where xt+1 is the position and orientation of the robot (with respect to a reference frame) at time t + 1, with (ξ, η) giving the x and y coordinates and θ the angle (with respect to the x-axis) that the robot is facing. The robot has two drive wheels, of radius r on an axle of length d. During time period t the right wheel is believed to rotate at a velocity of φRt and the left at a velocity of φLt. In this example, these velocities are fixed with φRt = 0.4 and φLt = 0.1. The state update function, F , calculates where the robot should be at each time point, given its previous position. However, in reality, there is some random fluctuation in the velocity of the wheels, for example, due to slippage. Therefore the actual position of the robot and the position given by equation F will differ. The magnitude of these random fluctuations is included in the model through Σx.

Unscented Kalman Filterの計測への応用に関する研究

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.

Optimal path planning for a mobile robot using cuckoo search algorithm

In order to transport materials flexibly and smoothly in a tight plant environment, an omni-directional mobile robot based on four Mecanum wheels was designed. The mechanical system of the mobile robot is made up of three separable layers so as to simplify its combination and reorganization. Each modularized wheel was installed on a vertical suspension mechanism, which ensures the moving stability and keeps the distances of four wheels invariable. The control system consists of two-level controllers that implement motion control and multi-sensor data processing, respectively. In order to make the mobile robot navigate in an unknown semi-structured indoor environment, the data from a Kinect visual sensor and four wheel encoders were fused to localize the mobile robot using an extended Kalman filter with specific processing. Finally, the mobile robot was integrated in an intelligent manufacturing system for material conveying. Experimental results show that the omni-directional mobile robot can move stably and autonomously in an indoor environment and in industrial fields.

The Design and Development of an Omni-Directional Mobile Robot Oriented to an Intelligent Manufacturing System

Many techniques for robot localization rely on the assumption that both process and measurement noises are uncorrelated, white, and normally distributed. However, if this assumption does not hold, these techniques are no longer optimal and, in addition, the maximum estimation errors can be hardly kept under control. In this paper, this problem is addressed by means of a tailored extended H $_\infty$ filter (EHF) fusing odometry and gyroscope data with position and heading measurements based on quick response (QR) code landmark recognition. In particular, it is shown that, by properly tuning EHF parameters and by using an adaptive mechanism to avoid finite escape time phenomena, it is possible to achieve a higher localization accuracy than using other dynamic estimators, even if QR codes are detected sporadically. Also, the proposed approach ensures a good tradeoff in terms of computational burden, convergence time, and deployment complexity.

Indoor Localization of Mobile Robots Through QR Code Detection and Dead Reckoning Data Fusion

The complete dynamics model of a four-Mecanum-wheeled robot considering mass eccentricity and friction uncertainty is derived using the Lagrange’s equation. Then based on the dynamics model, a nonlinear stable adaptive control law is derived using the backstepping method via Lyapunov stability theory. In order to compensate for the model uncertainty, a nonlinear damping term is included in the control law, and the parameter update law with σ-modification is considered for the uncertainty estimation. Computer simulations are conducted to illustrate the suggested control approach.

/pdf/modeling-and-adaptive-control-of-an-omni-mecanum-wheeled-3upba2sy1d.pdf

Modeling and Adaptive Control of an Omni-Mecanum-Wheeled Robot

This paper presents an INS (inertial navigation system) for an AGV (automatic guided vehicle) with Mecanum wheels. An omni wheel or a Mecanum wheel, which has rollers attached to a conventional wheel, facilitates omni-directional driving. Most positioning systems use the encoder because it can measure precisely the rotation of the wheel. However, it is difficult to accurately calculate the position of an AGV with omni wheels or Mecanum wheels because slips occur frequently in the rollers attached to the wheels. Therefore, many studies have been carried out to compensate for the weakness of the encoders by fusing an accelerometer and a gyro sensor. However, there is still a rapid increase in the number of errors, owing to the second integral of an accelerometer. Hence, this paper proposes an INS for an AGV with Mecanum wheels. The proposed system integrates an encoder, an accelerometer, and a gyro sensor through two Kalman filters. To verify the performance of the proposed INS, we analyzed the positioning accuracy of an AGV by studying straight, sideways, and diagonal movements over a 250 cm distance in a 300 cm × 300 cm space at speeds of about 200 and 380 mm/s. The results of the experiment showed that the proposed INS can measure effectively the position of an AGV, despite frequent slips.

Inertial Navigation System for an Automatic Guided Vehicle with Mecanum Wheels

Most localization algorithms use a range sensor or vision in a horizontal view, which usually imparts some disruption from a dynamic or static obstacle. By using landmarks on ceiling which the vehicle position were vertically measured, the disruption from horizontal view was reduced. We propose an indoor localization and navigation system based on an extended Kalman filter (EKF) and real-time vision system. A single upward facing digital camera was mounted on an autonomous vehicle as a vision sensor to recognize the landmarks. The landmarks consisted of multiple circles that were arranged in a defined pattern. Information on a landmark’s direction and its identity as a reference for an autonomous vehicle was produced by the circular arrangements. The pattern of the circles was detected using a robust image processing algorithm. To reduce the noise that came from uneven light, the process of noise reduction was separated into several regions of interest. The accumulative error caused by odometry sensors (i.e., encoders and a gyro) and the vehicle’s position were calculated and estimated, respectively, using the EKF algorithm. Both algorithms were tested on a vehicle in a real environment. The image processing method could precisely recognize the landmarks, and the EKF algorithm could accurately estimate the vehicle’s position. The experimental results confirmed that the proposed approaches are implementable.

/pdf/implementation-of-real-time-positioning-system-using-2v1qy9afxi.pdf

Implementation of real-time positioning system using extended Kalman filter and artificial landmark on ceiling

This paper presents a driving control of omnidirectional mobile robot with Mecanum wheel. The omnidirectional driving is useful to drive in narrow space. There are various ways to implement the omnidirectional driving. The Mecanum wheel is one ways for omnidirectional driving. However, it has weaknesses which are difficult to calculate kinematics and have inaccurate driving by slip. If slip occurred, Mecanum wheeled robot moves in unexpected direction. Therefore, we propose the driving control method using fuzzy inference system. By comparing to two rotation angles, we can know whether slip occurred. One is measured rotation angle and the other is calculated by kinematics. If there is a difference between two angles, slip occurred. Then each wheel is controlled by fuzzy inference system to correct orientation. We analyze performance of the proposed driving control method by simulation.

Driving control of mobile robot with Mecanum wheel using fuzzy inference system

Positioning accuracy improvement of laser navigation using UKF and FIS

This paper presents a positioning method of the laser guidance with a particle filter for an AGV (automatic guided vehicle). Wired guidance and magnetic guidance system have been used mainly for AGV's guidance in industry. However, these guidance systems have an expensive installation cost and a difficult maintenance. Therefore, the laser guidance system as a wireless guidance system is increasingly used. However, this system has a number of positioning errors when the AGV turns or moves fast by the characteristics of the laser guidance which detects reflectors using a rotating header within the particular operating environment. Hence, this paper proposes a positioning method to improve the performance of the laser guidance system. The proposed system integrates two encoders, a gyro sensor and laser guidance through the particle filter. To verify the performance of the proposed positioning method, we compare the positioning accuracy of the proposed method with an existing product of the laser guidance system. The experimental results show that the proposed method is more accurate than the existing product.

http://ieeexplore.ieee.org/iel5/6093620/6106068/06106256.pdf

Jung-Min Kim

Papers

Inertial Navigation System for an Automatic Guided Vehicle with Mecanum Wheels

Implementation of real-time positioning system using extended Kalman filter and artificial landmark on ceiling

Driving control of mobile robot with Mecanum wheel using fuzzy inference system

Positioning accuracy improvement of laser navigation using UKF and FIS

Implementation of laser navigation system using particle filter