Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Anticipating the Effects of Economic Displacement in Marine Space with Agent Based Models

As marine space is managed into appropriate resource use areas, it is inevitable that some is allocated towards a mutually exclusive spatial activity. This exclusion results in displacement that has real economic consequences. When a wind energy area is placed in coastal waters, navigable space is reduced and vessels are displaced from their former routes. The USCG is concerned that re-routing will result in vessels navigating within closer proximity than they would otherwise in an open ocean scenario, and fear that this will increase the risk of vessel collision (USCG 2016). They recommend research into tools that are capable of predicting changes in vessel traffic patterns (USCG 2016). Agent based models are a method capable of predicting these traffic patterns, and are composed individual, autonomous goal directed software objects that form emergent behavior of interest. Agents are controlled by a simple behavioral rule, they must arrive at their destination without colliding with an obstacle or other vessel. They enforce this rule with the gravitational potential that exists between two objects. Attractive forces pull each agent towards their destination, while repulsive forces push them away from danger. We validated simulated vessel tracks against real turning circle test data, tested for the presence of chaotic systems, developed metrics to assess transportation costs, and applied the method to assess a wind energy area located outside of the entrance to the Port of New York and New Jersey

A Biomimetic, Energy-Harvesting, Obstacle-Avoiding, Path-Planning Algorithm for UAVs

This dissertation presents two new approaches to energy harvesting for Unmanned Aerial Vehicles (UAV). One method is based on the Potential Flow Method (PFM); the other method seeds a wind-field map based on updraft peak analysis and then applies a variant of the Bellman-Ford algorithm to find the minimum-cost path. Both methods are enhanced by taking into account the performance characteristics of the aircraft using advanced performance theory. The combined approach yields five possible trajectories from which the one with the minimum energy cost is selected. The dissertation concludes by using the developed theory and modeling tools to simulate the flight paths of two small Unmanned Aerial Vehicles (sUAV) in the 500 kg and 250 kg class. The results show that, in mountainous regions, substantial energy can be recovered, depending on topography and wind characteristics. For the examples presented, as much as 50% of the energy was recovered for a complex, multi-heading, multi-altitude, 170 km mission in an average wind speed of 9 m/s. The algorithms constitute a Generic Intelligent Control Algorithm (GICA) for autonomous unmanned aerial vehicles that enables an extraction of atmospheric energy while completing a mission trajectory. At the same time, the algorithm automatically adjusts the flight path in order to avoid obstacles, in a fashion not unlike what one would expect from living organisms, such as birds and insects. This multi-disciplinary approach renders the approach biomimetic, i.e. it constitutes a synthetic system that “mimics the formation and function of biological mechanisms and processes.

Path planning in time dependent flows using level set methods

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 167-177).Autonomous underwater vehicles such as gliders have emerged as valuable scientific platforms due to their increasing uses in several oceanic applications, ranging from security, acoustic surveillance and military reconnaissance to collection of ocean data at specific locations for ocean prediction, monitoring and dynamics investigation. Gliders exhibit high levels of autonomy and are ideal for long range missions. As these gliders become more reliable and affordable, multi-vehicle coordination and sampling missions are expected to become very common in the near future. This endurance of gliders however, comes at an expense of being susceptible to typical coastal ocean currents. Due to the physical limitations of underwater vehicles and the highly dynamic nature of the coastal ocean, path planning to generate safe and fast vehicle trajectories becomes crucial for their successful operation. As a result, our motivation in this thesis is to develop a computationally efficient and rigorous methodology that can predict the time-optimal paths of underwater vehicles navigating in continuous, strong and dynamic ow-fields. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid ow currents to minimize their travel time. In this thesis, we fist review existing path planning methods and discuss their advantages and drawbacks. Then, we discuss the theory of level set methods and their utility in solving front tracking problems. Then, we present a rigorous (partial differential equation based) methodology based on the level set method, which can compute time-optimal paths of swarms of underwater vehicles, obviating the need for any heuristic control based approaches. We state and prove a theorem, along with several corollaries, that forms the foundation of our approach for path planning. We show that our algorithm is computationally efficient - the computational cost grows linearly with the number of vehicles and geometrically with spatial directions. We illustrate the working and capabilities of our path planning algorithm by means of a number of applications. First, we validate our approach through simple benchmark applications, and later apply our methodology to more complex, realistic and numerically simulated ow-fields, which include eddies, jets, obstacles and forbidden regions. Finally, we extend our methodology to solve problems of coordinated motion of multiple vehicles in strong dynamic ow-fields. Here, coordination refers to maintenance of specific geometric patterns by the vehicles. The level-set based control scheme that we derive is shown to provide substantial advantages to a local control approach. Specifically, the illustrations show that the resulting coordinated vehicle motions can maintain specific patterns in dynamic flow fields with strong and complex spatial gradients.by Sri Venkata Tapovan Lolla.S.M

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Learning Reach-to-Grasp Motions From Human Demonstrations

Reaching over to grasp an item is arguably the most commonly used motor skill by humans. Even under sudden perturbations, humans seem to react rapidly and adapt their motion to guarantee success. Despite the apparent ease and frequency with which we use this ability, a complete understanding of the underlying mechanisms cannot be claimed. It is partly due to such incomplete knowledge that adaptive robot motion for reaching and grasping under perturbations is not perfectly achieved. In this thesis, we take the discriminative approach for modelling trajectories of reach-to-grasp motion from expert demonstrations. Throughout this thesis, we will employ time-independent (autonomous) flow based representations to learn reactive motion controllers which can then be ported onto robots. This thesis is divided into three main parts. The first part is dedicated to biologically inspired modelling of reach-to-grasp motions with respect to the hand-arm coupling. We build upon previous work in motion modelling using autonomous dynamical systems (DS) and present a coupled dynamical system (CDS) model of these two subsystems. The coupled model ensures satisfaction of the constraints between the hand and the arm subsystems which are critical to the success of a reach-to-grasp task. Moreover, it reduces the complexity of the overall motion planning problem as compared to considering a combined problem for the hand and the arm motion. In the second part we extend the CDS approach to incorporate multiple grasping points. Such a model is beneficial due to the fact that many daily life objects afford multiple grasping locations on their surface. We combine a DS based approach with energy-function learning to learn a multiple attractor dynamical system where the attractors are mapped to the desired grasping points. We present the Augmented-SVM (ASVM) model that combines the classical SVM formulation with gradient constraints arising from the energy function to learn the desired dynamical function for motion generation. In the last part of this thesis, we address the problem of inverse-kinematics and obstacle avoidance by combining our flow-based motion generator with global configuration-space planners. We claim that the two techniques complement each other. On one hand, the fast reactive nature of our flow based motion generator can used to guide the search of a randomly exploring random tree (RRT) based global planner. On the other hand, global planners can efficiently handle arbitrary obstacles and avoid local minima present in the dynamical function learned from demonstrations. We show that combining the information from demonstrations with global planning in the form of a energy-map considerably decreases the computational complexity of state-of-the-art sampling based planners. We believe that this thesis has the following contributions to Robotics and Machine Learning. First, we have developed algorithms for fast and adaptive motion generation for reach-grasp motions. Second, we formulated an extension to the classical SVM formulation that takes into account the gradient information from data. We showed that instead of being limited as a classifier or a regressor, the SVM framework can be used as a more general function approximation technique. Lastly, we have combined our local methods with global approaches for planning to achieve arbitrary obstacle avoidance and considerable reduction in the computation complexity of the global planners

Advanced Path Planning and Collision Avoidance Algorithms for UAVs

The thesis aims to investigate and develop innovative tools to provide autonomous flight capability to a fixed-wing unmanned aircraft. Particularly it contributes to research on path optimization, tra jectory tracking and collision avoidance with two algorithms designed respectively for path planning and navigation. The complete system generates the shortest path from start to target avoiding known obstacles represented on a map, then drives the aircraft to track the optimum path avoiding unpredicted ob jects sensed in flight. The path planning algorithm, named Kinematic A*, is developed on the basis of graph search algorithms like A* or Theta* and is meant to bridge the gap between path-search logics of these methods and aircraft kinematic constraints. On the other hand the navigation algorithm faces concurring tasks of tra jectory tracking and collision avoidance with Nonlinear Model Predictive Control. When A* is applied to path planning of unmanned aircrafts any aircraft kinematics is taken into account, then practicability of the path is not guaranteed. Kinematic A* (KA*) generates feasible paths through graph-search logics and basic vehicle characteristics. It includes a simple aircraft kinematic-model to evaluate moving cost between nodes of tridimensional graphs. Movements are constrained with minimum turning radius and maximum rate of climb. Furtermore, separation from obstacles is imposed, defining a volume around the path free from obstacles (tube-type boundaries). Navigation is safe when the tracking error does not exceed this volume. The path-tracking task aims to link kinematic information related to desired aircraft positions with dynamic behaviors to generate commands that minimize the error between reference and real tra jectory. On the other hand avoid obstacles in flight is one of the most challenging tasks for autonomous aircrafts and many elements must be taken into account in order to implement an effective collision avoidance maneuver. Second part of the thesis describes a Nonlinear Model Predictive Control (NMPC) application to cope with collision avoidance and path tracking tasks. First contribution is the development of a navigation system able to match concurring problems: track the optimal path provided with KA* and avoid unpredicted obstacles detected with sensors. Second Contribution is the Sense & Avoid (S&A) technique exploiting spherical camera and visual servoing control logics

NASA Tech Briefs, August 1992

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