Viability-based computation of spatially constrained minimum time trajectories for an autonomous underwater vehicle: implementation and experiments.

A viability algorithm is developed to compute the constrained minimum time function for general dynamical systems. The algorithm is instantiated for a specific dynamics(Dubin’s vehicle forced by a flow field) in order to numerically solve the minimum time problem. With the specific dynamics considered, the framework of hybrid systems enables us to solve the problem efficiently. The algorithm is implemented in C using epigraphical techniques to reduce the dimension of the problem. The feasibility of this optimal trajectory algorithm is tested in an experiment with a Light Autonomous Underwater Vehicle (LAUV) system. The hydrodynamics of the LAUV are analyzed in order to develop a low-dimension vehicle model. Deployment results from experiments performed in the Sacramento River in California are presented, which show good performance of the algorithm.trajectories; underwater vehicle; viability algorithm; hybrid systems; implementation;

Line-of-Sight Path Following for Dubins Paths with Adaptive Sideslip Compensation of Drift Forces

This is the author’s final, accepted and refereed manuscript to the article.We present a nonlinear adaptive path-following controller that compensates for drift forces through vehicle sideslip. Vehicle sideslip arises during path following when the vehicle is subject to drift forces caused by ocean currents, wind and waves. The proposed algorithm is motivated by a lineof-sight (LOS) guidance principle used by ancient navigators, which is here extended to path following of Dubins paths. The unknown sideslip angle is treated as a constant parameter, which is estimated using an adaptation law. The equilibrium points of the cross-track and parameter estimation errors are proven to be uniformly semiglobally exponentially stable (USGES). This guarantees that the estimated sideslip angle converges to its true value exponentially. The adaptive control law is in fact an integral LOS controller for path following since the parameter adaptation law provides integral action. The proposed guidance law is intended for maneuvering in the horizontal-plane at given speeds and typical applications are marine craft, autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs) as well as other vehicles and craft where the goal is to follow a predefined parametrized curve without time constraints. Two vehicle cases studies are included to verify the theoretical results.http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6868251 "(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

The Isoline Tracking in Unknown Scalar Fields with Concentration Feedback

The isoline tracking of this work is concerned with the control design for a sensing vehicle to track a desired isoline of an unknown scalar field. To this end, we propose a simple PI-like controller for a Dubins vehicle in the GPS-denied environments. Our key idea lies in the design of a novel sliding surface based error in the standard PI controller. For the circular field, we show that the P-like controller can globally regulate the vehicle to the desired isoline with the steady-state error that can be arbitrarily reduced by increasing the P gain, and is eliminated by the PI-like controller. For any smoothing field, the P-like controller is able to achieve the local regulation. Then, it is extended to the cases of a single-integrator vehicle and a doubleintegrator vehicle, respectively. Finally, the effectiveness and advantages of our approaches are validated via simulations on the fixed-wing UAV and quadrotor simulators

A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking

This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of high-level decision making. Existing works tackle these issues by heuristically assigning targets to each sensing agent and solving the split problem for each agent. However, such heuristic methods reduce the target estimation performance in the absence of considering the changes of target state estimation along time. In this work, we detour the task-assignment problem by reformulating the general non-myopic planning problem to a distributed optimization problem with respect to targets. By combining alternating direction method of multipliers (ADMM) and local trajectory optimization method, we solve the problem and induce consensus (i.e., high-level decisions) automatically among the targets. In addition, we propose a modified receding-horizon control (RHC) scheme and edge-cutting method for efficient real-time operation. The proposed algorithm is validated through simulations in various scenarios.Comment: Copyright 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Mobile robotic network deployment for intruder detection and tracking

This thesis investigates the problem of intruder detection and tracking using mobile robotic networks. In the first part of the thesis, we consider the problem of seeking an electromagnetic source using a team of robots that measure the local intensity of the emitted signal. We propose a planner for a team of robots based on Particle Swarm Optimization (PSO) which is a population based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this thesis. Our results are also validated on an experimental testbed. In the second part, we present a game theoretic framework for visibility-based target tracking in multi-robot teams. A team of observers (pursuers) and a team of targets (evaders) are present in an environment with obstacles. The objective of the team of observers is to track the team of targets for the maximum possible time. While the objective of the team of targets is to escape (break line-of-sight) in the minimum time. We decompose the problem into two layers. At the upper level, each pursuer is allocated to an evader through a minimum cost allocation strategy based on the risk of each evader, thereby, decomposing the agents into multiple single pursuer-single evader pairs. Two decentralized allocation strategies are proposed and implemented in this thesis. At the lower level, each pursuer computes its strategy based on the results of the single pursuer-single evader target-tracking problem. We initially address this problem in an environment containing a semi-infinite obstacle with one corner. The pursuer\u27s optimal tracking strategy is obtained regardless of the evader\u27s strategy using techniques from optimal control theory and differential games. Next, we extend the result to an environment containing multiple polygonal obstacles. We construct a pursuit field to provide a guiding vector for the pursuer which is a weighted sum of several component vectors. The performance of different combinations of component vectors is investigated. Finally, we extend our work to address the case when the obstacles are not polygonal, and the observers have constraints in motion

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about invading antigens and needed antibodies. This information is encoded and sorted by T- and B-cells. The immune system has an adaptive component that can accelerate and intensify the immune response upon subsequent infection with the same antigen. The artificial memory cells attempt to mimic these characteristics for estimation error compensation and are constructed under normal conditions when all sensor systems function accurately, including those providing vehicle position and velocity information. The artificial memory cells consist of two main components: a collection of instantaneous measurements of relevant vehicle features representing the antigen and a set of instantaneous estimation errors or correction features, representing the antibodies. The antigen characterizes the dynamics of the system and is assumed to be correlated with the required corrections of position and velocity estimation or antibodies. When the navigation source is unavailable, the currently measured vehicle features from the onboard sensors are matched against the AIS antigens and the corresponding corrections are extracted and used to adjust the position and velocity estimation algorithm and provide the corrected estimation as actual measurement feedback to the vehicle’s control system. The proposed framework is implemented and tested through simulation in two versions: with corrections applied to the output or the input of the estimation scheme. For both approaches, the vehicle feature or antigen sets include increments of body axes components of acceleration and angular rate. The correction feature or antibody sets include vehicle position and velocity and vehicle acceleration adjustments, respectively. The impact on the performance of the proposed methodology produced by essential elements such as path generation method, matching algorithm, feature set, and the IMU grade was investigated. The findings demonstrated that in all cases, the proposed methodology could significantly reduce the accumulation of dead reckoning errors and can become a viable solution in situations where direct accurate measurements and other sources of information are not available. The functionality of the proposed methodology and its promising outcomes were successfully illustrated using the West Virginia University unmanned aerial system simulation environment