A Framework for Collaborative Multi-task, Multi-robot Missions

Robotics is a transformative technology that will empower our civilization for a new scale of human endeavors. Massive scale is only possible through the collaboration of individual or groups of robots. Collaboration allows specialization, meaning a multirobot system may accommodate heterogeneous platforms including human partners. This work develops a unified control architecture for collaborative missions comprised of multiple, multi-robot tasks. Using kinematic equations and Jacobian matrices, the system states are transformed into alternative control spaces which are more useful for the designer or more convenient for the operator. The architecture allows multiple tasks to be combined, composing tightly coordinated missions. Using this approach, the designer is able to compensate for non-ideal behavior in the appropriate space using whatever control scheme they choose. This work presents a general design methodology, including analysis techniques for relevant control metrics like stability, responsiveness, and disturbance rejection, which were missing in prior work. Multiple tasks may be combined into a collaborative mission. The unified motion control architecture merges the control space components for each task into a concise federated system to facilitate analysis and implementation. The task coordination function defines task commands as functions of mission commands and state values to create explicit closed-loop collaboration. This work presents analysis techniques to understand the effects of cross-coupling tasks. This work analyzes system stability for the particular control architecture and identifies an explicit condition to ensure stable switching when reallocating robots. We are unaware of any other automated control architectures that address large-scale collaborative systems composed of task-oriented multi-robot coalitions where relative spatial control is critical to mission performance. This architecture and methodology have been validated in experiments and in simulations, repeating earlier work and exploring new scenarios and. It can perform large-scale, complex missions via a rigorous design methodology

Behavior Flexibility for Autonomous Unmanned Aerial Systems

Autonomous unmanned aerial systems (UAS) could supplement and eventually subsume a substantial portion of the mission set currently executed by remote pilots, making UAS more robust, responsive, and numerous than permitted by teleoperation alone. Unfortunately, the development of robust autonomous systems is difficult, costly, and time-consuming. Furthermore, the resulting systems often make little reuse of proven software components and offer limited adaptability for new tasks. This work presents a development platform for UAS which promotes behavioral flexibility. The platform incorporates the Unified Behavior Framework (a modular, extensible autonomy framework), the Robotic Operating System (a RSF), and PX4 (an open- source flight controller). Simulation of UBF agents identify a combination of reactive robotic control strategies effective for small-scale navigation tasks by a UAS in the presence of obstacles. Finally, flight tests provide a partial validation of the simulated results. The development platform presented in this work offers robust and responsive behavioral flexibility for UAS agents in simulation and reality. This work lays the foundation for further development of a unified autonomous UAS platform supporting advanced planning algorithms and inter-agent communication by providing a behavior-flexible framework in which to implement, execute, extend, and reuse behaviors

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

Ant colonies: building complex organizations with minuscule brains and no leaders

Thus far the articles in the series JOD calls the “Organization Zoo” have employed the notion of a “zoo” metaphorically to describe an array of human institutions. Here we take the term literally to consider the design of the most complex organizations in the living world beside those of humans, a favorite of insect zoos around the world: ant colonies. We consider individuality and group identity in the functioning of ant organizations; advantages of a flat organization without hierarchies or leaders; self-organization; direct and indirect communication; job specialization; labor coordination; and the role of errors in innovation. The likely value and limitations of comparing ant and human organizations are briefly examined

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche