Verifying and validating multirobot missions

2015 IEEE 27th International Conference on Tools With Artificial Intelligence (ICTAI)

Jiang

et al. 2015

Abstract-The effective use of autonomous robot teams in highly-critical missions depends on being able to establish performance guarantees. However, establishing a guarantee for the behavior of an autonomous robot operating in an uncertain environment with obstacles is a challenging problem. This paper addresses the challenges involved in building a software tool for verifying the behavior of a multi-robot waypoint mission that includes uncertain environment geometry as well as uncertainty in robot motion. One contribution of this paper is an approach to the problem of a-priori specification of uncertain environments for robot program verification. A second contribution is a novel method to extend the Bayesian Network formulation to reason about random variables with different subpopulations, introduced to address the challenge of representing the effects of multiple sensory histories when verifying a robot mission. The third contribution is experimental validation results presented to show the effectiveness of this approach on a two-robot, bounding overwatch mission.

Section: Discussionmentioning

confidence: 99%

Section: Figure 2 Bounding Overwatch With Two Robotsmentioning

confidence: 94%

“…The bounding overwatch multirobot mission, without obstacles, is described in more detail in [8]. The following subsection reviews enough of this background to describe our results.…”

Section: Verification With Uncertain Geometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Verification of Multi-robot Missions in Uncertain Environments

Lyons

2015 IEEE 27th International Conference on Tools With Artificial Intelligence (ICTAI)

Jiang

et al. 2015

“…Thus, a C-WMD mission, such as the biohazard mission, needs to be verified to have a certain level of performance guarantee before execution. While our research group has presented methods (Lyons et al, 2014;Lyons et al, 2012;Lyons et al, 2013) for verification of robot behaviors, the integrated SLAM-based behaviors presented in this paper presents a new challenge of verifying robot behaviors that are based on probabilistic algorithms such as SLAM. Thus, additional future work is to verify a C-WMD mission (e.g., biohazard search) where SLAM-based behaviors are used.…”

Section: Discussionmentioning

confidence: 99%

SLAM-Based Spatial Memory for Behavior-Based Robots

Jiang

2015

IFAC-PapersOnLine

Knowledge is essential for an autonomous robot to act intelligently when tasked with a mission. With recent leaps of progress, the paradigm of SLAM (Simultaneous Localization and Mapping) has emerged as an ideal source of spatial knowledge for autonomous robots. However, despite advancements in both paradigms of SLAM and robot control, research in the integration of these areas has been lacking and remained open to investigation. This paper presents an integration of SLAM into a behavior-based robotic system as a dynamically acquired spatial memory, which can be used to enable new behaviors and augment existing ones. The effectiveness of the integrated system is demonstrated with a biohazard search mission, where a robot is tasked to search and locate a biohazard within an unknown environment under a time constraint.

Automatic Verification of Autonomous Robot Missions

O'Brien

Simulation, Modeling, and Programming for Autonomous Robots

Harrington

et al. 2014

Before autonomous robotics can be used for dangerous or critical missions, performance guarantees should be made available. This paper overviews a software system for the verification of behavior-based controllers in context of chosen hardware and environmental models. Robotic controllers are automatically translated to a process algebra. The system comprising both the robot and the environment are then evaluated by VIPARS, a verification software module in development, and compared to specific performance criteria. The user is returned a probability that the performance criteria will hold in the uncertainty of real-world conditions. Experimental results demonstrate accurate verification for a mission related to the search for a biohazard.