Hamzah Ahmad

Extended Kalman filter-based mobile robot localization with intermittent measurements

In this paper, a theoretical study on extended Kalman filter (EKF)-based mobile robot localization with intermittent measurements is examined by analysing the measurement innovation characteristics. Even if measurement data are unavailable and existence of uncertainties during mobile robot observations, it is suggested that the mobile robot can effectively estimate its location in an environment. This paper presents the uncertainties bounds of estimation by analysing the measurement innovation to preserve good estimations although some measurements data are sometimes missing. Theoretical analysis of the EKF is proposed to demonstrate the conditions when the problem occurred. From the analysis of measurement innovation, Jacobian transformation has been found as one of the main factors that affects the estimation performance. Besides that, the initial state covariance, process and measurement noises must be kept smaller to achieve better estimation results. The simulation and experimental results obtained are showing consistent behaviour as proposed in this paper.

Robot localization and mapping problem with unknown noise characteristics

In this paper, we examine the H<inf>∞</inf> Filter-based SLAM especially about its convergence properties. In contrast to Kalman Filter approach that considers zero mean gaussian noise, H<inf>∞</inf> Filter is more robust and may provide sufficient solutions for SLAM in an environment with unknown statistical behavior. Due to this advantage, H<inf>∞</inf> Filter is proposed in this paper, to efficiently estimate the robot and landmarks location under worst case situations. H<inf>∞</inf> Filter requires the designer to appropriately choose the noises covariance with respect to γ to obtain a desired outcome. We show some of the conditions to be satisfy in order to achieve better estimation results than Kalman Filter. From the experimental results, H<inf>∞</inf> Filter performs better than Kalman Filter for a case of bigger robot initial uncertainties. Subsequently, this proved that <inf>∞</inf> Filter can provide another available estimation method for especially in SLAM.

Design and Development of Unit Cell and System for Vanadium Redox Flow Batteries (V-RFB)

Vanadium redox flow battery (V-RFB) has been attracted by many researches; some are under field testing and demonstration stage, but information on construction, experimental characterization, electrolyte preparation, overall systems under study, etc. are still limited. This paper focus on the technical issues faced and the lessons learnt during the development of unit cell and system for V-RFB. Firstly, brief description on problem identification, development and implementation in cell design and system for V-RFB are discussed. Secondly, preliminary experiment on 25 cm2 laboratory, unit cell V-RFB presents various difficulties such as its high tendency to fall under failure mode are presented. Finally, discussion on experimental result which shows significant improvement on V-RFB system efficiency up to 72% with reduction of contact resistance, recorded an average of 8.6 mΩ. In addition, the newly developed system provides a constructive base for future studies in temperature-controlled system and a divided, open-circuit potentiometric cell for half-cell redox analysis.

An Assembly Sequence Planning Approach with a Multi-state Particle Swarm Optimization

Assembly sequence planning (ASP) becomes one of the major challenges in the product design and manufacturing. A good assembly sequence leads in reducing the cost and time of the manufacturing process. However, assembly sequence planning is known as a classical hard combinatorial optimization problem. Assembly sequence planning with more product components becomes more difficult to be solved. In this paper, an approach based on a new variant of Particle Swarm Optimization Algorithm (PSO) called the multi-state of Particle Swarm Optimization (MSPSO) is used to solve the assembly sequence planning problem. As in of Particle Swarm Optimization Algorithm, MSPSO incorporates the swarming behaviour of animals and human social behaviour, the best previous experience of each individual member of swarm, the best previous experience of all other members of swarm, and a rule which makes each assembly component of each individual solution of each individual member is occurred once based on precedence constraints and the best feasible sequence of assembly is then can be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and comparison has been conducted against other three approaches based on Simulated Annealing (SA), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement.

Feasibility study of partial observability in H ∞ filtering for robot localization and mapping problem

This paper presents H∞ Filter SLAM, which is also known as the minimax filter to estimate the robot and landmarks location with the analysis on partial observability. Some convergence conditions are also presented to aid the analysis. Due to SLAM is a controllable but unobservable problem, its difficult to estimate the position of robot and landmarks even though the control inputs are given to the system. As a result, Covariance Inflation which is a method of adding a pseudo positive semidefinite(PsD) matrix is proposed as one approach to analyze Partial Observability effects in SLAM and to reduce the computation cost. H∞ Filter is capable of withstand non-gaussian noise characteristics and therefore, may provide another available approach towards SLAM solution.

Fuzzy logic based centralized power sharing scheme

This paper presents a new technique to improve the centralized power sharing scheme for parallel connected inverters in AC islanded microgrids. The proposed technique is based on centralized power sharing scheme using Fuzzy Logic Controller (FLC). This technique can accurately control the active and reactive power coming from inverter thus ensuring excellent power sharing. Simulation results are presented to show effectiveness of the proposed scheme in sharing the load among the parallel connected inverters.

Examining the eigenvalues effect to the computational cost in mobile robot simultaneous localization and mapping

Covariance update in an extended Kalman filter-based simultaneous localization and mapping contributes the most to the computing time.Simplification of the covariance structure could decrease the computational cost and thereby shorten the processing time.Diagonalization of the covariance matrix by means of eigenvalues approach was introduced. One of the biggest factors that contributes to the computational cost of extended Kalman filter-based simultaneous localization and mapping is the computation of the covariance update. This results from the multiplications of the covariance matrix with other parameters along with the increment of its dimension, which is twice the number of landmarks. This study attempts to look for an optimal solution to decrease the computational complexity of the covariance matrix without compromising the accuracy of the state estimation through eigenvalue approach. This paper presents a study on the matrix-diagonalization technique, which is applied to the covariance matrix in extended Kalman filter-based simultaneous localization and mapping to simplify the multiplication process. The behavior of estimation and covariance were observed based on four case studies to analyze the performance of the proposed technique.

Sufficient condition for estimation in designing H ∞ filter-based slam

Extended Kalman filter (EKF) is often employed in determining the position of mobile robot and landmarks in simultaneous localization and mapping (SLAM). Nonetheless, there are some disadvantages of using EKF, namely, the requirement of Gaussian distribution for the state and noises, as well as the fact that it requires the smallest possible initial state covariance. This has led researchers to find alternative ways to mitigate the aforementioned shortcomings. Therefore, this study is conducted to propose an alternative technique by implementing filter in SLAM instead of EKF. In implementing filter in SLAM, the parameters of the filter especially γ need to be properly defined to prevent finite escape time problem. Hence, this study proposes a sufficient condition for the estimation purposes. Two distinct cases of initial state covariance are analysed considering an indoor environment to ensure the best solution for SLAM problem exists along with considerations of process and measurement noises statistical behaviour. If the prescribed conditions are not satisfied, then the estimation would exhibit unbounded uncertainties and consequently results in erroneous inference about the robot and landmarks estimation. The simulation results have shown the reliability and consistency as suggested by the theoretical analysis and our previous findings.

Analyzing the mobile robot localization performance in partially observable conditions

This paper deals with theoretical investigation of robot localization considering partial observability conditions. The problem is very important as most of the mobile robot applications are controllable but not observable due to some aspects. The paper investigate the importance of correlation to the mobile robot estimation through the technique of decorrelating some of the elements of state covariance of the updated state covariance. To demonstrate the effect of correlation, two cases of partial observability are examined which are the unstable and stable partial observability. The cases are build based on the configurations of the state covariance for mobile robot references when doing observations. Our preliminary results suggest that the system with stable partial observability shows very good and consistent estimation while the unstable case lead to inconsistency and erroneous estimation.

The effect of intermittent measurement in Simultaneous Localization and Mapping

Intermittent measurement is defined as a situation where a mobile robot experiences loss of measurement data during observation due to sensor failure or imperfection of the system. The impact of intermittent measurement on the Simultaneous Localization and Mapping (SLAM) of a mobile robot is the subject of this paper. The analysis is important since SLAM requires recursive measurement data update throughout the process. In this paper, the effect of intermittent measurement on the state error covariance matrix was analyzed on two basic conditions, which are when the mobile robot is stationary and when it is in motion. We have observed the impact on the determinant of covariance matrix. It is proven by our analysis that intermittent measurement could cause inaccurate estimation of robots position and might increase the state error covariance matrix.