Department Publications

Difficult conditions on outdoor terrains make outdoor autonomy for rovers, a challenging task. The conventional wheel odometry method uses orientation measurements to assume a momentary plane to apply wheel encoder readings. On uneven terrains, this method often gives poor results for position tracking, and therefore rarely used. To improve the conventional odometry motion model, immediate terrain data can be used. This paper proposes a novel state variable extension (SVE) method to establish a connection between state space variables of a terrain rover by combining terrain point clouds with rover kinematics. The simulation results show that when the 2D state variables (x, y, yaw) are known, the 2D state can be extended to its 3D state (x, y, z, roll, pitch, yaw) with minimal error. The proposed SVE method is employed in a particle filter to determine the 2D state variables, which in turn results in achieving …

第 23 回アストロダイナミクスシンポジウム (2013 年 7 月 29-81 日. 宇宙航空研究開発機構宇宙科学研究所 (JAXA)(ISAS)), 相模原市, 神奈川県

For high scientific return, planetary rovers need to navigate longer distances autonomously and be less dependent on Earth station. Collaboration between rovers can substantially increase the localization accuracy for all the rovers and therefore, opens up opportunity for future exploration missions. This study aims at introducing a decentralized cooperative SLAM technique for two rovers based on EKF-SLAM and FastSLAM algorithms. Several data fusion methods and motion strategies are formulated. Furthermore, a novel SLAM technique called, cooperative positioning SLAM is proposed. The results show that by fusing information, better localization accuracies can be achieved. To validate algorithms a 3D simulation environment has been developed using opensource tools.

In planetary rover missions, there exist severe communication constraints, which make it difficult to control rovers from a long distance (eg from the Earth). Therefore, there is a need to address the important issue of onboard local autonomy to allow deployed rovers to make their own decisions, which contributes to increased scientific return. To achieve this, robot algorithms should be constantly improved and innovative hardware mechanisms should be sought. In this context, simulators play a major role as they can help researchers develop novel technologies even without having access to expensive hardware components. In this paper, the authors present ongoing work on the planetary rover simulation environment that have been developed for testing and validating autonomous navigation of a planetary exploration rover using opensource tools. The simulation system has been developed using Gazebo, an open source 3D multi-robot simulation framework and ROS. The developed simulator provides an essential tool for visualization of the rover functionality for many state-of-art algorithms.

This paper introduces techniques for designing and implementing remotely controllable outdoor robots with the ability to localize itself while traversing in hazardous environments. The robot platform designed is a semi-autonomous tiller modified as required by the application. Localization based on wireless sensor network which has been a research interest of is addressed here with the use of Zigbee protocol. A Kalman filter was applied for the initial gathering of training data. For the improvement of the accuracy of position estimation, a fuzzy inference system is introduced. An extended Kalman filter is applied at the end in order to fuse the encoder data with the filtered RSS data. Results prove that a metric accuracy varying from 2m to 10m is achievable depending on the location of the robot with respect to the sensor nodes.

Developing autonomous mobile robots that can coexist with human in populated environments is still considered a big challenge. To address this problem the authors propose a novel scheme to assist mobile robots by providing localization information externally. This scheme combines the autonomous navigation and target tracking research fields to arrive at a structured assistance system for autonomous mobile robots. In the proposed scheme, the environment is sensed using a laser range finder and a camera based sensor unit. Using the Rao-Blackwellized particle filter technique, the robots that need assistance are continuously tracked. In contrast with conventional laser range finder based tracking systems, the placement of the sensor is changed to a level above average human height and the mobile robots are modified by attaching a cylindrical pole. The experiments showed the validity of the proposed …

第 22 回アストロダイナミクスシンポジウム (2012 年 7 月 30-31 日. 宇宙航空研究開発機構宇宙科学研究所 (JAXA)(ISAS)), 相模原市, 神奈川県

Traditional monoSLAM assumes stationary landmarks making it unable to cope up with dynamic environments where moving objects are present in the scene. This paper presents the parallel implementation of monoSLAM with a set of independent EKF trackers where stationary features and moving features are tracked separately. The difficult problem of detecting moving points from a moving camera is addressed by the epipolar constraint computed by using the measurement information already available with the monoSLAM algorithm. While doing so SLAM measurement outlier rejection is also performed. Results are presented to verify and highlight the advantages of our approach over traditional SLAM.

This paper outlines the problem of outdoor autonomous robot localization for agricultural operations. Vision based outdoor localization system is developed entirely of off-the-shelf components that can accurately guide a field robot in small agricultural fields. Visual odometry using downward faced camera is proposed as a high resolution relative localization system. A stereo vision based range measurement system is also developed and field tested as an absolute localization system that can bound the incremental error caused by the visual odometry system. The extended Kalman filter with measurement gating is implemented using both visual odometry and stereo range measurement data. Field tests demonstrated proposed system is capable of localization of two wheel tractor at a very low cost with acceptable accuracy in small agricultural fields.

Localization Assistance for Mobile Robot Navigation in Real, Populated Environments - Shibaura Institute of Technology Skip to main navigation Skip to search Skip to main content Shibaura Institute of Technology Home Shibaura Institute of Technology Logo English 日本語 Home Profiles Research units Research output Activities Press/Media Prizes Search by expertise, name or affiliation Localization Assistance for Mobile Robot Navigation in Real, Populated Environments Peshala Jayasekara, Takeshi Sasaki, Hideki Hashimoto Functional Control Systems Research output: Contribution to journal › Article › peer-review Overview Original language English Pages (from-to) 2P1-Q13(1)-2P1-Q13(2) Journal Default journal Publication status Published - 2011 May 28 Cite this APA Standard Harvard Vancouver Author BIBTEX RIS Powered by Pure, Scopus & Elsevier Fingerprint Engine™ All content on this site: …

Developingautonomous mobile robots thatcan coexist with human in populatedenvironments isstill considered a bigchallenge, Tb address thisproblemwe propose, a novel scheme to assist mobile robots by providing localizationinformationexternally Thisscheme eombines the autonomous navigation and target tracking research fieldsto arrive at a structured assistance system for autonomous mobile robots. In the proposed scheme, the environment issensed using a laserrange finder and camera based sensor unit. Using Rao-Blackwellizecl panicle filtertechnique, the robots that need assistance are continuously tracked, tncontrast with conventional laserrange finderbasedtrackingsystems, the placementof sensor ischanged to a levelabove average human heightand the mobile robots are medified by attaching a cylindrical pole. The experiments show the validity of the proposed scheme forsimultaneous localization assistance formultiple mobile robots. IXvomobile robots were simultaneously navigated ingiventrajectoriesusing assistance data, successfully

Autonomous personal vehicle systems face many challenges due to complex and unpredictable environments. Thus, much of such platforms' computational resources are wasted on trying to localize in a given environment, slowing down the navigation phase. To overcome this problem we propose intelligent assistance, a novel scheme to assist autonomous mobile platforms by providing localization information externally. In the proposed scheme, the moving targets are detected using a laser range finder and camera based sensor unit and tracked using particle filter based technique. Calculated localization is fed back to the mobile platform as assistance information. The preliminary experiments show the validity of the proposed scheme for mobile robots.

One of the most challenging problems of field robots is self-localization, which involves incremental update of position while in motion. Though wheel based odometry is cheaper to implement its accuracy degrades when wheels slip. In this paper performance of low-cost visual odometry approach is experimented as a feasibility test for field robot localization. We have used a downward-facing camera and tested localization error in view of various parameters such as frame size, frame rate, etc. A FFT-based image registration techniques was utilized to determine the precise translation distances and heading between consecutive frames captured from ground surface. Basic navigation experiments, including a loop-closing test and error propagation were conducted and interesting numerical results have been reported.

Position estimation using wheel odometric systems tends to give rather poor performances for an outdoor four-wheel skid steered mobile robot. Therefore autonomous control of these vehicles is extremely challenging in outdoor environments. This paper describes an outdoor localization system based on visual odometry for skid steered vehicle using forward faced camera and a downward faced camera. Optical flow field data is statistically analyzed to correctly estimate the position of the robot. Kalman Filtering is used to fuse data from two cameras for optimum performance. Also real-time Instantaneous Center of Rotation (ICR) detection using optical flow field data is proposed to calculate the heading angle. Two consumer grade cameras were used and algorithm was tested using open source image processing libraries. The proposed system yielded an acceptable positioning accuracy on short runs in typical …

This paper details the development of a model for a walking tractor also known as power tiller. A complete model for power tiller is developed considering kinematics, dynamics and engine dynamics for autonomous operation. Inputs to the simulation model are left clutch control signal and right clutch control signal. Data from an Inertial Measurement Unit (IMU) attached to the power tiller enables validation of the model using analogue matching and integral least squares. The result of the validation is that the model is a suitable representation of the power tiller. Developed model is intended to use for development of a controller for the remote and autonomous operation at agricultural fields.

In this paper we first analyze the effects of least square based parameter estimation for a autoregressive stochastic model of inertial sensor errors. We then proceed to develop the recursive least squares (RLS) estimation of the autoregressive model parameters and also discuss a fast update method for recursive least square estimation to reduce the computation complexity. This reduction leads to an efficient online dynamic estimation of inertial sensor error model which can then augment a navigation system based on such sensors. Simulation results and actual inertial sensor data are analyzed and it is shown that the RLS estimate can achieve a 20% reduction in forward prediction error as compared to the non-recursive estimate.

Research in the areas of localization, mapping and path planning for single mobile robots has been carried out extensively. Nevertheless, relatively little of its work is applied to multiple robot systems. Moreover, when these robots coexist with human, complex and unpredictable human environments make the above navigational tasks even more challenging. To address this problem we propose intelligent assistance, a novel scheme to assist mobile robots by providing localization information externally. This scheme aims to combine the research fields of autonomous mobile robot navigation and target tracking. The mobile robots are detected using a laser range finder and camera based sensor unit. Using Rao-Blackwellized particle filter technique, the sensory information is integrated in a probabilistic manner and the mobile robots that need assistance are continuously tracked. We maneuver the non-holonomic …

Intelligent Assistance in Localiation for Mobile Robots - Shibaura Institute of Technology Skip to main navigation Skip to search Skip to main content Shibaura Institute of Technology Home Shibaura Institute of Technology Logo English 日本語 Search content at Shibaura Institute of Technology Home Profiles Research units Research output Activities Press/Media Prizes Intelligent Assistance in Localiation for Mobile Robots Peshala G. Jayasekara, Young Eun Song, Takeshi Sasaki, Hideki Hashimoto Functional Control Systems Research output: Contribution to journal › Article › peer-review Overview Original language English Pages (from-to) 317-322 Journal Default journal Publication status Published - 2010 Nov 23 Cite this APA Standard Harvard Vancouver Author BIBTEX RIS Powered by Pure Link opens in a new tab, Scopus Link opens in a new tab & Elsevier Fingerprint Engine™ Link opens in a new tab All …

Intelligent Localization Assistance for Mobile Robots Using Rao-Blackwellized Particle Filter Based Target Tracking - Shibaura Institute of Technology Skip to main navigation Skip to search Skip to main content Shibaura Institute of Technology Home Shibaura Institute of Technology Logo English 日本語 Home Profiles Research units Research output Activities Press/Media Prizes Search by expertise, name or affiliation Intelligent Localization Assistance for Mobile Robots Using Rao-Blackwellized Particle Filter Based Target Tracking Peshala G. Jayasekara, Takeshi Sasaki, Leon Palafox, Hideki Hashimoto Functional Control Systems Research output: Contribution to journal › Article › peer-review Overview Original language English Pages (from-to) 346-349 Journal Default journal Publication status Published - 2010 Nov 26 Cite this APA Standard Harvard Vancouver Author BIBTEX RIS Powered by Pure, Scopus & …

Robust feature tracking is a requirement for many computer vision tasks such as indoor robot navigation. However, indoor scenes are characterized by poorly localizable features. As a result, indoor feature tracking without artificial markers is challenging and remains an attractive problem. We propose to solve this problem by constraining the locations of a large number of nondistinctive features by several planar homographies which are strategically computed using distinctive features. We experimentally show the need for multiple homographies and propose an illumination-invariant local-optimization scheme for motion refinement. The use of a large number of nondistinctive features within the constraints imposed by planar homographies allows us to gain robustness. Also, the lesser computation cost in estimating these nondistinctive features helps to maintain the efficiency of the proposed method. Our local …

This paper presents a development of a holonomic mobile robot with remote operation capability. Most of the currently available holonomic mobile robots find problems in field applications because they has not been adopted to field environment.To address those problems we have develop a holonomic platform with a capability of carrying 30 kg payload. This platform use normal car type wheels and a complex multi-processor control system controls each wheels steering and driving to achieve the correct holonomic motion. We have tested it in indoor and out door environments and it's design , hardware and control is presented in this paper.

• In some vision systems, the knowledge of the environment consist of a grid representation in which each object in the environment was represented by a 2D projection of its volume onto the horizontal plane. Such a representation is usually referred to as an “occupancy map”.

Research & Development of an Autonomous Mobile Robot "Transformer '08 to '09"for Tsukuba Challenge - Shibaura Institute of Technology Skip to main navigation Skip to search Skip to main content Shibaura Institute of Technology Home Shibaura Institute of Technology Logo English 日本語 Search content at Shibaura Institute of Technology Home Profiles Research units Research output Activities Press/Media Prizes Research & Development of an Autonomous Mobile Robot "Transformer '08 to '09"for Tsukuba Challenge Peshala Gehan Jayasekara , Shimon Ajisaka , Sousuke Nakamura , Takeshi Sasaki , Hajime Tamura , Hideki GHashimoto Functional Control Systems Research output: Contribution to journal › Article › peer-review Overview Original language English Pages (from-to) 1382-1384 Journal Default journal Publication status Published - 2009 Dec 26 Cite this APA Standard Harvard Vancouver Author …