Near minimum time path planning for bearing-only localisation and mapping

The main contribution of this paper is an algorithm for integrating motion planning and simultaneous localisation and mapping (SLAM). Accuracy of the maps and the robot locations computed using SLAM is strongly dependent on the characteristics of the environment, for example feature density, as well as the speed and direction of motion of the robot. Appropriate control of the robot motion is particularly important in bearing-only SLAM, where the information from a moving sensor is essential. In this paper a near minimum time path planning algorithm with a finite planning horizon is proposed for bearing-only SLAM. The objective of the algorithm is to achieve a predefined mapping precision while maintaining acceptable vehicle location uncertainty in the minimum time. Simulation results have shown the effectiveness of the proposed method. © 2005 IEEE

Active SLAM in structured environments

This paper considers the trajectory planning problem for line-feature based SLAM in structured indoor environments. The robot poses and line features are estimated using Smooth and Mapping (SAM) which is found to provide more consistent estimates than the Extended Kalman Filter (EKF). The objective of trajectory planning is to minimise the uncertainty of the estimates and to maximise coverage. Trajectory planning is performed using Model Predictive Control (MPC) with an attractor incorporating long term goals. This planning is demonstrated both in simulation and in a real-time experiment with a Pioneer2DX robot. ©2008 IEEE

Autonomous mobility scooters as assistive tools for the elderly

The aim of this research is to investigate the development of an autonomous navigation system that could be used as an assistive tool for elderly and disabled people in their activities of daily living. The navigation environment is an urban environment and the platform is a Mobility Scooter (MoS). To achieve this aim, a differentially steered MoS was modifed to receive motion commands from a computer and outfitted with onboard sensors that included a Global Positioning System (GPS) receiver and two 2D planar laser range sensors. Perception methods were developed to detect the presence of an outdoor pedestrian walkway. These methods achieved this by processing the range data produced by the laser sensors to identify features that are typically found around walkways like curbs, low vegetation, walls and barriers. A method that utilises GPS localisation information to plan and navigate a route in an outdoor urban environment was also developed. Extensive experimental work was conducted to test the accuracy, repeatability and usefulness of the sensory devices. The developed perception methodologies were evaluated in real world environments while the navigation algorithms were predominantly tested in virtual environments. A navigation system that plans a route in an urban environment and follows it using behaviours arranged in a hierarchy is presented and shown to have the ability to safely navigate an MoS along an outdoor pedestrian path

RobotAssist - A platform for human robot interaction research

This paper presents RobotAssist, a robotic platform designed for use in human robot interaction research and for entry into Robocup@Home competition. The core autonomy of the system is implemented as a component based software framework that allows for integration of operating system independent components, is designed to be expandable and integrates several layers of reasoning. The approaches taken to develop the core capabilities of the platform are described, namely: path planning in a social context, Simultaneous Localisation and Mapping (SLAM), human cue sensing and perception, manipulatable object detection and manipulation

Viewfinder: final activity report

The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources. The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation. The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein

Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion

Agricultural mobile robots have great potential to effectively implement different agricultural tasks. They can save human labour costs, avoid the need for people having to perform risky operations and increase productivity. Automation and advanced sensing technologies can provide up-to-date information that helps farmers in orchard management. Data collected from on-board sensors on a mobile robot provide information that can help the farmer detect tree or fruit diseases or damage, measure tree canopy volume and monitor fruit development. In orchards, trees are natural landmarks providing suitable cues for mobile robot localisation and navigation as trees are nominally planted in straight and parallel rows. This thesis presents a novel tree trunk detection algorithm that detects trees and discriminates between trees and non-tree objects in the orchard using a camera and 2D laser scanner data fusion. A local orchard map of the individual trees was developed allowing the mobile robot to navigate to a specific tree in the orchard to perform a specific task such as tree inspection. Furthermore, this thesis presents a localisation algorithm that does not rely on GPS positions and depends only on the on-board sensors of the mobile robot without adding any artificial landmarks, respective tapes or tags to the trees. The novel tree trunk detection algorithm combined the features extracted from a low cost camera's images and 2D laser scanner data to increase the robustness of the detection. The developed algorithm used a new method to detect the edge points and determine the width of the tree trunks and non-tree objects from the laser scan data. Then a projection of the edge points from the laser scanner coordinates to the image plane was implemented to construct a region of interest with the required features for tree trunk colour and edge detection. The camera images were used to verify the colour and the parallel edges of the tree trunks and non-tree objects. The algorithm automatically adjusted the colour detection parameters after each test which was shown to increase the detection accuracy. The orchard map was constructed based on tree trunk detection and consisted of the 2D positions of the individual trees and non-tree objects. The map of the individual trees was used as an a priority map for mobile robot localisation. A data fusion algorithm based on an Extended Kalman filter was used for pose estimation of the mobile robot in different paths (midway between rows, close to the rows and moving around trees in the row) and different turns (semi-circle and right angle turns) required for tree inspection tasks. The 2D positions of the individual trees were used in the correction step of the Extended Kalman filter to enhance localisation accuracy. Experimental tests were conducted in a simulated environment and a real orchard to evaluate the performance of the developed algorithms. The tree trunk detection algorithm was evaluated under two broad illumination conditions (sunny and cloudy). The algorithm was able to detect the tree trunks (regular and thin tree trunks) and discriminate between trees and non-tree objects with a detection accuracy of 97% showing that the fusion of both vision and 2D laser scanner technologies produced robust tree trunk detection. The mapping method successfully localised all the trees and non-tree objects of the tested tree rows in the orchard environment. The mapping results indicated that the constructed map can be reliably used for mobile robot localisation and navigation. The localisation algorithm was evaluated against the logged RTK-GPS positions for different paths and headland turns. The average of the RMS of the position error in x, y coordinates and Euclidean distance were 0.08 m, 0.07 m and 0.103 m respectively, whilst the average of the RMS of the heading error was 3:32°. These results were considered acceptable while driving along the rows and when executing headland turns for the target application of autonomous mobile robot navigation and tree inspection tasks in orchards