depth data; pipeline depth data; pipeline endpoints location in the 3D point cloud; and Right-of-Way distance. The techniques include detection, classification, and localization algorithms. Finally, a waypoints-based navigation system was implemented for the air- vehicle to fly over the course waypoints that were generated online by a heading angle demand to follow the pipeline structure in real-time based on the online identification of the pipeline endpoints relative to a camera frame

Autonomous search and tracking of objects using model predictive control of unmanned aerial vehicle and gimbal: Hardware-in-the-loop simulation of payload and avionics

This paper describes the design of model predictive control (MPC) for an unmanned aerial vehicle (UAV) used to track objects of interest identified by a real-time camera vision (CV) module in a search and track (SAT) autonomous system. A fully functional UAV payload is introduced, which includes an infra-red (IR) camera installed in a two-axis gimbal system. Hardware-in-loop (HIL) simulations are performed to test the MPC's performance in the SAT system, where the gimbal attitude and the UAV's flight trajectory are optimized to place the object to be tracked in the center of the IR camera's image.(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works