CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations

The estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization procedure for the entire trajectory and may not be feasible to be implemented in a low-cost robotic platform. In an attempt to allow the VIO to operate for a longer duration without either using or generating a map, this paper develops iterated cubature Kalman filter for VIO application that performs multiple corrections on a single measurement to optimize the current filter state and covariance during the measurement update. The optimization process is terminated using the maximum likelihood estimate based criteria. For comparison, this paper also develops a second solution to integrate VIO estimation with ranging measurements. The wireless communications between the vehicle and multiple beacons produce the ranging measurements and help to bound the accumulative errors. Experiments utilize publicly available dataset for validation, and a rigorous comparison between the two solutions is presented to determine the application scenario of each solution

Vision Analysis of Pack Ice for Potential Use in a Hazard Warning and Avoidance System

Ships travelling through pack ice are exposed to collisions that can result in structural damage to the hull. The GEM project at Memorial University has developed ice-ship interaction simulation software that allows study of the impact forces applied on a ship when it maneuvers through pack ice [1]. Such capability is useful in order to predict the collisions that would potentially affect the structural integrity and operational performance of ships and floating offshore structures. GEM is capable of simulating transit through complex pack ice formations at a rate much faster than real time. If hyper-real time simulation were available in a real operational setting, with actual ice, it would permit a variety of benefits, including general operational planning. If the near field ice information were sufficiently accurate, GEM could also be used in a “feed forward” near-field hazard warning and avoidance system (HWAS)

LiDAR and Vision Based Pack Ice Field Estimation for Aided Ship Navigation

Ships travelling through pack ice are exposed to structural damage in the hull due to collisions with ice-floe. GEM simulation environment, a Memorial University project, is an ice-ship interaction software that allows the study of the impact forces applied on a ship, when it maneuvers through pack ice [1]. At a rate much faster than the real-time, GEM is capable of simulating ship navigation through complex pack ice formations. Such a tool is beneficial in predicting hazardous collisions that affect structural integrity and operational performance of ships and floating offshore structures

AUV-Based Plume Tracking: A Simulation Study

This paper presents a simulation study of an autonomous underwater vehicle (AUV) navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained

Comparison of Stabilizing NMPC Designs for Wheeled Mobile Robots: an Experimental Study

In this paper, two stabilizing nonlinear model predictive control (NMPC) designs, namely, final-state equality constraint stabilizing design and final-state inequality constraint stabilizing design have been applied to achieve two wheeled mobile robot’s control objectives, i.e. point stabilization and trajectory tracking. In both controllers, final-state constraints are imposed, on the online optimization step, to guarantee the closed loop stability. As shown in the literature, both stabilizing designs were addressed to be computationally intense; thus, their real-time implementation is not tractable. Nonetheless, in this work, a recently developed toolkit implementing fast NMPC routines has been used to apply the two stabilizing designs on a mobile robot research platform after developing a C++ code, coupling the toolkit and the research platform’s software. Full scale experiments implementing the two stabilizing designs are conducted and contrasted in terms of performance measures and real-time requirements.IEEE IEEE Sri Lanka Section Robotics and Automation Section Chapter, IEEE Sri Lanka Sectio

Automated Tuning of the Nonlinear Complementary Filter for an Attitude Heading Reference Observer

Abstract In this paper we detail a numerical optimization method for automated tuning of a nonlinear filter used in Attitude Heading Reference Systems (AHRS). First, the Levenberg Marquardt method is used for nonlinear parameter estimation of the observer model. Two approaches are described; Extended Kalman Filter (EKF) based supervised implementation and unsupervised error minimization based implementation. The quaternion formulation is used in the development in order to have a global minimum parametrization in the rotation group. These two methods are then compared using both simulated and experimental data taken from a commercial Inertial Measurement Unit (IMU) used in an autopilot system of an unmanned aerial vehicle. The results reveal that the proposed EKF based supervised implementation is faster and also has a better robustness against different initial conditions

A systematic study of fuzzy {PID} controllers-function-based evaluation approach

A function-based evaluation approach is proposed for a systematic study of fuzzy proportional-integral-derivative (PID)-like controllers. This approach is applied for deriving process-independent design guidelines from addressing two issues: simplicity and nonlinearity. To examine the simplicity of fuzzy PID controllers, we conclude that direct-action controllers exhibit simpler design properties than gain-scheduling controllers. Then, we evaluate the inference structures of direct-action controllers in five criteria: control-action composition, input coupling, gain dependency, gain-role change, and rule/parameter growth. Three types of fuzzy PID controllers, using one-, two- and three-input inference structures, are analyzed. The results, according to the criteria, demonstrate some shortcomings in Mamdani's two-input controllers. For keeping the simplicity feature like a linear PID controller, a one-input fuzzy PID controller with "one-to-three" mapping inference engine is recommended. We discuss three evaluation approaches in a nonlinear approximation study: function-estimation-based, generalization-capability-based and nonlinearity-variation-based approximations. The study focuses on the last approach. A nonlinearity evaluation is then performed for several one-input fuzzy PID controllers based on two measures: nonlinearity variation index and linearity approximation index. Using these quantitative indices, one can make a reasonable selection of fuzzy reasoning mechanisms and membership functions without requiring any process information. From the study we observed that the Zadeh-Mamdani's "max-min-gravity" scheme produces the highest score in terms of nonlinearity variations, which is superior to other schemes, such as Mizumoto's "product-sum-gravity" and "Takagi-Sugeno-Kang" scheme

Two-level tuning of fuzzy PID controllers.

Fuzzy PID tuning requires two stages of tuning; low level tuning followed by high level tuning. At the higher level, a nonlinear tuning is performed to determine the nonlinear characteristics of the fuzzy output. At the lower level, a linear tuning is performed to determine the linear characteristics of the fuzzy output for achieving overall performance of fuzzy control. First, different fuzzy systems are defined and then simplified for two-point control. Non-linearity tuning diagrams are constructed for fuzzy systems in order to perform high level tuning. The linear tuning parameters are deduced from the conventional PID tuning knowledge. Using the tuning diagrams, high level tuning heuristics are developed. Finally, different applications are demonstrated to show the validity of the proposed tuning metho

New Methodology for Analytical and Optimal Design of Fuzzy PID Controllers

This paper describes a new methodology for the systematic design of fuzzy PID controllers based on theoretical fuzzy analysis and genetic-based optimization. An important feature of the proposed controller is its simple structure. It uses a one-input fuzzy inference with three rules and at most six tuning parameters. A closed-form solution for the control action is defined in terms of the nonlinear tuning parameters. The nonlinear proportional gain is explicitly derived in the error domain. A conservative design strategy is proposed for realizing a guaranteed-PID-performance (GPP) fuzzy controller. This strategy suggests that a fuzzy PID controller should be able to produce a linear function from its nonlinearity tuning of the system. The proposed PID system is able to produce a close approximation of a linear function for approximating the GPP system. This GPP system, incorporating with a genetic solver for the optimization, will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria (i.e., response error, stability, or robustness). Two indexes, linearity approximation index (LAI) and nonlinearity variation index (NVI), are suggested for evaluating the nonlinear design of fuzzy controllers. The proposed control system has been applied to several first-order, secondorder, and fifth-order processes. Simulation results show that the proposed fuzzy PID controller produces superior control performance than the conventional PID controllers, particularly in handling nonlinearities due to time delay and saturation