Academic literature on the topic 'Autonomous Underwater Vehicles, Underwater robotics, Mobile robots navigation'

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

Purpose The following paper details a “Q&A interview” conducted by Joanne Pransky, Associate Editor of Industrial Robot Journal, to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned successful business leader, regarding the commercialization and challenges of bringing technological inventions to the market while overseeing a company. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr William “Red” Whittaker, Fredkin Research Professor of Robotics, Robotics Institute, Carnegie Mellon University (CMU); CEO of Astrobotic Technology; and President of Workhorse Technologies. Dr Whittaker provides answers to questions regarding the pioneering experiences of some of his technological wonders in land, sea, air, underwater, underground and space. Findings As a child, Dr Whittaker built things and made them work and dreamed about space and robots. He has since then turned his dreams, and those of the world, into realities. Dr Whittaker’s formal education includes a BS degree in civil engineering from Princeton and MS and PhD degrees in civil engineering from CMU. In response to designing a robot to cleanup radioactive material at the Three Mile Island nuclear plant, Dr Whittaker established the Field Robotics Center (FRC) in 1983. He is also the founder of the National Robotics Engineering Center, an operating unit within CMU’s Robotics Institute (RI), the world’s largest robotics research and development organization. Dr Whittaker has developed more than 60 robots, breaking new ground in autonomous vehicles, field robotics, space exploration, mining and agriculture. Dr Whittaker’s research addresses computer architectures for robots, modeling and planning for non-repetitive tasks, complex problems of objective sensing in random and dynamic environments and integration of complete robot systems. His current focus is Astrobotic Technology, a CMU spin-off firm that is developing space robotics technology to support planetary missions. Dr Whittaker is competing for the US$20m Google Lunar XPRIZE for privately landing a robot on the Moon. Originality/value Dr Whittaker coined the term “field robotics” to describe his research that centers on robots in unconstrained, uncontrived settings, typically outdoors and in the full range of operational and environmental conditions: robotics in the “natural” world. The Field Robotics Center has been one of the most successful initiatives within the entire robotics industry. As the Father of Field Robotics, Dr Whittaker has pioneered locomotion technologies, navigation and route-planning methods and advanced sensing systems. He has directed over US$100m worth of research programs and spearheaded several world-class robotic explorations and operations with significant outreach, education and technology commercializations. His ground vehicles have driven thousands of autonomous miles. Dr Whittaker won DARPA’s US$2m Urban Challenge. His Humvees finished second and third in the 2005 DARPA’s Grand race Challenge desert race. Other robot projects have included: Dante II, a walking robot that explored an active volcano; Nomad, which searched for meteorites in Antarctica; and Tugbot, which surveyed a 1,800-acre area of Nevada for buried hazards. Dr Whittaker is a member of the National Academy of Engineering. He is a fellow of the American Association for Artificial Intelligence and served on the National Academy of Sciences Space Studies Board. Dr Whittaker received the Alan Newell Medal for Research Excellence. He received Carnegie Mellon’s Teare Award for Teaching Excellence. He received the Joseph Engelberger Award for Outstanding Achievement in Robotics, the Advancement of Artificial Intelligence’s inaugural Feigenbaum Prize for his contributions to machine intelligence, the Institute of Electrical and Electronics Engineers Simon Ramo Medal, the American Society of Civil Engineers Columbia Medal, the Antarctic Service Medal and the American Spirit Honor Medal. Science Digest named Dr Whittaker one of the top 100 US innovators for his work in robotics. He has been recognized by Aviation Week & Space Technology and Design News magazines for outstanding achievement. Fortune named him a “Hero of US Manufacturing”. Dr Whittaker has advised 26 PhD students, has 16 patents and has authored over 200 publications. Dr Whittaker’s vision is to drive nanobiologics technology to fulfillment and create nanorobotic agents for enterprise on Earth and beyond (Figure 1).

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair, and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new multi-sensing platform, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (Underwater Autonomous Vehicles, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal-processing techniques. We show an example of this approach for monitoring offshore wind turbines, by designing an experimental setup to mimic a wind turbine using a stepper motor and custom-designed acrylic fan blades. We use the distance sensor, which is a Time-of-Flight sensor, to achieve the monitoring process. We use two different approaches for the condition monitoring process: offline and online classification. We tested the offline classification approach using two different communication techniques: serial and Wi-Fi. We performed the online classification approach using two different communication techniques: LoRa and optical. We train our classifier offline and transfer its parameters to the Limpet for online classification. We simulated and classified four different faults in the operation of wind turbines. We tailored a data processing procedure for the gathered data and trained the Limpet to distinguish among each of the functioning states. The results show successful classification using the online approach, where the processing and analysis of the data is done on-board by the microcontroller. By using online classification, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. This work shines light on how robots can perform on-board signal processing and analysis to gain multi-functional sensing capabilities, improve their communication requirements, and monitor the structural health of equipment.