A Hybrid Multi-Robot Control Architecture

Multi-robot systems provide system redundancy and enhanced capability versus single robot systems. Implementations of these systems are varied, each with specific design approaches geared towards an application domain. Some traditional single robot control architectures have been expanded for multi-robot systems, but these expansions predominantly focus on the addition of communication capabilities. Both design approaches are application specific and limit the generalizability of the system. This work presents a redesign of a common single robot architecture in order to provide a more sophisticated multi-robot system. The single robot architecture chosen for application is the Three Layer Architecture (TLA). The primary strength of TLA is in the ability to perform both reactive and deliberative decision making, enabling the robot to be both sophisticated and perform well in stochastic environments. The redesign of this architecture includes incorporation of the Unified Behavior Framework (UBF) into the controller layer and an addition of a sequencer-like layer (called a Coordinator) to accommodate the multi-robot system. These combine to provide a robust, independent, and taskable individual architecture along with improved cooperation and collaboration capabilities, in turn reducing communication overhead versus many traditional approaches. This multi-robot systems architecture is demonstrated on the RoboCup Soccer Simulator showing its ability to perform well in a dynamic environment where communication constraints are high

Coalition Formation under Uncertainty

Many multiagent systems require allocation of agents to tasks in order to ensure successful task execution. Most systems that perform this allocation assume that the quantity of agents needed for a task is known beforehand. Coalition formation approaches relax this assumption, allowing multiple agents to be dynamically assigned. Unfortunately, many current approaches to coalition formation lack provisions for uncertainty. This prevents application of coalition formation techniques to complex domains, such as real-world robotic systems and agent domains where full state knowledge is not available. Those that do handle uncertainty have no ability to handle dynamic addition or removal of agents from the collective and they constrain the environment to limit the sources of uncertainty. A modeling approach and algorithm for coalition formation is presented that decreases the collective\u27s dependence on knowing agent types. The agent modeling approach enforces stability, allows for arbitrary expansion of the collective, and serves as a basis for calculation of individual coalition payoffs. It explicitly captures uncertainty in agent type and allows uncertainty in coalition value and agent cost, and no agent in the collective is required to perfectly know another agents type. The modeling approach is incorporated into a two part algorithm to generate, evaluate, and join stable coalitions for task execution. A comparison with a prior approach designed to handle uncertainty in agent type shows that the protocol not only provides greater flexibility, but also handles uncertainty on a greater scale. Additional results show the application of the approach to real-world robotics and demonstrate the algorithm\u27s scalability. This provides a framework well suited to decentralized task allocation in general collectives

Fusion: A Framework for Human Interaction with Flexible-Adaptive Automation Across Multiple Unmanned Systems

Future unmanned systems operators and heterogeneous unmanned systems must be able to work as agile synchronous teams to complete tactical reconnaissance, surveillance, and target acquisition related missions requiring the use of automation to assist the human operator. Interface research in this area is critical to the success of human-automation teaming, thus requiring a research test bed that brings together humans, autonomy, and systems. Fusion is a framework that enables natural human interaction with flexible and adaptive automation via the use of intelligent agents reasoning among disparate domain knowledge sources, machine learning providing monitoring services and intelligent aids to the operator, cooperative planners and advanced simulation through an instrumented, goal oriented operator interface that empowers scientific experimentation and technology advancement across multiple systems. There are four primary research threads that the Fusion framework is addressing to accomplish these goals: cloud-based simulation architecture; software extensibility; interface instrumentation; and, human-autonomy dialog through retrospection

Dynamic Behavior Sequencing for Hybrid Robot Architectures

Hybrid robot control architectures separate planning, coordination, and sensing and acting into separate processing layers to provide autonomous robots both deliberative and reactive functionality. This approach results in systems that perform well in goal-oriented and dynamic environments. Often, the interfaces and intents of each functional layer are tightly coupled and hand coded so any system change requires several changes in the other layers. This work presents the dynamic behavior hierarchy generation (DBHG) algorithm, which uses an abstract behavior representation to automatically build a behavior hierarchy for meeting a task goal. The generation of the behavior hierarchy occurs without knowledge of the low-level implementation or the high-level goals the behaviors achieve. The algorithm’s ability to automate the behavior hierarchy generation is demonstrated on a robot task of target search, identification, and extraction. An additional simulated experiment in which deliberation identifies which sensors to use to conserve power shows that no system modification or predefined task structures is required for the DBHG to dynamically build different behavior hierarchies