Abstract-This paper presents a method for reasoning about the effects of sensor error on high-level 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. The creation of robot controllers for complex tasks is an arduous and error-prone process, requiring the iterative design and testing of large, complex controllers. In many cases, these controllers extend beyond single continuous controllers into large, hybrid controllers that allow for discrete switching among a set of individual, continuous controllers. Recently, researchers have developed methods for automatically synthesizing complex, hybrid controllers from high-level task specifications in a manner that provides guarantees about the behavior of the robot [1, 2, 5, 9, 10, 15, 17, 18, 20] . These methods cover a variety of capabilities and applications. The approach presented in [8] , for example, facilitates the creation of a non-reactive controller from temporal logic specifications. Conversely, other approaches such as [11, 20] focus on creating controllers that react to the system's perception of environmental inputs. Another example of differing approaches is that of [4, 9] when compared to [1, 8] ; the former two methods use feedback controllers, while the latter two use sampling-based methods to allow for motion planning involving complex dynamics and environments. The authors of [19] present a a receding horizon framework for reducing computational complexity, at the cost of completeness. Previous work facilitated the creation of controllers that were guaranteed to satisfy their underlying specifications, but such guarantees were based on the assumption of perfect sensing and actuation. In this paper, we relax the assumption of perfect sensing, and use probabilistic analysis to investigate the correctness of the high-level behavior of the controllers. Related work includes [13] , in which the authors control linear, stochastic systems with temporal logic specifications
Such approaches can generate sequence of controllers that can be proven to be correct REF .
Probabilistic Analysis of Correctness of High-Level Robot Behavior with Sensor Error
Robotics: Science and Systems
Computer Science