Optimal path planning for surveillance with temporal-logic constraints

In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes Büchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.This material is based upon work supported in part by ONR-MURI (award N00014-09-1-1051), ARO (award W911NF-09-1-0088), and Masaryk University (grant numbers LH11065 and GD102/09/H042), and other funding sources (AFOSR YIP FA9550-09-1-0209, NSF CNS-1035588, NSF CNS-0834260). (N00014-09-1-1051 - ONR-MURI; W911NF-09-1-0088 - ARO; LH11065 - Masaryk University; GD102/09/H042 - Masaryk University; FA9550-09-1-0209 - AFOSR YIP; CNS-1035588 - NSF; CNS-0834260 - NSF

Probabilistic visual verification for robotic assembly manipulation

In this paper we present a visual verification approach for robotic assembly manipulation which enables robots to verify their assembly state. Given shape models of objects and their expected placement configurations, our approach estimates the probability of the success of the assembled state using a depth sensor. The proposed approach takes into account uncertainties in object pose. Probability distributions of depth and surface normal depending on the uncertainties are estimated to classify the assembly state in a Bayesian formulation. The effectiveness of our approach is validated in comparative experiments with other approaches.Boeing Compan

Autonomous Golf Cars for Public Trial of Mobility-on-Demand Service

We detail the design of autonomous golf cars which were used in public trials in Singapore’s Chinese and Japanese Gardens, for the purpose of raising public awareness and gaining user acceptance of autonomous vehicles. The golf cars were designed to be robust, reliable, and safe, while operating under prolonged durations. Considerations that went in to the overall system design included the fact that any member of the public had to not only be able to easily use the system, but to also not have the option to use the system in an unintended manner. This paper details the hardware and software components of the golf cars with these considerations, and also how the booking system and mission planner facilitated users to book for a golf car from any of ten stations within the gardens. We show that the vehicles performed robustly throughout the prolonged operations with a small localization variance, and that users were very receptive from the user survey results.Singapore. National Research Foundatio

MDP Optimal Control under Temporal Logic Constraints

In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. We synthesize a control policy such that the MDP satisfies the given specification almost surely, if such a policy exists. In addition, we designate an "optimizing proposition" to be repeatedly satisfied, and we formulate a novel optimization criterion in terms of minimizing the expected cost in between satisfactions of this proposition. We propose a sufficient condition for a policy to be optimal, and develop a dynamic programming algorithm that synthesizes a policy that is optimal under some conditions, and sub-optimal otherwise. This problem is motivated by robotic applications requiring persistent tasks, such as environmental monitoring or data gathering, to be performed.Comment: Technical report accompanying the CDC2011 submissio

LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees

We present a method to generate a robot control strategy that maximizes the probability to accomplish a task. The task is given as a Linear Temporal Logic (LTL) formula over a set of properties that can be satisfied at the regions of a partitioned environment. We assume that the probabilities with which the properties are satisfied at the regions are known, and the robot can determine the truth value of a proposition only at the current region. Motivated by several results on partitioned-based abstractions, we assume that the motion is performed on a graph. To account for noisy sensors and actuators, we assume that a control action enables several transitions with known probabilities. We show that this problem can be reduced to the problem of generating a control policy for a Markov Decision Process (MDP) such that the probability of satisfying an LTL formula over its states is maximized. We provide a complete solution for the latter problem that builds on existing results from probabilistic model checking. We include an illustrative case study.Comment: Technical Report accompanying IFAC 201

A distributed algorithm for 2D shape duplication with smart pebble robots

We present our digital fabrication technique for manufacturing active objects in 2D from a collection of smart particles. Given a passive model of the object to be formed, we envision submerging this original in a vat of smart particles, executing the new shape duplication algorithm described in this paper, and then brushing aside any extra modules to reveal both the original object and an exact copy, side-by-side. Extensions to the duplication algorithm can be used to create a magnified version of the original or multiple copies of the model object. Our novel duplication algorithm uses a distributed approach to identify the geometric specification of the object being duplicated and then forms the duplicate from spare modules in the vicinity of the original. This paper details the duplication algorithm and the features that make it robust to (1) an imperfect packing of the modules around the original object; (2) missing communication links between neighboring modules; and (3) missing modules in the vicinity of the duplicate object(s). We show that the algorithm requires O(1) storage space per module and that the algorithm exchanges O(n) messages per module. Finally, we present experimental results from 60 hardware trials and 150 simulations. These experiments demonstrate the algorithm working correctly and reliably despite broken communication links and missing modules.United States. Army Research Office (Grant W911NF-08-1-0228)National Science Foundation (U.S.). Office of Emerging Frontiers in Research and Innovation (Grant 0735953)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

On the completeness of ensembles of motion planners for decentralized planning

We provide a set of sufficient conditions to establish the completeness of an ensemble of motion planners-that is, a set of loosely-coupled motion planners that produce a unified result. The planners are assumed to divide the total planning problem across some parameter space(s), such as task space, state space, action space, or time. Robotic applications have employed ensembles of planners for decades, although the concept has not been formally unified or analyzed until now. We focus on applications in multi-robot navigation and collision avoidance. We show that individual resolutionor probabilistically-complete planners that meet certain communication criteria constitute a (respectively, resolution- or probabilistically-) complete ensemble of planners. This ensemble of planners, in turn, guarantees that the robots are free of deadlock, livelock, and starvation.Boeing Compan

Optimality and robustness in multi-robot path planning with temporal logic constraints

In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes Büchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.This work was supported in part by the Office of Naval Research (grant number MURI N00014-09-1051), Army Research Office (grant number W911NF-09-1-0088), Air Force Office of Scientific Research (grant number YIP FA9550-09-1-020), National Science Foundation (grant number CNS-0834260), Singapore-MIT Alliance for Research and Technology (SMART) Future of Urban Mobility Project and by Natural Sciences and Engineering Research Council of Canada. (MURI N00014-09-1051 - Office of Naval Research; W911NF-09-1-0088 - Army Research Office; YIP FA9550-09-1-020 - Air Force Office of Scientific Research; CNS-0834260 - National Science Foundation; Singapore-MIT Alliance for Research and Technology (SMART); Natural Sciences and Engineering Research Council of Canada

How was your day? Online visual workspace summaries using incremental clustering in topic space

Someday mobile robots will operate continually. Day after day, they will be in receipt of a never ending stream of images. In anticipation of this, this paper is about having a mobile robot generate apt and compact summaries of its life experience. We consider a robot moving around its environment both revisiting and exploring, accruing images as it goes. We describe how we can choose a subset of images to summarise the robot's cumulative visual experience. Moreover we show how to do this such that the time cost of generating an summary is largely independent of the total number of images processed. No one day is harder to summarise than any other.Micro Autonomous Consortium Systems and Technology (United States. Army Research Laboratory (Grant W911NF-08-2-0004))United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1031

Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments

In this paper, we present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The environment is modeled as a field that is defined over a finite set of locations. The field grows linearly at locations that are not within the range of a robot and decreases linearly at locations that are within range of a robot. We assume that the robots travel on given closed paths. The speed of each robot along its path is controlled to prevent the field from growing unbounded at any location. We consider the space of speed controllers that are parametrized by a finite set of basis functions. For a single robot, we develop a linear program that computes a speed controller in this space to keep the field bounded, if such a controller exists. Another linear program is derived to compute the speed controller that minimizes the maximum field value over the environment. We extend our linear program formulation to develop a multirobot controller that keeps the field bounded. We characterize, both theoretically and in simulation, the robustness of the controllers to modeling errors and to stochasticity in the environment