Publications

Preprints

1. Optimal Sensor and Actuator Selection for Factored Markov Decision Processes: Complexity, Approximability and Algorithms Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram Pre-print (under review)

Conference/Workshop Articles

1. Submodular Information Selection for Hypothesis Testing with Misclassification Penalties Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram 6th Annual Learning for Decision and Control Conference (L4DC), Oxford UK. [Abs] [PDF]

   We consider the problem of selecting an optimal subset of information sources, where the goal is to identify the true state of the world from a finite set of hypotheses, based on finite observation samples from the sources. In order to characterize the learning performance, we propose a misclassification penalty framework, which aims to reduce the likelihood of acting on erroneous predictions. In a centralized Bayesian learning setting, we study the problem of selecting a minimum cost information set, while ensuring that the maximum penalty of misclassifying the true state is bounded. We prove that this combinatorial optimization problem is submodular, and establish high-probability guarantees for near-optimal performance of a standard greedy algorithm, with the associated finite sample convergence rates for the Bayesian beliefs. Next, we consider the dual problem, where one seeks to select a set of information sources under a limited budget, while trying to minimize the maximum penalty for misclassifying the true state, under finite observation samples from the sources. We prove that this problem is submodular and establish high-probability near-optimal guarantees for a standard greedy algorithm. Finally, we validate our theoretical results through some numerical simulations, and show that the greedy algorithm works well in practice.

1. On the Complexity and Approximability of Optimal Sensor Selection for Mixed-Observable Markov Decision Processes Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram 2023 American Control Conference (ACC), San Diego CA.
   ACC Student Travel Grant Award [Abs] [PDF]

   Mixed-Observable Markov Decision Processes (MOMDPs) are used to model systems where the state space can be decomposed as a product space of a set of state variables, and the controlling agent is able to measure only a subset of those state variables. In this paper, we consider the setting where we have a set of potential sensors to select for the MOMDP, where each sensor measures a certain state variable and has a selection cost. We formulate the problem of selecting an optimal set of sensors for MOMDPs (subject to certain budget constraints) to maximize the expected infinite-horizon reward of the agent and show that this sensor placement problem is NP-Hard, even when one has access to an oracle that can compute the optimal policy for any given instance. We then study a greedy algorithm for approximate optimization and show that there exist instances of the MOMDP sensor selection problem where the greedy algorithm can perform arbitrarily poorly. Finally, we provide some empirical results of greedy sensor selection over randomly generated MOMDP instances and show that, in practice, the greedy algorithm provides near-optimal solutions for many cases, despite the fact that one cannot provide general theoretical guarantees for its performance. In total, our work establishes fundamental complexity results for the problem of optimal sensor selection (at design-time) for MOMDPs.

1. Deriving Spatial Policies for Overtaking Maneuvers with Autonomous Vehicles Jayanth Bhargav, Johannes Betz, Hongrui Zheng, Rahul Mangharam 2022 IEEE 14th International Conference on COMmunication Systems & NETworkS (COMSNETS) - 8th Workshop on Intelligent Transportation Systems
   ACM Travel Grant Award [Abs] [PDF]

   Planning an accurate and safe trajectory is a crucial element in autonomous driving. To execute complex driving maneuvers like overtaking, motion planning requires an enhanced decision-making algorithm that decides the when, where and how of the overtaking maneuver. This paper proposes an algorithm that increases the likelihood of a safe overtaking maneuver by learning spatial information. Here, spatial information refers to the track portion/curve and the position of the ego vehicle with reference to that. The technique is applied to an autonomous racing setup where vehicles have to detect and operate at the limits of dynamic handling. To learn the spatial information, offline experiments of a 2-player race are conducted to generate probability distributions of overtaking maneuvers conditioned on speed and relative-position of the ego vehicle with respect to the opponent. Furthermore, a Switched Model Predictive Contouring Controller (SMPCC) is proposed for incorporating the policy learning algorithm into the path planning and control setup. Extensive simulations show that the proposed algorithm is able to achieve an increased number of overtakes at different track portions on known and unknown race tracks.

1. Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars Jayanth Bhargav, Johannes Betz, Hongrui Zheng, Rahul Mangharam 2021 IEEE International Conference on Robotics and Automation (ICRA 2021)- 1st Workshop on Opportunities and Challenges in Autonomous Racing [Abs] [PDF]

   The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial parts in autonomous racing is path planning and decision making for an overtaking maneuver with a dynamic opponent vehicle. In this paper we present the evaluation of a track based offline policy learning approach for autonomous racing. We define specific track portions and conduct offline experiments to evaluate the probability of an overtaking maneuver based on speed and position of the ego vehicle. Based on these experiments we can define overtaking probability distributions for each of the track portions. Further, we propose a switching MPCC controller setup for incorporating the learnt policies to achieve a higher rate of overtaking maneuvers. By exhaustive simulations, we show that our proposed algorithm is able to increase the number of overtakes at different track portions.

1. IoT Based Wireless Sensor Network (WSN) for Condition Monitoring of Low Power Rooftop PV Panels Sushmita Sarkar, K Uma Rao, Jayanth Bhargav, Shrikesh Sheshaprasad, Anirudh Sharma CA 2019 IEEE 4th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON) [Abs] [PDF]

   Rooftop PV systems are now becoming ubiquitous with the reduction in costs of PV panels and related technologies. Developing countries like India have undertaken Green Energy Transition to ensure sustainability and energy self-sufficiency. The increase in consumer participation in view of the nation's goal necessitates technology advancements in condition monitoring of PV systems. As of today, state of art and robust monitoring systems are available and deployed for large solar farms and have proved to increase the operational efficiency of those systems, whereas there are no standard monitoring systems for small-scale installations. This paper presents a survey on Operation and Maintenance (O&M) of PV systems and an IoT based Wireless Sensor Network for PV panel monitoring.

1. Signature Analysis of Electrical Faults in Standalone PV Systems with Storage Sushmita Sarkar, K Uma Rao, Jayanth Bhargav, Shrikesh Sheshaprasad, Anirudh Sharma CA 2019 IEEE 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE) [Abs] [PDF]

   Stand-alone PV systems are increasingly becoming popular as a viable alternative to utility power supply, especially in remote corners, where accessibility is a problem to deploy conventional distribution systems. It is also becoming a choice of conscious consumers who wish to contribute to environment protection through use of green energy. In this context, it is vital to have a fault diagnosis system in place to identify various faults in the PV system. This paper presents a signature analysis of various electrical faults in a stand-alone PV system, with battery storage. The focus of the work is to minimize the sensors to be deployed for detection and classification of the faults.

1. Parametrics for the choice of Embedded Systems and Control Algorithms for Application Specific Robot Designs Jayanth Bhargav, Shourya TR, Arti Anantharaman, C Chinmay, K Uma Rao, Girish Rao Salanke 2018 IEEE 2nd International Conference on Inventive Systems and Control (ICISC) [Abs] [PDF]

   Micro controller contains one or more processor cores along with peripheral devices and memory. Micro-controllers find extensive use in embedded system applications such as automobile engine control systems, bio-medical devices, electronic appliances, etc. They are specific to a particular task, are small and compact in size, and consume less power; all of these features have resulted in the ubiquitous deployment of micro-controllers in various devices. However, it must be noted that a microcontroller is a Control Circuit, and not a Driving Circuit. This statement is supported by the fact that the output current of a microcontroller is too small to drive a motor connected to it directly; in fact, this is an unpropitious experiment and can damage the microcontroller. The microcontroller controls the working of actuators connected to it by following the logic coded onto it, and in this sense, forms the very brain of the embedded system. But in order to drive the motor, we need a type of switch (MOSFET, Relay, etc.) which can take the small output current of the microcontroller and control the power source to the motor. Essentially, a microcontroller accepts real-time inputs and generates an output control signal as per the rules and predefined logical deductions, in the prescribed algorithm. This paper discusses the criterion one must consider for the choice of an embedded system from the plethora of systems available in the market and the choice of control technique for different robotic applications.

Journal Articles

1. Novel Modular LS-SVM Based Regression Model for Prediction of Solar Power (2019) Sushmita Sarkar, K Uma Rao, Prema V., Jayanth Bhargav, Shourya T.R. International Journal of Engineering Sciences and Management-A Multidisciplinary Journal of VTU, 01, 01-07, [Abs] [PDF]

   PV power penetration is increasing rapidly. Hence, its impact on the grid operation becomes a major concern for the utilities. Predictive models of renewable energy are an essential requirement to analyze this impact. Design of an accurate, effective and powerful model for forecasting is a highly challenging task in weather dependent renewable energy sources. This paper proposes a novel modular Least Square SVM (LS-SVM) based Regression model for solar-power forecast. The solar power is calculated using predicted value of solar irradiance. This method highly reduces the computation complexity and time. The proposed model uses a Time-Series decoupling strategy where instead oftreating the historical data as a single time series, the data is split into multiple time series. Each time series carries the historical data of solar irradiance at a particular time of the day during the period under consideration. The model has shown promising results with a MAPE of 3.5% for a day ahead prediction

Book Chapters

1. User Interactive GUI for Integrated Design of PV Systems Sushmita Sarkar, K Uma Rao, Prema V, Anirudh Sharma C A, Jayanth Bhargav, Shrikesh Sheshaprasad Intelligent Renewable Energy Systems, Wiley Online Library
   Print ISBN:9781119786276 | Online ISBN:9781119786306 | DOI:10.1002/9781119786306 01, 01-07, [Abs] [PDF]

   All over the world, penetration of solar PV in electrical grids has seen a steep increase in the last decade. This includes both low power solar roof top installations and large-scale solar farms. Consciousness about climate change and encouraging government policies especially in developing countries have made investment in solar PV farms an attractive option for private investors. Further large amount of infertile land in tropical countries has also boosted installation of PV farms in such spaces. Two important decisions for any PV system are the space required for panel installation and the cost incurred. The cost depends on the type of installation. Popular models are standalone PV micro grid, large farms and that are grid-tied and those that are not tied to the grid. Based on the model, the required power electronics has to be designed. Currently, there are no open-source design tool to help the consumer/investor to design the specifications and ratings of different components. In this paper, a unique and a novel GUI is presented to design the entire solar PV systems. Further break-up of cost components, standards and configuration for inverters are also presented in the GUI.