Creature co-op: Achieving robust remote operations with a community of low-cost robots

The concept is advanced of carrying out space based remote missions using a cooperative of low cost robot specialists rather than monolithic, multipurpose systems. A simulation is described wherein a control architecture for such a system of specialists is being investigated. Early results show such co-ops to be robust in the face of unforeseen circumstances. Descriptions of the platforms and sensors modeled and the beacon and retriever creatures that make up the co-op are included

Building safer robots: Safety driven control

In recent years there has been a concerted effort to address many of the safety issues associated with physical human-robot interaction (pHRI). However, a number of challenges remain. For personal robots, and those intended to operate in unstructured environments, the problem of safety is compounded. In this paper we argue that traditional system design techniques fail to capture the complexities associated with dynamic environments. We present an overview of our safety-driven control system and its implementation methodology. The methodology builds on traditional functional hazard analysis, with the addition of processes aimed at improving the safety of autonomous personal robots. This will be achieved with the use of a safety system developed during the hazard analysis stage. This safety system, called the safety protection system, will initially be used to verify that safety constraints, identified during hazard analysis, have been implemented appropriately. Subsequently it will serve as a high-level safety enforcer, by governing the actions of the robot and preventing the control layer from performing unsafe operations. To demonstrate the effectiveness of the design, a series of experiments have been conducted using a MobileRobots PeopleBot. Finally, results are presented demonstrating how faults injected into a controller can be consistently identified and handled by the safety protection system. © The Author(s) 2012

An intelligent agent architecture in which to pursue robot learning

This paper describes a multi-layered, intelligent agent software architecture, developed for mobile and undersea robot applications in the defense sector, and to provide tele-autonomy to space-based manipulator robots. The architecture has a deliberative layer which uses a state-based planner, a middle layer for sequencing partially ordered plans using robot skills, and a lower layer repertoire of continuous robot skills. The system has been shown to provide a higher level of human supervision that preserves safety while allowing for task level direction, reaction to out-ofnorm parameters, and human intervention at all levels of control. For this workshop, we hypothesize that the architecture is a useful framework in which to explore learning techniques. In particular, we outline techniques appropriate to learning within a given layer, techniques for migrating competences from higher to lower layers, and overall system adaptation from its interaction with the environment. Examples are reinforcement learning for tuning individual skills, case-based techniques to improve the re-planning capability of the deliberative layer, and chunking or explanation-based learning to migrate new strategies created by the planner into standard procedures for the sequencing level. Background and Motivation Since the late eighties we have investigated ways to combine deliberation and reactivity in robot control architectures [Sanborn et al 1989, Bonasso 91, & Bonasso et al 92], in order to program robots to carry out tasks robustly in field environments. Field environments are those in which events for which the robot has a response can occur unpredictably, and wherein the locations of objects and other agents is usually not known with certainty until the robot is carrying out the required task. A robot control software architecture, developed at MITRE is an outgrowth of several lines of situated reasoning research in robot intelligence [Firby 89, Gat 91, Connel