Animesh Sahu received his MS Dual Degree in Electronics and Communication Engineering (ECE). His research work was supervised by Dr. Harikumar. Here’s a summary of his research work on Model predictive control based algorithm for multi-target tracking using a swarm of fixed wing UAVs:
Unmanned 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, including surveillance, modeling, and environmental monitoring. One critical duty is target tracking.
My research proposes a model predictive control (MPC) algorithm for tracking numerous targets utilizing a swarm of unmanned aerial vehicles (UAVs). Multi target tracking through UAVs has numerous diculties, owing to the limited communication range, the dynamic and unpredictable environment, and the fluctuating communication network. MPC has been shown to be an efficient method for process control, tracking, and path planning, 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 fixed-wing aircraft category having ight 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 rst scenario. In the second situation, the number of UAVs are 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 eld of view (FOV) of any of the UAV’s cameras. To relate the hyperparameters utilized in MPC to mission eciency, 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 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: rst, 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 proposed method and greedy approach for both centralized and decentralized MPC implementation.