Control for Localization and Visibility Maintenance of an Independent Agent using Robotic Teams

Given a non-cooperative agent, we seek to formulate a control strategy to enable a team of robots to localize and track the agent in a complex but known environment while maintaining a continuously optimized line-of-sight communication chain to a fixed base station. We focus on two aspects of the problem. First, we investigate the estimation of the agent\u27s location by using nonlinear sensing modalities, in particular that of range-only sensing, and formulate a control strategy based on improving this estimation using one or more robots working to independently gather information. Second, we develop methods to plan and sequence robot deployments that will establish and maintain line-of-sight chains for communication between the independent agent and the fixed base station using a minimum number of robots. These methods will lead to feedback control laws that can realize this plan and ensure proper navigation and collision avoidance

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems

Efficient Mission Planning for Robot Networks in Communication Constrained Environments

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes

Personnel recovery operations for special operations forces in urban environments modeling successful overt and clandestine methods of recovery

This thesis presents two prescriptive models for approaching challenges to special operations forces with regard to personnel recovery in an urban environment. It begins by developing a model for overt recovery methods, using McRaven's model of Special Operations as the foundation. This model is then tested against three different case studies from operations in Mogadishu, Somalia in 1993. The original six principles proposed by McRaven are complimented with four newly-prescribed principles that account for the interactions of the isolated personnel. Following this analysis, a nonconventional assisted recovery model is presented for clandestine personnel recovery methods. This model borrows the relative superiority concept from McRaven's theory, but proposes six different principles. This model is evaluated using three case studies from the World War II era through Operation IRAQI FREEDOM. These cases support the idea that while the urban operational environment may vary across time and space, the principles supporting successful personnel recovery operations endure.http://archive.org/details/personnelrecover109451159Major, United States ArmyMajor, United States Air ForceApproved for public release; distribution is unlimited

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Active Object Classification from 3D Range Data with Mobile Robots

This thesis addresses the problem of how to improve the acquisition of 3D range data with a mobile robot for the task of object classification. Establishing the identities of objects in unknown environments is fundamental for robotic systems and helps enable many abilities such as grasping, manipulation, or semantic mapping. Objects are recognised by data obtained from sensor observations, however, data is highly dependent on viewpoint; the variation in position and orientation of the sensor relative to an object can result in large variation in the perception quality. Additionally, cluttered environments present a further challenge because key data may be missing. These issues are not always solved by traditional passive systems where data are collected from a fixed navigation process then fed into a perception pipeline. This thesis considers an active approach to data collection by deciding where is most appropriate to make observations for the perception task. The core contributions of this thesis are a non-myopic planning strategy to collect data efficiently under resource constraints, and supporting viewpoint prediction and evaluation methods for object classification. Our approach to planning uses Monte Carlo methods coupled with a classifier based on non-parametric Bayesian regression. We present a novel anytime and non-myopic planning algorithm, Monte Carlo active perception, that extends Monte Carlo tree search to partially observable environments and the active perception problem. This is combined with a particle-based estimation process and a learned observation likelihood model that uses Gaussian process regression. To support planning, we present 3D point cloud prediction algorithms and utility functions that measure the quality of viewpoints by their discriminatory ability and effectiveness under occlusion. The utility of viewpoints is quantified by information-theoretic metrics, such as mutual information, and an alternative utility function that exploits learned data is developed for special cases. The algorithms in this thesis are demonstrated in a variety of scenarios. We extensively test our online planning and classification methods in simulation as well as with indoor and outdoor datasets. Furthermore, we perform hardware experiments with different mobile platforms equipped with different types of sensors. Most significantly, our hardware experiments with an outdoor robot are to our knowledge the first demonstrations of online active perception in a real outdoor environment. Active perception has broad significance in many applications. This thesis emphasises the advantages of an active approach to object classification and presents its assimilation with a wide range of robotic systems, sensors, and perception algorithms. By demonstration of performance enhancements and diversity, our hope is that the concept of considering perception and planning in an integrated manner will be of benefit in improving current systems that rely on passive data collection

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

Reactive exploration with self-reconfigurable systems

Modular self-reconfigurable robots (MSR) are robots composed of modules that translate over one another to permit reconfiguration and locomotion. This construction allows them to traverse a broader range of environments than legged or wheeled robots. MSR also posses the ability to split apart to parallelize their efforts or combine with each other to produce a larger robot capable of navigating more diverse terrain. In this paper we discuss a state-based reactive architecture for the distributed control of cooperative MSR teams in unknown environments. The MSR use local sensory data from the environment and a model of the team to select their actions. These actions include selecting a destination, aborting a route to a destination, splitting into two separate robots, and combining with another robot. In simulation, team-configuration, environmental complexity, and behavioral parameters are varied to discern the most effective circumstances for the architecture and MSR. Our results show that the best configuration of the system is highly dependent on the environment