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Atilla Dogan - One of the best experts on this subject based on the ideXlab platform.

  • PTEM Based Moving Obstacle Detection and Avoidance for an Unmanned Ground Vehicle
    Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; M, 2017
    Co-Authors: Gangadhar Rajashekaraiah, Hakki Erhan Sevil, Atilla Dogan
    Abstract:

    This study presents the development and implementation of an autonomous obstacle avoidance algorithm for an UGV (Unmanned Ground Vehicle). This research improves the prior work by enhancing the obstacle avoidance capability to handle moving obstacles as well as stationary obstacles. A mathematical representation of the area of operation with obstacles is formulated by PTEM (Probabilistic Threat Exposure Map). The PTEM quantifies the risk in being at a position in an area with different types of obstacles. A LRF (Laser Range Finder) sensor is mounted on the UGV for obstacle data in the area that is used to construct the PTEM. A guidance algorithm processes the PTEM and generates the speed and heading commands to steer the UGV to assigned waypoints while avoiding obstacles. The main contribution of this research is to improve the PTEM framework by updating it continuously as new LRF readings are obtained, on the contrary to the prior work with fixed PTEM. The improved PTEM construction algorithm is implemented in a MATLAB/Simulink simulation environment that includes models of the UGV, LRF, all the sensors and actuators needed for the control of the UGV. The performance of the algorithm is also demonstrated in real time experiments with an actual UGV system.

  • Real-Time Obstacle Avoidance and Waypoint Navigation of an Unmanned Ground Vehicle
    Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control, 2012
    Co-Authors: Hakki Erhan Sevil, Atilla Dogan, Pranav Desai, Brian Huff
    Abstract:

    Real-time obstacle avoidance and navigation are key fields of research in the area of autonomous vehicles. The primary requirements of autonomy are to detect or sense changes and react to them without human intervention in a safe and efficient manner. The objective of this research is to develop autonomous way-point navigation and obstacle avoidance capabilities for an unmanned ground vehicle (UGV). This research consists of developing and implementing an environment mapping system capable of detecting and localizing potential obstacles using real-time sensor data. The real-time obstacle mapping system developed in this work automatically generates the Probabilistic Threat Exposure Map (PTEM). The PTEM construction algorithm successfully constructs a probabilistic obstacle map both in simulation and real-time. Autonomous waypoint navigation is also achieved for both simulation and real-time platforms. These activities are a part of a larger effort to establish a theoretical foundation and real-time implementation of autonomous and cooperative multi-UxV guidance solutions in adversarial environments.Copyright © 2012 by ASME

  • Cooperative target pursuit by multiple UAVs in an adversarial environment
    Robotics and Autonomous Systems, 2011
    Co-Authors: Ugur Zengin, Atilla Dogan
    Abstract:

    This paper presents the development of a cooperation strategy for multiple UAVs to pursue a target moving in an adversarial environment where Threat Exposure should be minimized, and obstacles and restricted areas should be avoided. A probabilistic approach is used to model the adversarial environment. A cost function is defined to quantify placement of UAVs around the target in formation in terms of Threat Exposure level and distance to the target. The cost function is used to develop a cooperation strategy for a team of UAVs to follow the target such that the total Threat Exposure of the team and the average distance to the target throughout the pursuit are minimized according to the weighting coefficients specified. The cooperation strategy has the feature of collision avoidance as well as data-fusion-based estimation of the target trajectory based on noisy measurements. Simulation results have demonstrated that the cooperation reduces the risk of losing the target during the pursuit while avoiding obstacles and restricted areas. Further, the UAVs guided by the cooperation strategy can follow the target closer without increasing the total Threat Exposure level as compared to cases where the UAVs pursue the target without cooperation.

  • Construction of an obstacle map and its realtime implementation on an Unmanned Ground Vehicle
    2011 IEEE Conference on Technologies for Practical Robot Applications, 2011
    Co-Authors: Pranav Desai, Atilla Dogan, Hakki Erhan Sevil, Brian Huff
    Abstract:

    This paper presents the development of an obstacle mapping system based on the concept of a Probabilistic Threat Exposure Map (PTEM). The paper also discusses the realtime embedded implementation of this obstacle mapping system on a small Unmanned Ground Vehicle (UGV) to support realtime obstacle avoidance. These activities are a part of a larger effort to establish a theoretical foundation for autonomous and cooperative multi-UxV guidance solutions in adversarial environments.

  • Real-Time Target Tracking for Autonomous UAVs in Adversarial Environments: A Gradient Search Algorithm
    IEEE Transactions on Robotics, 2007
    Co-Authors: Ugur Zengin, Atilla Dogan
    Abstract:

    This paper presents a rule-based intelligent guidance strategy for autonomous pursuit of mobile targets by unmanned aerial vehicles (UAVs) in an area with Threats, obstacles, and restricted regions. The probabilistic Threat Exposure map (PTEM) is used as the mathematical formulation of the area of operation for the guidance strategy to make intelligent decisions based on a set of defined rules. The rules are developed for three objectives in the order of priority as: 1) avoid obstacles/restricted regions; 2) maintain the target proximity; 3) minimize UAV Threat Exposure level. A least-square estimation and kinematic relations are used to estimate/predict the target states based on noisy position measurements. The work presented herein addresses the same problem as in a previous work by the authors, and aims at improving the computational efficiency without compromising the performance. Simulation results of several pursuit scenarios demonstrate the full capabilities of the strategy and the improvement over the previous work

Ugur Zengin - One of the best experts on this subject based on the ideXlab platform.

  • Cooperative target pursuit by multiple UAVs in an adversarial environment
    Robotics and Autonomous Systems, 2011
    Co-Authors: Ugur Zengin, Atilla Dogan
    Abstract:

    This paper presents the development of a cooperation strategy for multiple UAVs to pursue a target moving in an adversarial environment where Threat Exposure should be minimized, and obstacles and restricted areas should be avoided. A probabilistic approach is used to model the adversarial environment. A cost function is defined to quantify placement of UAVs around the target in formation in terms of Threat Exposure level and distance to the target. The cost function is used to develop a cooperation strategy for a team of UAVs to follow the target such that the total Threat Exposure of the team and the average distance to the target throughout the pursuit are minimized according to the weighting coefficients specified. The cooperation strategy has the feature of collision avoidance as well as data-fusion-based estimation of the target trajectory based on noisy measurements. Simulation results have demonstrated that the cooperation reduces the risk of losing the target during the pursuit while avoiding obstacles and restricted areas. Further, the UAVs guided by the cooperation strategy can follow the target closer without increasing the total Threat Exposure level as compared to cases where the UAVs pursue the target without cooperation.

  • Real-Time Target Tracking for Autonomous UAVs in Adversarial Environments: A Gradient Search Algorithm
    IEEE Transactions on Robotics, 2007
    Co-Authors: Ugur Zengin, Atilla Dogan
    Abstract:

    This paper presents a rule-based intelligent guidance strategy for autonomous pursuit of mobile targets by unmanned aerial vehicles (UAVs) in an area with Threats, obstacles, and restricted regions. The probabilistic Threat Exposure map (PTEM) is used as the mathematical formulation of the area of operation for the guidance strategy to make intelligent decisions based on a set of defined rules. The rules are developed for three objectives in the order of priority as: 1) avoid obstacles/restricted regions; 2) maintain the target proximity; 3) minimize UAV Threat Exposure level. A least-square estimation and kinematic relations are used to estimate/predict the target states based on noisy position measurements. The work presented herein addresses the same problem as in a previous work by the authors, and aims at improving the computational efficiency without compromising the performance. Simulation results of several pursuit scenarios demonstrate the full capabilities of the strategy and the improvement over the previous work

  • CDC - Real-Time Target Tracking for Autonomous UAVs in Adversarial Environments: A Gradient Search Algorithm
    Proceedings of the 45th IEEE Conference on Decision and Control, 2006
    Co-Authors: Ugur Zengin, Atilla Dogan
    Abstract:

    In this paper, we present a real-time target tracking strategy for autonomous UAV operations in adversarial environments. The strategy generates the commanded heading and speed within the dynamic constraints of the UAV (i) to ensure that the UAV does not enter restricted regions (ii) to maintain the pursuit of the target (iii) to minimize the Threat Exposure level of the UAV. Probabilistic Threat Exposure map (PTEM) of the area of operation is generated by using a set of Gaussian probability distribution functions. PTEM defines various types of Threats in a single framework and gives the risk of Exposure to these sources of Threat as a function of position. A steepest gradient search approach is utilized to determine in which direction the UAV should move to minimize the Threat Exposure or maximize the likelihood of avoiding a restricted region. Simulation results show the capability of the strategy to track a maneuvering target in an adversarial environment.

  • Cooperative target tracking for autonomous UAVs in an adversarial environment
    AIAA Guidance Navigation and Control Conference and Exhibit, 2006
    Co-Authors: Ugur Zengin, Atilla Dogan
    Abstract:

    The objective in this paper is to pursue a target by multiple UAVs when the target moves in an area with multiple Threats, obstacles and restricted areas. In the pursuit there are two conflicting objectives: (i) all UAVs stay close to the target and (ii) all UAVs avoid restricted-areas/obstacles and total Threat Exposure level is minimized. This paper develops a formulation to quantify the trade-off between these two objectives in a cooperative manner by providing the UAVs with the proximity circles that quantify how close and in what formation the UAVs should follow the target. These proximity circles are calculated by using a gradient search approach, to minimize a cost function at every update instant that represents the total Threat Exposure of the UAVs and their total distance to the target. Each UAV computes its proximity circle in a decentralized manner by estimating the other UAV’s proximity circle. The cooperation reduces the overall Threat Exposure and the distance of the team to the target by dynamically changing the proximity circles in comparison to the case where each UAV tries to track the target without considering the fact that there are other UAVs performing the very same task.

  • unmanned aerial vehicle dynamic target pursuit by using probabilistic Threat Exposure map
    Journal of Guidance Control and Dynamics, 2006
    Co-Authors: Atilla Dogan, Ugur Zengin
    Abstract:

    A strategy is presented for an unmanned aerial vehicle (UAV) to follow a moving target in an area the probabilistic Threat Exposure map of which is assumed to be known based on a priori data. A probabilistic Threat Exposure map is defined to be the risk of Exposure to multiple sources of Threat as a function of position. The strategy generates speed and heading angle commands within the dynamic constraints of the UAV. There are three main objectives in order of priority: 1) All of the restricted areas are avoided. 2) The UAV stays within the proximity of the target by a prespecified distance. 3) The total Threat Exposure level is minimized. During the pursuit, the heading and speed of the target and their time variations are not directly measured but are estimated from the measurements of the target positions. If, for any reason, the sensor can no longer measure the current position of the target, the strategy starts using the predicted target states based on the past measurements to guide the UAV toward the proximity of the target until the UAV detects the target again. I. Introduction I N this paper, a target following strategy is introduced for an unmanned aerial vehicle (UAV) flying through an area of multiple sources of Threat that are modeled as a probabilistic Threat Exposure map (PTEM). The PTEM is a map that indicates the Threat level of an area due to different types of static Threat sources using probability density functions. Recently, there has been an increasing interest in probabilistic approaches in mission planning for the UAVs. This is because the probabilistic approaches are inherently very suitable to handle the uncertainty in the information, such as the locations of the Threats. There are several papers in the literature using various probabilistic approaches to deal with the path-planning problem of the UAV applications based on the probabilistic map of the area of operation. 1,2 In Refs. 1 and 2, various path-planning strategies are proposed to minimize the level of Threat Exposure while flying to a stationary target through an area of multiple Threats. This risk of Exposure to a source of Threat is a function of position, defined to be the probability of becoming disabled by the source of Threat at a given position. The probability is assumed to have a Gaussian distribution over the area of operation. The probabilistic Threat Exposure map is constructed from the probability distribution functions of all of the sources of Threat in the area. In Ref. 3, a graph-based probabilistic approach is developed to use the probabilistic map of the area. Unlike the Voronoi graph-based approaches, the nodes and links of the graph are based directly on the probabilistic map. The region of operation is divided into cells whose occupancy value is determined based on the sensor readings. By the application of the conditional probability of occupancy using Bayes rule and the Bellman‐Ford algorithm, the shortest path is found. Because the path-planning strategies rely on probabilistic maps, the construction and online update of the maps are very crucial. In Ref. 4, a probabilistic map of an area with multiple distinguishable moving obstacles is built by using Bayesian estimation, and

Raghvendra V. Cowlagi - One of the best experts on this subject based on the ideXlab platform.

  • Handbook of Dynamic Data Driven Applications Systems - Dynamic Sensor-Actor Interactions for Path-Planning in a Threat Field
    Handbook of Dynamic Data Driven Applications Systems, 2018
    Co-Authors: Benjamin S. Cooper, Raghvendra V. Cowlagi
    Abstract:

    We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a Threat field with minimum Threat Exposure. The Threat field is an unknown, time-invariant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The Threat field is estimated by a network of mobile sensors that can measure the Threat field pointwise at their locations. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the Threat Exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves such that the actor’s risk is minimized. Future applications of this problem setup include, for example, delivery (by an actor) of emergency supplies to a remote location that lies within/beyond a region afflicted by wildfire or atmospheric contaminants (the Threat field). We formulate this problem on a grid defined on the actor’s workspace, which defines a topological graph \(\mathcal {G}\). The Threat field is assumed to be finitely parameterized by coefficients of spatial basis functions. Least squares estimates of these parameters are constructed using measurements from the sensors and the actor. Whereas edge transitions in the graph \(\mathcal {G}\) are deterministic, the transition costs depend on the Threat field estimates, and are deterministic but unknown. The actor and the sensors interact iteratively. At each iteration, Dijkstra’s algorithm is used to determine a minimum risk path in the graph \(\mathcal {G}\) for the actor. Next, a set of grid points “near” this path are identified as points of interest. Finally, the next set of sensor locations is determined to maximize the confidence of Threat field estimates on these points of interest, the Threat field estimate is accordingly updated, and the iteration repeats. We explore the effect of initial sensor placement on the convergence of the iterative planner-sensor as well as discuss convergence properties with respect to the relative number of parameters and sensors available.

  • Interactive Planning and Sensing in Uncertain Environments with Task-Driven Sensor Placement
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Benjamin Cooper, Raghvendra V. Cowlagi
    Abstract:

    We consider the problem of planning the path of a vehicle, called the actor, to traverse a Threat field with minimum Threat Exposure. The Threat field is an unknown, time-invariant, and strictly positive scalar field defined on a compact 2D spatial domain estimated by a network of mobile sensors. The Threat field is assumed to be finitely parametrized by coefficients of spatial basis functions. Estimates of these parameters are constructed using measurements from the sensors. A novelty of this problem setup is that the actor can request the sensors to reposition themselves. The actor and the sensors interact iteratively. At each iteration, Dijkstra's algorithm is used to determine the actor's path with minimum expected Threat Exposure. Next, a set of grid points “near” this path are identified. Finally, the next set of sensor locations is determined to maximize the confidence of Threat field estimates on these grid points, the Threat field estimate is accordingly updated, and the iteration repeats. We study the convergence of these iterations and the actor's performance under this interactive sensor placement method. We compare the proposed method to a typical information-driven sensor placement approach. We demonstrate that in comparison, not only is the proposed method faster by at least two orders of magnitude, but in certain situations also results in improved actor performance.

  • ACC - Interactive Planning and Sensing in Uncertain Environments with Task-Driven Sensor Placement
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Benjamin S. Cooper, Raghvendra V. Cowlagi
    Abstract:

    We consider the problem of planning the path of a vehicle, called the actor, to traverse a Threat field with minimum Threat Exposure. The Threat field is an unknown, time-invariant, and strictly positive scalar field defined on a compact 2D spatial domain estimated by a network of mobile sensors. The Threat field is assumed to be finitely parametrized by coefficients of spatial basis functions. Estimates of these parameters are constructed using measurements from the sensors. A novelty of this problem setup is that the actor can request the sensors to reposition themselves. The actor and the sensors interact iteratively. At each iteration, Dijkstra's algorithm is used to determine the actor's path with minimum expected Threat Exposure. Next, a set of grid points “near” this path are identified. Finally, the next set of sensor locations is determined to maximize the confidence of Threat field estimates on these grid points, the Threat field estimate is accordingly updated, and the iteration repeats. We study the convergence of these iterations and the actor's performance under this interactive sensor placement method. We compare the proposed method to a typical information-driven sensor placement approach. We demonstrate that in comparison, not only is the proposed method faster by at least two orders of magnitude, but in certain situations also results in improved actor performance.

Ahmed Ghanmi - One of the best experts on this subject based on the ideXlab platform.

  • A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment
    IEEE Congress on Evolutionary Computation, 2010
    Co-Authors: Jean Berger, Abdeslem Boukhtouta, Khaled Jabeur, Adel Guitouni, Ahmed Ghanmi
    Abstract:

    Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and Threat Exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.

  • IEEE Congress on Evolutionary Computation - A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment
    IEEE Congress on Evolutionary Computation, 2010
    Co-Authors: Jean Berger, Abdeslem Boukhtouta, Khaled Jabeur, Adel Guitouni, Ahmed Ghanmi
    Abstract:

    Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and Threat Exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.

Jean Berger - One of the best experts on this subject based on the ideXlab platform.

  • A new mixed-integer linear programming model for rescue path planning in uncertain adversarial environment
    Computers & Operations Research, 2012
    Co-Authors: Jean Berger, Abdeslem Boukhtouta, Abdelhamid Benmoussa, Ossama Kettani
    Abstract:

    Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far rely on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model is proposed to solve the discrete rescue path planning problem for a single agent navigating in uncertain adversarial environment. It relies on a novel and simplified mathematical mixed-integer linear programming formulation aimed at minimizing traveled distance and Threat Exposure. Exploiting a user-defined survivability function approximation and survivability threshold, the approximate model allows constructing a solution providing an adjustable optimality gap interval on the optimal solution. Experimental results show the value of the proposed approach in computing near optimal solutions reasonably fast for various problem instances.

  • A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment
    IEEE Congress on Evolutionary Computation, 2010
    Co-Authors: Jean Berger, Abdeslem Boukhtouta, Khaled Jabeur, Adel Guitouni, Ahmed Ghanmi
    Abstract:

    Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and Threat Exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.

  • IEEE Congress on Evolutionary Computation - A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment
    IEEE Congress on Evolutionary Computation, 2010
    Co-Authors: Jean Berger, Abdeslem Boukhtouta, Khaled Jabeur, Adel Guitouni, Ahmed Ghanmi
    Abstract:

    Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and Threat Exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.