Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization

The accurate estimation of the noise covariance matrix (NCM) in a dynamic system is critical for state estimation and control, as it has a major influence in their optimality. Although a large number of NCM estimation methods have been developed, most of them assume the noises to be white. However, in many real-world applications, the noises are colored (e.g., they exhibit temporal autocorrelations), resulting in suboptimal solutions. Here, we introduce a novel brain-inspired algorithm that accurately and adaptively estimates the NCM for dynamic systems subjected to colored noise. Particularly, we extend the Dynamic Expectation Maximization algorithm to perform both online noise covariance and state estimation by optimizing the free energy objective. We mathematically prove that our NCM estimator converges to the global optimum of this free energy objective. Using randomized numerical simulations, we show that our estimator outperforms nine baseline methods with minimal noise covariance estimation error under colored noise conditions. Notably, we show that our method outperforms the best baseline (Variational Bayes) in joint noise and state estimation for high colored noise. We foresee that the accuracy and the adaptive nature of our estimator make it suitable for online estimation in real-world applications.Comment: 62nd IEEE Conference on Decision and Contro

Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise

The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise. However, the active inference community has been challenged with the critical task of manually tuning the noise smoothness parameter. To solve this problem, we introduce a novel online noise smoothness estimator based on the idea of free energy principle. We mathematically show that our estimator can converge to the free energy optimum during smoothness estimation. Using this formulation, we introduce a joint state and noise smoothness observer design called DEMs. Through rigorous simulations, we show that DEMs outperforms state-of-the-art state observers with least state estimation error. Finally, we provide a proof of concept for DEMs by applying it on a real life robotics problem - state estimation of a quadrotor hovering in wind, demonstrating its practical use.Comment: 6 pages, 8 figure

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad

Informative Path Planning for Search and Rescue using a UAV

Target search in an obstacle filled environment is a practically relevant challenge in robotics that has a huge impact in the society. The wide range of applications include searching for victims in a search and rescue operation, detecting weeds in precision agriculture, patrolling borders for military and navy, automated census of endangered species in a forest etc. An efficient target search algorithm provides a data acquisition platform with least human intervention, thus improving the quality of life of humans. This thesis aims at introducing a general path planning algorithm for UAVs flying at different heights in an obstacle filled environment, searching for targets in the ground field. An adaptive informative path planning (IPP) algorithm is introduced that simultaneously trade off between area coverage, field of view, height dependent sensor performance and obstacle avoidance. It plans under uncertainties in the sensor measurements at varying heights, and is robust against wrong target detections. It generates an optimal fixed horizon plan in the form of a 3D minimum-snap trajectory that maximizes the information gain in minimum flight time by providing maximum area coverage, without any collision with the obstacles. The resulting planner is modular in terms of the mapping strategy, environment complexity, different target, changes in the sensor model and optimizer used. The planner is tested against varying environmental complexities, demonstrating its capability in handling a wide range of possible environments. The planner outperforms other planners like non-adaptive IPP planner, coverage planner and random sampling planner, by demonstrating the fastest decrease in map error while flying for a fixed time budget. A proof of concept for the algorithm is provided through real experiments by running the algorithm on a UAV flying inside a lab environment, searching for targets lying on the ground. All the targets were successfully found and mapped by the algorithm, demonstrating its applicability in a real-life target search problem.Mechanical Engineering | Biomechanical Design - BioRobotic