Abstract. Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. This requirement raises many issues for existing approaches to software verification. An approach based on behavior-based controllers in a process-algebra framework is proposed by Lyons et al [15] to side-step state combinatorics. In this paper we show that this approach can be used to generate a Dynamic Bayesian Network for the problem, and that verification is reduced to a filtering problem for this network. We present validation results for the verification of a multiple waypoint robot mission using this approach.

Abstract-This paper describes the need and methods required to construct an integrated software verification and mission specification system for use in robotic missions intended for counter-weapons of mass destruction (c-WMD) operations, as part of a 3-year effort for the Defense Threat Reduction Agency. The overall system architecture is described. The principal tool for verification is a process algebra, PARS, based on port automata theory. PARS is introduced, emphasizing its ability to represent probabilistic programs and uncertain and dynamic environments, followed by the analysis of mission properties for an example robotic mission.

No abstract

Abstract:Establishing performance guarantees for robot missions is especially important for C-WMD applications. Software verification techniques, such as model checking (Clark 1999, Jhala & Majumdar 2009, can be applied to robotic applications but characteristics of this application area, including addition of a robot environment model and handling continuous spatial location well, exacerbate state explosion, a key weakness of these methods.We have proposed an approach to verifying robot missions that shifts the focus from state-based analysis onto the solution of a set of flow equations . The key novelty introduced in this paper is a probabilistic spatial representation for flow equations. We show how this representation models the spatial situation for robot motion with environments or controllers that include discrete choice (constraints).A model such as we propose here is useful only if it can accurately predict robot motion. We conclude by presenting three validation results that show this approach has strong predictive power; that is, that the verifications it produces can be trusted.

SUMMARYVisual Homing is a navigation method based on comparing a stored image of a goal location to the current image to determine how to navigate to the goal location. It is theorized that insects such as ants and bees employ visual homing techniques to return to their nest or hive, and inspired by this, several researchers have developed elegant robot visual homing algorithms. Depth information, from visual scale, or other modality such as laser ranging, can improve the quality of homing. While insects are not well equipped for stereovision, stereovision is an effective robot sensor. We describe the challenges involved in using stereovision derived depth in visual homing and our proposed solutions. Our algorithm,Homing with Stereovision(HSV), utilizes a stereo camera mounted on a pan-tilt unit to build composite wide-field stereo images and estimate distance and orientation from the robot to the goal location. HSV is evaluated in a set of 200 indoor trials using two Pioneer 3-AT robots showing it effectively leverages stereo depth information when compared to a depth from scale approach.