Mobile Robot Localization Using Bar Codes as Artificial Landmarks

"Where am I' is the central question in mobile robot navigation. Robust and reliable localization are of vital importance for an autonomous mobile robot because the ability to constantly monitor its position in an unpredictable, unstructured, and dynamic environment is the essential prerequisite to build up and/or maintain environmental maps consistently and to perform path planning. Thus, selflocalization as precondition for goal-oriented behavior is a fundamental property an autonomous mobile robot needs to be equipped with. Accurate, flexible and low-cost localization are important issues for achieving autonomous and cooperative motions of mobile robots. Mobile robots usually perform self-localization by combining position estimates obtained from odometry or inertial navigation with external sensor data. The objective of the thesis is to present a pragmatic idea which utilizes a camera-based bar code recognition technique in order to support mobile robot localization In indoor environments. The idea is to further improve already existing localization capabilities, obtained from dead-reckoning, by furnishing relevant environmental spots such as doors, stairs, etc. with semantic information. In order to facilitate the detection of these landmarks the employment of bar codes is proposed. The important contribution of the thesis is the designing of two software programs. The first program is the bar code generation program which is able to generate five types of bar code labels that play a major role in the proposed localization method. The second program is the bar code recognition program that analyzes image files looking for a bar code label. If a label is found the program recognizes it and display both the information it contains and its coding type. Results concerning the generation of five types of bar code labels which are code 2 of 5, code 3 of9 , codabar code, code 128 and code 2 of 5 interleaved and the detection and identification of these labels from image files are obtained. In conclusion the thesis proposes a solution to mobile robot self-localization problem, which is the central significant for implementing an autonomous mobile robot, by utilizing a camera-based bar code recognition technique to support the basic localization capabilities obtained from a dead-reckoning method in an indoor environment

3D Perception Based Lifelong Navigation of Service Robots in Dynamic Environments

Lifelong navigation of mobile robots is to ability to reliably operate over extended periods of time in dynamically changing environments. Historically, computational capacity and sensor capability have been the constraining factors to the richness of the internal representation of the environment that a mobile robot could use for navigation tasks. With affordable contemporary sensing technology available that provides rich 3D information of the environment and increased computational power, we can increasingly make use of more semantic environmental information in navigation related tasks.A navigation system has many subsystems that must operate in real time competing for computation resources in such as the perception, localization, and path planning systems. The main thesis proposed in this work is that we can utilize 3D information from the environment in our systems to increase navigational robustness without making trade-offs in any of the real time subsystems. To support these claims, this dissertation presents robust, real world 3D perception based navigation systems in the domains of indoor doorway detection and traversal, sidewalk-level outdoor navigation in urban environments, and global localization in large scale indoor warehouse environments.The discussion of these systems includes methods of 3D point cloud based object detection to find respective objects of semantic interest for the given navigation tasks as well as the use of 3D information in the navigational systems for purposes such as localization and dynamic obstacle avoidance. Experimental results for each of these applications demonstrate the effectiveness of the techniques for robust long term autonomous operation

An intelligent multi-floor mobile robot transportation system in life science laboratories

In this dissertation, a new intelligent multi-floor transportation system based on mobile robot is presented to connect the distributed laboratories in multi-floor environment. In the system, new indoor mapping and localization are presented, hybrid path planning is proposed, and an automated doors management system is presented. In addition, a hybrid strategy with innovative floor estimation to handle the elevator operations is implemented. Finally the presented system controls the working processes of the related sub-system. The experiments prove the efficiency of the presented system

Designing Automated Guided Vehicle Using Image Sensor

Automated guided vehicles (AGV) are one of the greatest achievements in the field of mobile robotics. Without continuous guidance from a human they navigate on desired path thus completing various tasks, e.g. fork lifting objects, towing, and product transportation inside manufacturing firm. Their development can revolutionize the world in the sense of fool proof navigation and accurate maneuvering. Though most of the presently the AGV work in a retrofitted environment, work space as they require some identification for tracing their guide path, works are going on developing such AGVs which are dynamic in sense of navigation and whose locomotion is not limited to just a retrofitted workspace. The aim of this work was developing such a natural feature AGV which takes visual input in the form images and gains detailed object, obstacle, landmark, identification to decide its guide path. The AGV set up developed, used a commercial electric motor based car ‘Reva i’, as chassis which was fitted with camera to take real time input and resolve it using segmentation and image processing techniques to reach a decision of driving controls. These controls were communicated, or better imparted to vehicle using parallel port of computer to servo motors, which in turn controlled the motion of vehicle. The work was focused more on dynamically controlling the vehicle using refinement of driving mechanism (hardware), however it could be assisted using better segmentation and obstacle detection algorithm. All the retro-fitting and codes were developed in such a way that they could be improved at any stage of time. The results could be enhanced if a better stereoscopic camera were used with a dedicated cpu with better graphics capability. This vision based AGV can revolutionize the mobile robotics world, including systems where a human driver is required to take decisions on the basis of visualized condition