Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone

This work investigates the problem of simultaneous tracking and jamming of a rogue drone in 3D space with a team of cooperative unmanned aerial vehicles (UAVs). We propose a decentralized estimation, decision and control framework in which a team of UAVs cooperate in order to a) optimally choose their mobility control actions that result in accurate target tracking and b) select the desired transmit power levels which cause uninterrupted radio jamming and thus ultimately disrupt the operation of the rogue drone. The proposed decision and control framework allows the UAVs to reconfigure themselves in 3D space such that the cooperative simultaneous tracking and jamming (CSTJ) objective is achieved; while at the same time ensures that the unwanted inter-UAV jamming interference caused during CSTJ is kept below a specified critical threshold. Finally, we formulate this problem under challenging conditions i.e., uncertain dynamics, noisy measurements and false alarms. Extensive simulation experiments illustrate the performance of the proposed approach.Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Joint Estimation and Control for Multi-Target Passive Monitoring with an Autonomous UAV Agent

This work considers the problem of passively monitoring multiple moving targets with a single unmanned aerial vehicle (UAV) agent equipped with a direction-finding radar. This is in general a challenging problem due to the unobservability of the target states, and the highly non-linear measurement process. In addition to these challenges, in this work we also consider: a) environments with multiple obstacles where the targets need to be tracked as they manoeuvre through the obstacles, and b) multiple false-alarm measurements caused by the cluttered environment. To address these challenges we first design a model predictive guidance controller which is used to plan hypothetical target trajectories over a rolling finite planning horizon. We then formulate a joint estimation and control problem where the trajectory of the UAV agent is optimized to achieve optimal multi-target monitoring

Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning

In this work we propose a coverage planning control approach which allows a mobile agent, equipped with a controllable sensor (i.e., a camera) with limited sensing domain (i.e., finite sensing range and angle of view), to cover the surface area of an object of interest. The proposed approach integrates ray-tracing into the coverage planning process, thus allowing the agent to identify which parts of the scene are visible at any point in time. The problem of integrated ray-tracing and coverage planning control is first formulated as a constrained optimal control problem (OCP), which aims at determining the agent's optimal control inputs over a finite planning horizon, that minimize the coverage time. Efficiently solving the resulting OCP is however very challenging due to non-convex and non-linear visibility constraints. To overcome this limitation, the problem is converted into a Markov decision process (MDP) which is then solved using reinforcement learning. In particular, we show that a controller which follows an optimal control law can be learned using off-policy temporal-difference control (i.e., Q-learning). Extensive numerical experiments demonstrate the effectiveness of the proposed approach for various configurations of the agent and the object of interest.Comment: 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09 December 2022, Cancun, Mexic

Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents

In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.Comment: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 202

Tracking multiple mobile devices in CCTV-enabled areas

Over the last decade, we have witnessed an unprecedented interest in indoor positioning technologies, with a variety of solutions developed in academic and industrial research labs. Although the field has reached a significant level of maturity, there is still no dominant solution and, as a consequence, positioning services are still lacking in many buildings. In order for a solution to be widely implemented and adopted, two key requirements must be satisfied: low cost and high accuracy. The dichotomy between cost and accuracy has fragmented the technology landscape, leading to a plethora of competing solutions that cannot satisfy both requirements simultaneously. The key objective of this thesis is to investigate how to unify the two disparate camps, providing high positioning accuracy with very low cost. Many approaches have tried to achieve this goal by fusing different sensor modalities. However, the majority of existing work has only investigated how to fuse sequences of measurements for which the associations with the targets are known (i.e. device personal data). Sensor fusion techniques that combine device personal data and anonymous sensor streams (where the associations between the measurements and the targets are not known) remain under-explored as of today. In this thesis, we investigate how to efficiently combine device sensor data and anonymous sensor streams from various sensor modalities in order to build low cost and high accuracy positioning systems. By combining these two types of sensor modalities in one system we see a great potential in designing cost-effective and accurate positioning systems for challenging environments such as for tracking people in highly dynamic industrial settings. Our goal is to design a multi-target multi-sensor tracking framework which will utilise existing sensor infrastructure found in industrial environments and large public buildings (e.g museums) in order to provide reliable positioning services.</p

Tracking multiple mobile devices in CCTV-enabled areas

Over the last decade, we have witnessed an unprecedented interest in indoor positioning technologies, with a variety of solutions developed in academic and industrial research labs. Although the field has reached a significant level of maturity, there is still no dominant solution and, as a consequence, positioning services are still lacking in many buildings. In order for a solution to be widely implemented and adopted, two key requirements must be satisfied: low cost and high accuracy. The dichotomy between cost and accuracy has fragmented the technology landscape, leading to a plethora of competing solutions that cannot satisfy both requirements simultaneously. The key objective of this thesis is to investigate how to unify the two disparate camps, providing high positioning accuracy with very low cost. Many approaches have tried to achieve this goal by fusing different sensor modalities. However, the majority of existing work has only investigated how to fuse sequences of measurements for which the associations with the targets are known (i.e. device personal data). Sensor fusion techniques that combine device personal data and anonymous sensor streams (where the associations between the measurements and the targets are not known) remain under-explored as of today. In this thesis, we investigate how to efficiently combine device sensor data and anonymous sensor streams from various sensor modalities in order to build low cost and high accuracy positioning systems. By combining these two types of sensor modalities in one system we see a great potential in designing cost-effective and accurate positioning systems for challenging environments such as for tracking people in highly dynamic industrial settings. Our goal is to design a multi-target multi-sensor tracking framework which will utilise existing sensor infrastructure found in industrial environments and large public buildings (e.g museums) in order to provide reliable positioning services.</p