Results 1  10
of
32
Controlling wild bodies using linear temporal logic
 In Proceedings Robotics: Science and Systems
, 2011
"... Fig. 1. a) Our vehicle of study is a $4 weasel ball; b) it consists entirely of a battery and slowly oscillating motor mounted to a plastic shell. Abstract—There is substantial interest controlling a group of bodies from specifications of tasks given in a highlevel, humanlike language. This paper p ..."
Abstract

Cited by 22 (4 self)
 Add to MetaCart
(Show Context)
Fig. 1. a) Our vehicle of study is a $4 weasel ball; b) it consists entirely of a battery and slowly oscillating motor mounted to a plastic shell. Abstract—There is substantial interest controlling a group of bodies from specifications of tasks given in a highlevel, humanlike language. This paper proposes a methodology that creates lowlevel hybrid controllers that guarantee that a group of bodies execute a highlevel specified task without dynamical system modeling, precise state estimation or state feedback. We do this by exploiting the wild motions of very simple bodies in an environment connected by gates which serve as the system inputs, as opposed motors on the bodies. We present experiments using inexpensive hardware demonstrating the practical feasibility of our approach to solving tasks such as navigation, patrolling, and coverage. I.
Probabilistic Analysis of Correctness of HighLevel Robot Behavior with Sensor Error
"... Abstract—This paper presents a method for reasoning about the effects of sensor error on highlevel robot behavior. We consider robot controllers that are synthesized from a set of highlevel, temporal logic task specifications, such that the resulting robot behavior is guaranteed to satisfy these sp ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
(Show Context)
Abstract—This paper presents a method for reasoning about the effects of sensor error on highlevel robot behavior. We consider robot controllers that are synthesized from a set of highlevel, temporal logic task specifications, such that the resulting robot behavior is guaranteed to satisfy these specifications when assuming perfect sensors and actuators. We relax the assumption of perfect sensing, and calculate the probability with which the controller satisfies a set of temporal logic specifications. We consider parametric representations, where the satisfaction probability is found as a function of the model parameters, and numerical representations, allowing for the analysis of large examples. We illustrate our approach with three examples of varying size that provide insight into unintuitive effects of sensor error that can inform the specification design process. I.
Correct, Reactive Robot Control from Abstraction and Temporal Logic Specifications
 IEEE RAM
"... We describe recent advances in formal synthesis of robot controllers from temporal logic specifications. In particular, we consider reactive specifications where the robot continuously gathers information about its environment and decides its action at run time based on this information. The automat ..."
Abstract

Cited by 14 (4 self)
 Add to MetaCart
We describe recent advances in formal synthesis of robot controllers from temporal logic specifications. In particular, we consider reactive specifications where the robot continuously gathers information about its environment and decides its action at run time based on this information. The automatically generated controller is provably correct with respect to a given specification for all the valid environment behaviors. We discuss the main limitation of such controller synthesis – the state explosion problem – and two different approaches that mitigate this problem. Computational tools that implement these approaches are also described. An autonomous vehicle navigating an urbanlike environment is used as an illustrative example throughout the paper.
D.: Automated verification and strategy synthesis for probabilistic systems (extended version) (2013), available from [49
"... Abstract. Probabilistic model checking is an automated technique to verify whether a probabilistic system, e.g., a distributed network protocol which can exhibit failures, satisfies a temporal logic property, for example, “the minimum probability of the network recovering from a fault in a given ti ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
(Show Context)
Abstract. Probabilistic model checking is an automated technique to verify whether a probabilistic system, e.g., a distributed network protocol which can exhibit failures, satisfies a temporal logic property, for example, “the minimum probability of the network recovering from a fault in a given time period is above 0.98”. Dually, we can also synthesise, from a model and a property specification, a strategy for controlling the system in order to satisfy or optimise the property, but this aspect has received less attention to date. In this paper, we give an overview of methods for automated verification and strategy synthesis for probabilistic systems. Primarily, we focus on the model of Markov decision processes and use property specifications based on probabilistic LTL and expected reward objectives. We also describe how to apply multiobjective model checking to investigate tradeoffs between several properties, and extensions to stochastic multiplayer games. The paper concludes with a summary of future challenges in this area. 1
Guaranteeing highlevel behaviors while exploring partially known maps
 In RSS. IEEE
, 2012
"... Abstract—This paper presents an approach for automatically synthesizing and resynthesizing a hybrid controller that guarantees a robot will exhibit a userdefined highlevel behavior while exploring a partially known workspace (map). The approach includes dynamically adjusting the discrete abstract ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
Abstract—This paper presents an approach for automatically synthesizing and resynthesizing a hybrid controller that guarantees a robot will exhibit a userdefined highlevel behavior while exploring a partially known workspace (map). The approach includes dynamically adjusting the discrete abstraction of the workspace as new regions are detected by the robot’s sensors, automatically rewriting the specification (formally defined using Linear Temporal Logic) and resynthesizing the control while preserving the robot state and its history of task completion. The approach is implemented within the LTLMoP toolkit and is demonstrated using a Pioneer 3DX in the lab. I.
Controlling Wild Mobile Robots Using Virtual Gates and Discrete Transitions
"... Abstract—We present an approach to controlling multiple mobile robots without requiring system identification, geometric map building, localization, or state estimation. Instead, we purposely design them to execute wild motions, which means each will strike every open set infinitely often along the ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
Abstract—We present an approach to controlling multiple mobile robots without requiring system identification, geometric map building, localization, or state estimation. Instead, we purposely design them to execute wild motions, which means each will strike every open set infinitely often along the boundary of any connected region in which it is placed. We then divide the environment into a discrete set of regions, with borders delineated with simple markers, such as colored tape. Using simple sensor feedback, we show that complex tasks can be solved, such as patrolling, disentanglement, and basic navigation. The method is implemented in simulation and on real robots, which for many tasks are fully distributed without any mutual communication. I.
1 Getting it Right the First time: Robot Mission Guarantees in the Presence of Uncertainty*
"... Abstract—Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. We have previously developed an approach to establishing performance guarantees for behaviorbased controllers in a processalgebra framework. We extend that work he ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
(Show Context)
Abstract—Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. We have previously developed an approach to establishing performance guarantees for behaviorbased controllers in a processalgebra framework. We extend that work here to include random variables, and we show how our prior results can be used to generate a Dynamic Bayesian Network for the coupled system of program and environment model. Verification is reduced to a filtering problem for this network. Finally, we present validation results that demonstrate the effectiveness of the verification of a multiple waypoint robot mission using this approach. I.
Probabilistically Safe Control of Noisy Dubins Vehicles
 In IEEE Intelligent Robots and Systems (IROS) Conference, 2012
, 2012
"... Abstract — We address the problem of controlling a stochastic version of a Dubins vehicle such that the probability of satisfying a temporal logic specification over a set of properties at the regions in a partitioned environment is maximized. We assume that the vehicle can determine its precise ini ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
(Show Context)
Abstract — We address the problem of controlling a stochastic version of a Dubins vehicle such that the probability of satisfying a temporal logic specification over a set of properties at the regions in a partitioned environment is maximized. We assume that the vehicle can determine its precise initial position in a known map of the environment. However, inspired by practical limitations, we assume that the vehicle is equipped with noisy actuators and, during its motion in the environment, it can only measure its angular velocity using a limited accuracy gyroscope. Through quantization and discretization, we construct a finite approximation for the motion of the vehicle in the form of a Markov Decision Process (MDP). We allow for task specifications given as temporal logic statements over the environmental properties, and use tools in Probabilistic Computation Tree Logic (PCTL) to generate an MDP control policy that maximizes the probability of satisfaction. We translate this policy to a vehicle feedback control strategy and show that the probability that the vehicle satisfies the specification in the original environment is bounded from below by the maximum probability of satisfying the specification on the MDP. I.
Temporal logic motion control using actorcritic methods
 In Robotics and Automation (ICRA), 2012 IEEE International Conference on
, 2012
"... This paper considers the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of th ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
This paper considers the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov Decision Process (MDP). The robot control problem becomes finding the control policy which maximizes the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each stateaction pair, as well as solving the necessary optimization problem for the optimal policy, are computationally intensive. To address these issues, we propose an approximate dynamic programming framework based on a leastsquare temporal difference learning method of the actorcritic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Simulations confirm that convergence of the parameters translates to an approximately optimal policy. 1
Signature of Author.......................................................
"... This dissertation has not been submitted as an exercise for a degree at any other University. Except where otherwise stated, the work described herein has been carried out by the author alone. This dissertation may be borrowed or copied upon request with the permission of the Librarian, University o ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
This dissertation has not been submitted as an exercise for a degree at any other University. Except where otherwise stated, the work described herein has been carried out by the author alone. This dissertation may be borrowed or copied upon request with the permission of the Librarian, University of