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Model predictive control based multi-target tracking using a swarm of fixed wing UAVsAuthor: ANIMESH SAHU 20161028 Date: 2022-04-09 Report no: IIIT/TH/2022/27 Advisor:Harikumar K AbstractUnmanned aerial vehicles (UAVs) are considered to be among the most advanced robotics technologies available today. They are capable of supporting and transporting sensors, communication equipment, and a variety of other payloads. UAVs are cost-effective, and with better sensing technology, they are being utilized by a wide variety of enterprises, both military and commercial. They have been acknowledged in recent years for their ability to perform a variety of tasks, particularly in the military domain like surveillance, convoy protection, border patrol. One of the key aspects involved in these military applications is target tracking. The existing literature on target tracking is primarily focused on tracking a single target. In this thesis, a Model Predictive Control (MPC) based algorithm is proposed for tracking multiple ground target swarms of unmanned aerial vehicles (UAVs). Swarm of UAVs have distinct advantages when compared to a swarm of unmanned aerial vehicles (UGVs). When used for applications like multi-target tracking with a sensor like a camera, tracking through aerial view avoids issues like loss of observability which is typically associated with monocular vision-based target tracking from the ground. Another advantage of UAVs over UGVs is that the tracking in case of UAVs is simple while tracking through UGVs due to obstacles and the restricted environment becomes complex. There are two primary types of UAVs, namely fixed-wing aircraft(FWA) and multi-rotor UAVs. FWA because of their ability to cover longer distances at higher speeds, are preferred in military applications, but they come with a disadvantage that they come with lower bound on speed and non-zero-turn radius. Multi-target tracking through UAVs has numerous difficulties, owing to the maneuverability of target, atmospheric disturbances. MPC has been shown to be an efficient method for process control, tracking, path planning, and the ability to handle disturbances well, among other applications. Future commands with a short time horizon are determined with high precision using future prediction and optimization techniques. The primary argument for choosing MPC over other techniques is its ability to adequately account for constraints, allowing all processes to run at their maximum possible performance. All UAVs here belong to the fixed-wing aircraft category having flight velocity, climb rate, and turn rate limits. Each UAV is equipped with a downward-facing camera for the purpose of detecting and tracking the target. Two scenarios are studied in which the total number of UAVs equals the total number of targets in the first scenario. In the second situation, the number of UAVs is less than the number of targets, resulting in a conservative solution in which the objective is to maximize the average time duration that targets are within the FOV of any of the UAV’s cameras. To relate the hyperparameters utilized in MPC to mission efficiency, a data-driven Gaussian process (GP) model is created. The GP model provides black-box identification of non-linear dynamic systems using a probabilistic non-parametric modeling technique. Gaussian processes can identify regions of the input space where prediction accuracy is low due to a lack of data or its complexity by offering a greater degree of variation around the anticipated mean. Bayesian optimization is used to determine the MPC hyperparameters that maximize mission efficiency by treating the algorithm’s generalization performance as a sample from a Gaussian process. The tractable posterior distribution generated by the GP enables the most efficient use of data from previous trials, allowing for the best decisions on which parameters to test next. Both situations are numerically simulated using a distributed MPC formulation technique. Apart from numerical simulations, the following performance comparisons are made by employing constraints in different ways: first, between decentralized and centralized approaches via error vs. time plots for individual UAVs, and then between computing efficiency and root mean square error for different prediction horizons. Second, between the proposed method and greedy approach for both centralized and decentralized MPC implementation Full thesis: pdf Centre for Robotics |
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