Humanoid Robot Localization Research Articles

Novel lightweight odometric learning method for humanoid robot localization

Odometry as one of the inevitable parts of robot behavior control plays an essential role in the localization of humanoid robots. Calculating odometry on the humanoid robots is usually based on one or combination of the vision, laser scanner, magnetic sensor, and pressure sensors. Vision or laser scanner-based approaches require high computational power for analyzing visual information. Hence, these kinds of approaches are not suitable for all kind of the small humanoid robots. On the other hand, it is known that magnetic sensors have instability problems in different environments. Furthermore, calculating accurate dead reckoning (pure odometry) is very difficult because of the complex mechanical system of humanoid robots, the presence of many sources of uncertainty, and inaccuracy in motion executions such as foot slippage. Therefore, this paper presents a robust learning method to localize a humanoid robot named Lightweight Humanoid robot Odometric Learning method (LHOL). This method does not employ any vision, magnetic, additional pressure sensors, and laser scanners, and, therefore, eliminates dependencies to these sensors for the first time. The method's core learning is based on the artificial neural network (ANN) which uses kinematic computations, IMU (roll and pitch data), and the robot's actuators’ internal present load data as input data. The proposed LHOL method proves high accuracy on a novel fully 3D printed kid-sized humanoid robot platform (ARC) with both open-loop and closed-loop walk engines at differently covered floors.

A Reliability-Based Particle Filter for Humanoid Robot Self-Localization in RoboCup Standard Platform League

This paper deals with the problem of humanoid robot localization and proposes a new method for position estimation that has been developed for the RoboCup Standard Platform League environment. Firstly, a complete vision system has been implemented in the Nao robot platform that enables the detection of relevant field markers. The detection of field markers provides some estimation of distances for the current robot position. To reduce errors in these distance measurements, extrinsic and intrinsic camera calibration procedures have been developed and described. To validate the localization algorithm, experiments covering many of the typical situations that arise during RoboCup games have been developed: ranging from degradation in position estimation to total loss of position (due to falls, ‘kidnapped robot’, or penalization). The self-localization method developed is based on the classical particle filter algorithm. The main contribution of this work is a new particle selection strategy. Our approach reduces the CPU computing time required for each iteration and so eases the limited resource availability problem that is common in robot platforms such as Nao. The experimental results show the quality of the new algorithm in terms of localization and CPU time consumption.

Color Image Processing and Self-Localization for a Humanoid Soccer Robot

The paper proposed a series of image processing algorithm to recognize the evidences in an image accurately for humanoid soccer robot, such as color image segmentation based on HSV model, edge detection based on four linear operator, field straight line extraction by Hough transform based on 8-neighbhour connected domain clusters and identification of line intersection shape based on Hopfield network. Based on evidences from image processing, Piecewise Monte Carlo localization is presented to solve kidnap problem so that localization of humanoid robot can be not only adapt to rule changes for competition, but also be more efficient and robust. The effectiveness of the piecewise MCL is verified by RoboCup Adult Size humanoid soccer robot, Erectus. The experimental results showed that the humanoid robot was able to solve the kidnap problem adaptively with two strategies: resetting or revising, in which resetting was more efficient than revising gradually.

A VISION-BASED LOCATION POSITIONING SYSTEM VIA AUGMENTED REALITY: AN APPLICATION IN HUMANOID ROBOT NAVIGATION

In this paper, we present a vision-based localization system using mobile augmented reality (MAR) and mobile audio augmented reality (MAAR) techniques, applicable to both humans and humanoid robots navigation in indoor environments. In the first stage, we propose a system that recognizes the location of a user from the image sequence of an indoor environment using its onboard camera. The location information is added to the user's view in the form of 3D objects and audio sounds with location information and navigation instruction content via augmented reality (AR). The location is recognized by using the prior knowledge about the layout of the environment and the location of the AR markers. The image sequence can be obtained using a smart phone's camera and the marker detection, 3D object placement and audio augmentation will be performed by the phone's operating processor and graphical/audio modules. Using this system will majorly reduce the hardware complexity of such navigation systems, as it replaces a system consisting of a mobile PC, wireless camera, head-mounted displays (HMD) and a remote PC with a smart phone with camera. In the second stage, the same algorithm is employed as a novel vision-based autonomous humanoid robot localization and navigation approach. The proposed technique is implemented on a humanoid robot NAO and improves the robot's navigation and localization performance previously done using an extended Kalman filter (EKF) by presenting location-based information to the robot through different AR markers placed in the robot environment.

Active Sound Localization in a Symmetric Environment

Localization for humanoid robots becomes difficult when events that disrupt robot positioning information occur. This holds especially true in symmetric environments because landmark data may not be sufficient to determine orientation. We propose a system of localizing humanoid robots in a known, symmetric environment using a Rao-Blackwellized particle filter and a sound localization system. This system was used in the RoboCup Standard Platform League, and has been found to reduce the amount of own-goals scored as compared with the previously used localization system without sound.