Baisravan HomChaudhuri

Cooperative Control of Multiple Uninhabited Aerial Vehicles for Monitoring and Fighting Wildfires

Uninhabited aerial vehicles provide numerous advantages in fighting wildland fires that include persistent operation and elimination of humans from performing what can be dull, dangerous, and dirty work. Multiple cooperating uninhabited aerial vehicles can potentially bring about a paradigm shift in the way we fight complex wildland fires. This paper investigates algorithmic development for cooperative control of a number of uninhabited aerial vehicles engaged in fighting a wildland fire. The paper considers two tasks to be performed by a group of uninhabited aerial vehicles: 1) Cooperative tracking of a fire front for accurate situational awareness, and 2) cooperative, autonomous fire fighting using fire suppressant fluid. The scenario considered in this paper makes the following assumptions: information regarding the location of the fire and position of all uninhabited aerial vehicles is made available to each uninhabited aerial vehicle; and each uninhabited aerial vehicle is equipped with unlimited fire suppressant fluid which extinguishes fire in a circle of specified area directly beneath it. This paper formulates these two tasks of fire fighting based upon optimization of respective utility functions, develops a decentralized control method for the cooperative uninhabited aerial vehicles, and analyzes the system for its stability and its ability to carry out the tasks. The proposed strategies have been verified with the help of extensive simulations. Although simplifying assumptions have been made, this preliminary study presents a framework for path planning and cooperative control of multiple uninhabited aerial vehicles engaged in gathering data and actively fighting forest fires.

A fuel economic model predictive control strategy for a group of connected vehicles in urban roads

The advancements in communication, sensing, and computing has enabled the development of connected vehicle systems where improved decision and control strategies are enabled with the aid of information exchange within the vehicular system. In this paper, we consider a connected vehicle system and develop fuel economic control strategies for a group of vehicles in congested urban road conditions. We exploit the Signal Phase and Timing (SPAT) information from the traffic lights and utilize model predictive control with a modified cost to reduce stopping at red lights and improve the fuel economy for a group of vehicles. The simulation results indicate the improvement in group performance for our proposed method.

Market based approach for solving optimal power flow problem in smart grid

Smart grid has generated much attention recently due to its potential in bringing a revolutionary change in the production, distribution, and utilization of power. However, before a smart grid can become fully functional, it requires technological advancements in a number of interdisciplinary domains. Even though smart grid facilitates run-time optimal allocation of power via extensive instrumentation and information accessibility, the process of optimal power allocation becomes challenging due to the massively distributed generation facilities, loads, and due to the intermittency of generation. Optimal power flow problem essentially minimizes the overall cost of power generation while meeting the total load or power demand at the consumer end. In this paper, a Market Based technique has been presented to carry out the optimal power flow in a smart grid. The Market Based Resource Allocation is inspired from the concepts in economic market, where resources are allocated to activities through the process of competitive buying and selling. In the proposed technique, consumers act as potential power buyers and generation units act as the power sellers. The proposed method derives its significance due to its ability in optimizing power flow in a grid in a distributed manner, i.e., from local interactions. This feature provides immense scalability and robustness to uncertainties. In addition, this paper presents the evaluation of the proposed Market Based technique via a simulated scenario of power consumers and producers in a Smart Grid. The IEEE 30-bus system (RTS 79) with six generation units is used to test the proposed method in optimizing the total generation cost, and the results are compared with that obtained from widely used Matpower software.

Fast Model Predictive Control-Based Fuel Efficient Control Strategy for a Group of Connected Vehicles in Urban Road Conditions

In this paper, we develop a fast model predictive control (MPC)-based fuel economic control strategy for a group of connected vehicles in urban road conditions. The proposed control strategy is decentralized in nature, as every vehicle evaluates its own strategy using only neighborhood information. Along with the vehicle-to-vehicle communication, we exploit the signal phase and timing information from traffic lights to develop computationally efficient MPC-based strategies that reduce stopping at red lights and improve the fuel economy for a group of vehicles. The simulation results indicate the improvement in group performance and computational advantages of our proposed method.

Market based allocation of power in smart grid

In recent years, the concept of Smart Grid has generated much attention among the producers and consumers of electric power, the policy makers, as well as the researchers. Smart Grid technology promises to revolutionize the way in which electricity is produced, delivered, and utilized. However, it requires technological advancement in a number of interdisciplinary domains before complete benefits of smart grid can be realized. Significant among them are the technological advances to enable substantial increase in the use of renewable energy sources coupled with a massive increase in energy efficiency in not only generation but also in distribution and utilization. In particular, the usage of renewable energy sources is envisioned to result into a massively distributed power generation and distribution system composed of a large number of generating stations operating on disparate renewable technologies. Optimal allocation of existing energy resources becomes a challenge due to massively distributed nature of generation facilities and consumption sites, and due to uncertainty caused by inherent random fluctuations in generation. In this paper, a Market Based technique has been presented for carrying out the optimal allocation for efficient utilization of the energy produced in a Smart Grid. The Market Based Resource Allocation is a distributed technique inspired by the concepts from the economic market where resources are allocated to the activities through the process of competitive buying and selling. In the proposed technique, the energy consumers act as the potential buyers of the energy and the energy producers act as the sellers of energy. The paper evaluates the proposed Market Based technique via a number of simulated scenarios of energy consumers and producers in a Smart Grid. The proposed technique optimizes the energy production cost and the transmission loss of electricity as these costs are reflected in the bidding and asking prices of the consumers and the sellers respectively.

Optimal fireline generation for wildfire fighting in uncertain and heterogeneous environment

Fire is a natural component of many ecosystems but wildland fires often do pose serious threats to public safety, properties and natural resources. Forest fire acts as a dominant factor in reshaping of terrain and change of the ecosystem of a particular area. The total damage due to wildland fire shows an increasing trend over the past decade. Forest Fire Decision Support Systems (FFDSS) have been developed for the last thirty years all over the world that supplies valuable information on forest fire detection, fire behavior and other aspects of forest fires but lacks in developing intelligent fire suppression strategies. In this paper, an effort has been made to generate intelligent fire suppression strategies with efficient resource allocation using the Genetic Algorithm based optimization tool in a heterogeneous and uncertain scenario. The goal of this research is to perform intelligent resource allocation along with the generation of optimal firelines that minimizes the total burned area due to wildland fire. The solutions generated at each generations of the Genetic Algorithm (GA) are used to build the firelines in a heterogeneous terrain where advanced forest fire propagation model is used to evaluate the fitness values of each generated solutions. The optimal firelines thus obtained through the Simulation-Optimization technique minimizes the total damage due to wildland fire and eliminates the chance of any fire escape i.e., firefront reaching the fireline positions before they are built. Such techniques integrated with the existing FFDSS hold promise in effectively controlling forest fires.

A Market Based Distributed Optimization for Power Allocation in Smart Grid

The potential of Smart Grid in bringing about a revolutionary change in the production, distribution, and utilization of power has generated much attention among producers and consumers of electric power, policy makers as well as researchers. However, before a smart grid can become fully functional, it requires technological advancements in a number of interdisciplinary domains. In particular, the usage of renewable energy sources is envisioned to result in a massively distributed power generation and distribution system composed of a large number of generating stations operating on disparate renewable technologies. Even though smart grid facilitates run-time optimal allocation of power via extensive instrumentation and information accessibility, the process of optimal power routing becomes challenging due to the massively distributed nature of generation facilities and consumption sites, and due to uncertainty caused by inherent random fluctuations in generation. In this paper, a Market Based technique has been presented for carrying out the optimal allocation for efficient utilization of the energy produced in a Smart Grid. The Market Based Resource Allocation is inspired by concepts from the economic market, where resources are allocated to activities through the process of competitive buying and selling. In the proposed technique, the energy consumers act as the potential energy buyers and the energy producers act as the energy sellers. The paper evaluates the proposed Market Based technique via a number of simulated scenarios of energy consumers and producers in a Smart Grid. The proposed technique optimizes the transmission loss of electric power distribution. Simulation results of the proposed approach are presented at the end of this paper.Copyright

Genetic Algorithm based Simulation-Optimization for Fighting Wildfires

Wildfire is one of the most significant disturbances responsible for reshaping the terrain and changing the ecosystem of a particular region. Its detrimental effects on environment as well as human...

Fuel Efficient Control Strategies for Connected Hybrid Electric Vehicles in Urban Roads

This paper presents a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed in this paper where the higher level controller is considered to be a part of the transportation infrastructure while the lower level controllers are considered to be present in every HEV. The higher level controller uses model predictive control strategy to evaluate the energy efficient velocity profiles for every vehicle for a given horizon. Each lower level controller then tracks its velocity profile (obtained from the higher level controller) in a fuel efficient fashion using equivalent consumption minimization strategy (ECMS). In this paper, the vehicles are modeled in Autonomie software and the simulation results provided in the paper shows the effectiveness of our proposed control architecture.Copyright

Fuzzy counter Ant Algorithm for Maze Problem

This effort explores the effectiveness of adding a layer of fuzzy logic to a group of swarming multi agent robots for exploration and exploitation of an unknown obstacle rich environment represented by a 2D maze problem. The generalized maze problem has been considered as an interesting test bed by various researchers in AI and neural networks. Using a cooperative multi agent robot system reduces the convergence time considerably as compared to a single agent. For the multi agent case, a robust and effective decision making technique is required that prevents a robot from moving to a region already explored by some other robot. In this paper, we present a counter ant algorithm (modified ant colony optimization algorithm) based on a fuzzy inference system which enables multiple agents in path planning along the unexplored regions of a maze in order to find a solution rapidly. Simulation results demonstrate the effectiveness of this approach.