Guaranteeing High-Level Behaviors while Exploring Partially Known Maps

Sarid, Shahar; Xu, Bingxin; Kress-Gazit, Hadas

doi:10.15607/rss.2012.viii.048

Cited by 23 publications

(28 citation statements)

References 19 publications

(39 reference statements)

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“…LTLMoP contains an approach [23] to guarantee highlevel behavior for map exploration when the environment is continuously updated. The approach synthesizes and resynthesizes plans, expressed in linear temporal logic, of a hybrid controller, when new map information is discovered.…”

Section: Related Workmentioning

confidence: 99%

On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles

Mitsch

Ghorbal

Platzer

2013

Robotics: Science and Systems IX

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Abstract-Nowadays, robots interact more frequently with a dynamic environment outside limited manufacturing sites and in close proximity with humans. Thus, safety of motion and obstacle avoidance are vital safety features of such robots. We formally study two safety properties of avoiding both stationary and moving obstacles: (i) passive safety, which ensures that no collisions can happen while the robot moves, and (ii) the stronger passive friendly safety in which the robot further maintains sufficient maneuvering distance for obstacles to avoid collision as well. We use hybrid system models and theorem proving techniques that describe and formally verify the robot's discrete control decisions along with its continuous, physical motion. Moreover, we formally prove that safety can still be guaranteed despite location and actuator uncertainty.

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Section: Related Workmentioning

confidence: 99%

On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles

Mitsch

Ghorbal

Platzer

2013

Robotics: Science and Systems IX

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“…But this suffers the same issue as having no environment model: the robot's belief may not correspond to what actually happens (due to environmental dynamics). Guo et al [21] and Sarid et al [22] both iteratively produce a correct by construction program as uncertain information becomes known. However, it's not possible with that approach to verify the program in advance.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Verification of Multi-robot Missions in Uncertain Environments

Lyons

Arkin

Jiang

et al. 2015

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

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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.

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“…Model-checking has been successfully applied to software verification [13]. For robot control, Linear Temporal Logic (LTL) is commonly used to specify behavior [22,11]. LTL formula are representable as Büchi Automata, which define finite state languages over infinite length strings.…”

Section: Related Workmentioning

confidence: 99%

Correct Software Synthesis for Stable Speed-Controlled Robotic Walking

Dantam¹,

Hereid²,

Ames³

et al. 2013

Robotics: Science and Systems IX

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Abstract-We present a software synthesis method for speedcontrolled robot walking based on supervisory control of a context-free Motion Grammar. First, we use Human-Inspired control to identify parameters for fixed speed walking and for transitions between fixed speeds, guaranteeing dynamic stability. Next, we build a Motion Grammar representing the discretetime control for this set of speeds. Then, we synthesize C code from this grammar and generate supervisors 1 online to achieve desired walking speeds, guaranteeing correctness of discrete computation. Finally, we demonstrate this approach on the Aldebaran NAO, showing stable walking transitions with dynamically selected speeds.

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Guaranteeing High-Level Behaviors while Exploring Partially Known Maps

Cited by 23 publications

References 19 publications

On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles

On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles

Probabilistic Verification of Multi-robot Missions in Uncertain Environments

Correct Software Synthesis for Stable Speed-Controlled Robotic Walking

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