A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles

Full version unavailable due to 3rd party copyright restrictions.The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.EOARD (European Office of Aerospace Research & Development), euCognitio

Bioinspired Control of Rudderless Morphing UAVs

Morphing to seamlessly alter aircraft geometry for either multi-mission or adaptive fly-by-feel flight has recently become an emerging field of research. With the added benefits of tailored aerodynamics, an aircraft no longer needs to be designed to suit a single cruise flight condition. This is particularly useful for small Unmanned Aerial Vehicles (UAVs) which, like birds and insects, tend to operate at lower altitudes and even in urban environments where the flow can frequently change drastically. The primary objective of this research is to investigate morphing applications for rudderless UAVs, which have seldom been studied prior to this point, through bioinspiration. As natural fliers undergo multi-scale low-altitude morphing to adapt to changes in either flight objective or aerodynamic conditions, they are prime subjects for investigation. This is accomplished through both analytical aerodynamic modeling, and experimental design and investigation of novel morphing actuators using Macro Fiber Composites (MFCs). Using these smart material actuators, complex shape change such as spanwise camber morphing and three-dimensional bending-twisting coupling is achieved. This dissertation presents three main contributions to the field of morphing aircraft. The first contribution is an analytical derivation that assesses the impact of scale and altitude on flight. This is aimed at justifying the need for morphing technologies particularly at the UAV scale by assessing the impact of winds on flight velocity and direction. More specifically, both a steady wind and a quasi-steady sharp-edge cross wind were assessed to characterize the response, and showed that low-altitude fliers are prone to drastic changes in flight path, acceleration, and sensitivity with respect to winds. A nonlinear Lifting Line Theory (LLT) model was also developed specifically for spanwise morphing aircraft. With this model, the spanwise geometry of a morphing wing can be tailored and optimized to achieve a desired aerodynamic outcome. As this model is capable of characterizing nonlinear aerodynamics, the spanwise wing geometry is tailored to recover from stall. A comprehensive analysis of possible adaptation scenarios is also conducted to characterize the limitations of the system and demonstrated excellent recovery capabilities of the spanwise morphing wing. Lastly, a novel bioinspired tail actuator is developed for multifunctional pitch and yaw control using MFCs. Two Finite Element Method (FEM) models are compared to determine both an appropriate method of modeling MFC actuators with custom non-rectangular geometries and fiber orientations, and the optimal fiber orientation to obtain adequate transverse and out-of-plane displacements. The optimized actuator was integrated into a bioinspired aircraft for wind tunnel testing. Experimental investigation was geared towards quantifying both pitch and yaw response of the actuator with respect to both changes in angle of attack and sideslip.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145843/1/llgamble_1.pd

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp