Model Reference Adaptive Control of Quadrotor UAVs: A Neural Network Perspective

Adaptive models and online learning are two equivalent topics under different umbrellas of research – control systems and machine learning. This chapter will tackle one such application of a neural network-based model reference adaptive controller on a quadrotor unmanned aerial vehicle while stating the general principles behind each design decision so the knowledge can be generalized to other practical applications. The application-oriented presentation of this chapter will run parallel to most research and development processes in the field, where the physical system or a simulator is usually available and a simple control system (such as PID) has already been implemented as a baseline. The black-box nature of a neural network can truly be leveraged to improve performance after reading this chapter. Several practical considerations when approaching such a problem have been discussed together with their general and implemented solutions. The simulation results for the problem have been presented to demonstrate the success of this control strategy

GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search

Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OOIs rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75,000 sq. m. Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline.Comment: 7 pages, 5 figures, 1 table, for associated video see: https://youtu.be/K0jkzdQ_j2E , to appear in International Conference on Robotics and Automation (ICRA) 202

Citation sentence reuse behavior of scientists: A case study on massive bibliographic text dataset of computer science

Our current knowledge of scholarly plagiarism is largely based on the similarity between full text research articles. In this paper, we propose an innovative and novel conceptualization of scholarly plagiarism in the form of reuse of explicit citation sentences in scientific research articles. Note that while full-text plagiarism is an indicator of a gross-level behavior, copying of citation sentences is a more nuanced micro-scale phenomenon observed even for well-known researchers. The current work poses several interesting questions and attempts to answer them by empirically investigating a large bibliographic text dataset from computer science containing millions of lines of citation sentences. In particular, we report evidences of massive copying behavior. We also present several striking real examples throughout the paper to showcase widespread adoption of this undesirable practice. In contrast to the popular perception, we find that copying tendency increases as an author matures. The copying behavior is reported to exist in all fields of computer science; however, the theoretical fields indicate more copying than the applied fields