Augie Widyotriatmo

Navigation Function-Based Control of Multiple Wheeled Vehicles

In this paper, a collision-free navigation method for a group of autonomous wheeled vehicles is investigated. The position and orientation information of individual vehicles is transformed to navigation variables, which are the distance left to the goal position, the angle made by the orientation of the vehicle at the goal position and the vehicle-to-target (v-to-t) vector, and the angle made by the heading direction of the vehicle and the v-to-t vector. As a Lyapunov function for deriving a smooth control law that drives all the vehicles from an initial configuration to a goal configuration, a new navigation function that incorporates the squared norm of the navigation variables, the boundaries of collision-free areas, and the angles made by the vehicle heading direction and the vehicle-to-obstacle (v-to-o) vectors is proposed. The asymptotic stability of the closed-loop system is proved. The effectiveness of the developed algorithm is illustrated through three simulations (three vehicles in a free environment, three vehicles in the presence of a static obstacle, and eight vehicles operating along a corridor) and two experiments (static and moving obstacles avoidance). The proposed algorithm has been implemented on a real forklift and the navigation of the forklift from an arbitrary initial configuration to a goal configuration while avoiding collisions has been demonstrated.

A collaborative control of brain computer interface and robotic wheelchair

This paper presents a new scheme in collaborating non-invasive brain computer interface (BCI) and a wheelchair equipped with robotic systems. The BCI system implements steady state visual evoked potential (SSVEP) method that extracts features from electro-encephalography (EEG) signals in determining the intentions or commands from human-brain. The classification of EEG signals utilizes filter-bank in accordance with frequency of the stimuli visual. The user intentions, which are to move “left”, “right”, and “forward”, and “stop” are collaborated with an intelligent robotic system of a wheelchair. The wheelchair system is equipped with environment recognition sensors. The collaborative control is to manage the motion of the wheelchair based upon the intention of the user and the condition of the environment. The scenario is limited to the collaboration of BCI-robotic wheelchair in a corridor environment with walls on both sides. The user intention is set to “forward”. The intelligent robotic wheelchair perform wall following and obstacle avoidance motions while receiving the command “forward”. Also, it performs emergency-stop if the extracted intentions from the BCI put the user in dangerous situation. Experimental results are conducted to show the effectiveness of the proposed method.

Decision making framework for autonomous vehicle navigation

A key trait of an autonomous vehicle is the ability to handle multiple objectives, such as planning to guide the autonomous vehicle from an initial to a goal configuration, avoiding obstacles, and decision making to choose an optimal action policy. Moreover, the autonomous vehicle should act in ways that are robust to sorts of uncertainties, such as wheel slip, sensors affected by noise, obstacle move unpredictably, etc. In this paper we propose a decision making framework for autonomous vehicle conducting a nonholonomic motion planner, localization, obstacle avoidance, and also dealing with the uncertainties. The decision making framework manages the safety and task related assignment by adopting the partially observable Markov decision process model. We predict N-steps belief states from the action sequence candidate and use them as inputs of a proposed reward value function. The dimensionality problem in the searching of an action sequence from the action space (linear and rotational velocities) is simplified by applying a nonholonomic motion planner as a reference. The simulation results show the decision path resulting from decision making framework depends on the initial setting of belief state of its position and orientation, and also the determination of uncertainties of the sensors and the actuators of the vehicle.

Optimization of capacity and operational scheduling for grid-tied microgrid using pumped-storage hydroelectricity and photovoltaic

This paper deals with the optimization of capacity and operation scheduling for a grid-tied microgrid system. Photovoltaic power system is used as renewable energy source and pumped-storage hydroelectricity is used as energy storage. The physical models of the photovoltaic system and pumped-storage hydroelectricity are developed. The objective function is formulated based on capital and operational costs of individual systems. The constraints for the optimization are defined considering the model of systems, operational limitations, and performance requirements. The capacity and the operation scheduling of the photovoltaic and pumped-storage hydroelectricity are optimized based on the solar insolation data and average load demand tied to the microgrid. The performances required are high renewable energy penetration and low curtailed renewable energy. The optimization problem is solved using the mixed integer linear problem (MILP). With the proposed scheme, the renewable energy penetration ratio achieves 50%, which is acceptable. Moreover, the curtailed renewable energy ratio of 1.22% is obtained, which is very low. Both achievements, the acceptable renewable energy penetration ratio and the low curtailed renewable energy ratio, are the key outcomes of this research.

Electromyography (EMG) signal recognition using combined discrete wavelet transform based on Artificial Neural Network (ANN)

Rapid disability patients increasing over time and need a solution in the future. Hand amputation is one form of disability that common in Indonesian society. A possible solution would be necessary at the moment is the development of prosthetic hand that has the ability as a human hand. The development of neuroscience has now reached the stage of the bodys ability to use the signal as an input signal to operate a system. One of the applications of the science development is the use of electromyography (EMG) signals as an input to the control system to operate the prosthetic hand. This study is divided into two stages: a preliminary study and further research. Initial research focus in the process of EMG signal pattern recognition and advanced research focus in the development of a prototype prosthetic hand that is integrated with the controller system. Preliminary research indicates that the results of pattern recognition EMG signal using wavelet transform and Artificial Neural Network (ANN) classification has an accuracy rate of about 77.5 %. Based on these results, it can be concluded that the study results could be used as a signal input to program control of the prosthetic hand that will be developed in phase two.

Optimization of a grid-tied microgrid configuration using dual storage systems

This paper presents optimization in capacity sizing and operational schedule for a grid-tied microgrid using dual storage systems. Photovoltaic power system is used as a renewable energy source. Pumped-storage hydroelectricity and battery energy storage system are utilized as energy storage systems. The physical models of the photovoltaic system, pumped-storage hydroelectricity, and battery energy storage system are developed. The objective function is formulated to minimize capital and operational costs of grid-tied microgrid system. The constraints for the optimization are formulated based on the system model, operational limitations, and performance requirements. Capacity and operational schedule for the microgrid system is optimized using solar insolation data and load demand data. Performances required for microgrid system are high renewable energy penetration with low curtailed renewable energy. Mixed integer linear programming (MILP) is used to solve the optimization problem. With the proposed method, renewable energy penetration ratio achieves 50%. The curtailed renewable energy ratio of 10.4% is obtained.

Quadrotor position control based on model identification and proportional-derivative algorithm

In this paper, position control for a quadrotor in outdoor environment is designed and is validated by experiment. The quadrotor used in the experiment is equipped by accelerometers which are used by an internal controller to maintain its velocity in the global coordinate, and a global position system (GPS) for location determination. A proportional derivative (PD) controller, utilizing feedback of the data location from the GPS, is used to control the motion of quadrotor, reaching a final position from an initial position. Identification of motion model is conducted to determine proper parameters for the controller. The experiments show that the quadrotor can achieve the final positions with acceptable errors in the presence of noise in the GPS measurement and disturbance caused by the wind in outdoor environment.

Wall following control of a mobile robot without orientation sensor

The paper presents a novel wall following control of a mobile robot without orientation sensors. The problem is formulated as a path following problem of a mobile robot. The control law derived using the proposed method assure the asymptotic stabilization of the origin by using the Lyapunov method. The effectiveness of the proposed method is shown by experimental results using a P3DX mobile robot utilizing sonar sensors to detect only the distance to the wall without orientation sensor.

Mobile robot localization using modified particle filter

Localization is one of many issues in mobile robot study. Localization is an essential ability for mobile robot to determine its location, so that it can plan a movement and go to a desired location. The mutual method for mobile robot localization is using a particle filter. High computation needs in particle filter is one of problem in particle filtering to get accurate location. The paper proposes a low computational mobile robot localization using a particle filter. It uses two methods: local localization using a dead reckoning method and global localization using a landmark-based vision sensor. Simulation results show that the proposed method provides a good estimation on the mobile robot position and orientation.

Optimization of capacity and operational scheduling for microgrid system using two-stage stochastic linear programming

This paper presents the determination of capacity and operational schedule for a grid-tied microgrid system based on a stochastic optimization method. A photovoltaic power system is used as a renewable energy source, while battery system is utilized as energy storage systems. The microgrid system can be operated using the usual priority scheme or the proposed scheduling scheme. The mathematical model for the microgrid system is developed. The objective function is formulated from a capital and operational costs. The constraints for the optimization are formulated based on system model, physical limitations, and performance requirements. Performances required for microgrid system are high renewable energy penetration with low curtailed renewable energy. Two-stage stochastic linear programming method is used to solve the optimization problem. Proposed scheduling scheme is able to increase renewable energy penetration ratio by 4% and reduce curtailed renewable energy production ratio by 7%. The combination of scheduling scheme and stochastic optimization to improve performances of microgrid system are the key outcomes of this research.