Immanuel Ashokaraj

Contaminant Cloud Boundary Monitoring Using Network of UAV Sensors

In this paper, we describe research work currently being undertaken to detect, model, and track the shape of a contaminant cloud boundary using air borne sensor swarms. A model of the contaminant cloud boundary is first developed using a splinegon, defined by a set of vertices linked by segments of constant curvature. This model is then used in an estimator to predict the evolution of the contaminant cloud. This approach is efficient in that only the vertices and segment curvatures are required to define the cloud boundary, rather than using a distribution function to represent the dispersion density.

Sensor based robot localisation and navigation: using interval analysis and unscented Kalman filter

Multiple sensor fusion for robot localisation and navigation has attracted a lot of interest in recent years. This paper describes a sensor based navigation approach using an interval analysis (IA) based adaptive mechanism for an unscented Kalman filter (UKF). The robot is equipped with inertial sensors (INS), encoders and ultrasonic sensors. A UKF is used to estimate the robots position using the inertial sensors and encoders. Since the UKF estimates may be affected by bias, drift etc. we propose an adaptive mechanism using IA to correct these defects in estimates. In the presence of landmarks the complementary robot position information from the IA algorithm using ultrasonic sensors is used to estimate and bound the errors in the UKF robot position estimate.

Robust Sensor-Based Navigation for Mobile Robots

This paper describes a deterministic approach for sensor-based localization and navigation of a mobile robot equipped with ultrasonic sensors using interval analysis. For localization, the map is 2-D and assumed to be known. It is shown that robot localization is achieved without the need for an interval model of the robot; instead, the physical limitations of the robot are used to predict and track the robots position. In classical methods of robot localization such as Kalman filters, the data-association step is extremely complex and usually based on linearization. The interval analysis method proposed in this paper bypasses the data-association step and directly deals with the nonlinear problem in a global way.

Contaminant cloud boundary monitoring using UAV sensor swarms

In this paper we describe research work currently being undertaken to detect, model and track the shape of a contaminant cloud boundary using air borne sensor swarms. The model of the cloud boundary is then used to predict the future evolution of the cloud shape so that an airborne sensor swarm of UAVs can perform manoeuvres that will enable the exact shape and track of the cloud to be determined accurately and in a timely fashion. The contaminant cloud models currently used are usually based on numerical techniques. However in this research work the kinematics of the evolving cloud to approximate the shape of the cloud using splinegons is explored. This approach is e‐cient in that only the vertices and segment curvatures are required to deflne the cloud boundary, rather than a distribution function.

Feature based robot navigation: using fuzzy logic and interval analysis

This work describes a new approach for mobile robot navigation using interval analysis and fuzzy logic. The robot is equipped with inertial sensors, encoders and ultrasonic sensors. The map used for this study is two-dimensional and it is assumed to be known. Multiple sensor fusion for robot localisation and navigation has attracted a lot of interested in recent years. The most commonly used approach is based on Kalman filter and other stochastic filters. Here we propose an alternative approach using interval analysis with multiple sets of ultrasonic measurements. Interval analysis has been already successfully applied in the past for robot localisation. But the results obtained may be conservative. Therefore this approach is extended using multiple sets of ultrasonic measurements, which results in estimation of multiple interval robot positions. These multiple interval robot positions are then fused using fuzzy logic to give a less conservative interval robot position estimate. Also interval analysis based algorithm can be used only in the presence of land marks. This problem is overcome here using additional sensors such as encoders and inertial sensors, which gives an estimate of the robot position using fuzzy logic in the absence of land marks.

A fuzzy logic approach in feature based robot navigation using interval analysis and UKF

Multiple sensor fusion for robot localisation and navigation has attracted a lot of interest in recent years. This paper describes a sensor based navigation approach using an interval analysis (IA) based adaptive mechanism for an unscented Kalman filter (UKF). The robot is equipped with inertial sensors (INS), encoders and ultrasonic sensors. An UKF is used to estimate the robot position using the inertial sensors and encoders. Since the UKF estimates may be affected by bias, drift etc., an adaptive mechanism using IA to correct these defects in estimates is proposed. The IA robot position estimate may be conservative, in which case multiple measurements are taken and the multiple interval robot position is fused together using fuzzy logic to obtain a single interval robot position estimate. In the presence of landmarks the complementary robot position information from the IA algorithm using ultrasonic sensors is used to estimate and bound the errors in the UKF robot position estimate.

Application of an extended Kalman filter to multiple low cost navigation sensors in wheeled mobile robots

The present aim of this research is to design a navigation sensor suite for a newly built mobile robot using low cost multiple sensors. This paper addresses the problem of generating navigational data for a wheeled mobile robot. An extended Kalman filter (EKF) is used to fuse data from multiple low cost sensors. In order to estimate the spatial position of a wheeled robot, a combination of accelerometers, a rate gyroscope and two wheel encoders are used. A fundamental principle of dynamic systems is that, if we can measure all internal system states, we have complete freedom in control system design. The system discussed in this paper has more measurement sensors than system states and therefore the sensors give overlapping, low-grade information affected by noise, bias, drift etc. The dynamics of the robot and sensor system are nonlinear. Therefore an EKF is used to fuse these overlapping low-grade measured sensor data and give the best possible estimate of the mobile robot position. A significant advantage of using multiple sensors is that measurement errors can be identified by comparison of different sensor readings.

A Robust Approach to Multiple Sensor Based Navigation for an Aerial Robot

This paper describes a multiple sensor fusion approach in which a sensor based navigation scheme needs to fuse a stochastic aerial robot position estimate from an extended Kalman filter (EKF) with a deterministic aerial robot position estimate from an interval analysis (IA) algorithm. The aerial robot is equipped with inertial sensors (INS) and ultrasonic sensors. An EKF is used to estimate the aerial robots position using the inertia! sensors. When landmarks are present, the ultrasonic sensor measurements are processed using an IA algorithm to get an interval aerial robot position estimate. In order to obtain a better estimate for the aerial robot position both deterministic and stochastic estimates need to be used via a data fusion approach. Thus there is a need to study how to fuse the aerial robot position estimate having a Gaussian distribution (from EKF) with a aerial robot position estimate that has a uniform distribution (from IA). This is accomplished here by using the Box-Muller transform to transform the interval aerial robot position estimate having a uniform distribution to a real number aerial robot position with a Gaussian distribution and giving that as a measurement to the EKF to obtain a fused estimate of the aerial robot position

Implementation of an adaptive EKF to multiple low cost navigation sensors in wheeled mobile robots

The present aim of this research is to design a navigation sensor suite for a newly built mobile robot using low cost multiple sensors. A basic requirement for an autonomous mobile robot is its ability to localize itself accurately. This paper describes an accurate method for generating navigational data for a wheeled mobile robot. An adaptive extended Kalman filter (AEKF) is used to fuse data from multiple low cost sensors. In order to estimate the spatial position of a wheeled robot, a combination of accelerometers, a rate gyroscope and two wheel encoders are used. The system discussed in this paper has more measurement sensors than system states and therefore the sensors give overlapping, low-grade information affected by noise, bias, drift, etc. The dynamics of the robot and sensor system are non-linear. Therefore an AEKF is used to estimate these overlapping low-grade measured sensor data and give the best possible estimate of the mobile robot position. The adaptive mechanism in this case uses the Riccati Equation adaption. The basic idea is to change the Kalman Gain. This is done by changing the Process noise co-variance matrix adaptively. Simulations show an improved performance in the estimates from the AEKF when compared to the EKF.

Actuator Model Uncertainty with Bounded Responses

Multi-wheel mobile robotic vehicles require speed and torque measurements, which are constrained within practical error bounds. The paper systematically analyses the multiple sensor uncertainty and guarantees bounded input signals for the general case of a PMDC actuator within the context of wheel electrically actuated vehicles. For such vehicles often complex sensors and feedback systems are required to continuously monitor changes on the state variables, speed and torque and compensate accordingly. The paper proposes a bounded sensor error inclusion in the model analysis. The resulting mathematical formulation results in the derivation of the actuation signals with guaranteed intervals. The analyses also extend to the power converter operating at Q1-mode and clearly shows the duty-cycle variations and how this can affect the actuator system and consequently the robotic vehicle