Deep Learning Based Methods for Outdoor Robot Localization and Navigation

The number of elderly people is increasing around the globe. In order to support the growing of ageing society, mobile robot is one of viable choices for assisting the elders in their daily activities. These activities happen in any places, either indoor or outdoor. Although outdoor activities benefit the elders in many ways, outdoor environments contain difficulties from their unpredictable natures. Mobile robots for supporting humans in outdoor environments must automatically traverse through various difficulties in the environments using suitable navigation systems.Core components of mobile robots always include the navigation segments. Navigation system helps guiding the robot to its destination where it can perform its designated tasks. There are various tools to be chosen for navigation systems. Outdoor environments are mostly open for conventional navigation tools such as Global Positioning System (GPS) devices. In this thesis three systems for localization and navigation of mobile robots based on visual data and deep learning algorithms are proposed. The first localization system is based on landmark detection. The Faster Regional-Convolutional Neural Network (Faster R-CNN) detects landmarks and signs in the captured image. A Feed-Forward Neural Network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The second system is the navigation system based on visual data and a deep reinforcement learning algorithm called Deep Q Network (DQN). The employed DQN automatically guides the mobile robot with visual data in the form of images, which received from the only Universal Serial Bus (USB) camera that attached to the robot. DQN consists of a deep neural network called convolutional neural network (CNN), and a reinforcement learning algorithm named Q-Learning. It can make decisions with visual data as input, using experiences from consequences of trial-and-error attempts. Our DQN agents are trained in the simulation environments provided by a platform based on a First-Person Shooter (FPS) game named ViZDoom. Simulation is implemented for training to avoid any possible damage on the real robot during trial-and-error process. Perspective from the simulation is the same as if a camera is attached to the front of the mobile robot. There are many differences between the simulation and the real world. We applied a markerbased Augmented Reality (AR) algorithm to reduce differences between the simulation and the world by altering visual data from the camera with resources from the simulation.The second system is assigned the task of simple navigation to the robot, in which the starting location is fixed but the goal location is random in the designated zone. The robot must be able to detect and track the goal object using a USB camera as its only sensor. Once started, the robot must move from its starting location to the designated goal object. Our DQN navigation method is tested in the simulation and on the real robot. Performances of our DQN are measured quantitatively via average total scores and the number of success navigation attempts. The results show that our DQN can effectively guide a mobile robot to the goal object of the simple navigation tasks, for both the simulation and the real world.The third system employs a Transfer Learning (TL) strategy to reduce training time and resources required for the training of newly added tasks of DQN agents. The new task is the task of reaching the goal while also avoiding obstacles at the same time. Additionally, the starting and the goal locations are all random within the specified areas. The employed transfer learning strategy uses the whole network of the DQN agent trained for the first simple navigation task as the base for training the DQN agent for the second task. The training in our TL strategy decrease the exploration factor, which cause the agent to rely on the existing knowledge from the base network more than randomly selecting actions during the training. It results in the decreased training time, in which optimal solutions can be found faster than training from scratch.We evaluate performances of our TL strategy by comparing the DQN agents trained with our TL at different exploration factor values and the DQN agent trained from scratch. Additionally, agents trained from our TL are trained with the decreased number of episodes to extensively display performances of our TL agents. All DQN agents for the second navigation task are tested in the simulation to avoid any possible and uncontrollable damages from the obstacles. Performances are measured through success attempts and average total scores, same as in the first navigation task. Results show that DQN agents trained via the TL strategy can greatly outperform the agent trained from scratch, despite the lower number of training episodes.博士(工学)法政大学 (Hosei University

Autonomous diode laser weeding mobile robot in cotton field using deep learning, visual servoing and finite state machine

Small autonomous robotic platforms can be utilized in agricultural environments to target weeds in their early stages of growth and eliminate them. Autonomous solutions reduce the need for labor, cut costs, and enhance productivity. To eliminate the need for chemicals in weeding, and other solutions that can interfere with the crop’s growth, lasers have emerged as a viable alternative. Lasers can precisely target weed stems, effectively eliminating or stunting their growth. In this study an autonomous robot that employs a diode laser for weed elimination was developed and its performance in removing weeds in a cotton field was evaluated. The robot utilized a combination of visual servoing for motion control, the Robotic operating system (ROS) finite state machine implementation (SMACH) to manage its states, actions, and transitions. Furthermore, the robot utilized deep learning for weed detection, as well as navigation when combined with GPS and dynamic window approach path planning algorithm. Employing its 2D cartesian arm, the robot positioned the laser diode attached to a rotating pan-and-tilt mechanism for precise weed targeting. In a cotton field, without weed tracking, the robot achieved an overall weed elimination rate of 47% in a single pass, with a 9.5 second cycle time per weed treatment when the laser diode was positioned parallel to the ground. When the diode was placed at a 10°downward angle from the horizontal axis, the robot achieved a 63% overall elimination rate on a single pass with 8 seconds cycle time per weed treatment. With the implementation of weed tracking using DeepSORT tracking algorithm, the robot achieved an overall weed elimination rate of 72.35% at 8 seconds cycle time per weed treatment. With a strong potential for generalizing to other crops, these results provide strong evidence of the feasibility of autonomous weed elimination using low-cost diode lasers and small robotic platforms

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications