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19
Optimal path planning for surveillance with temporallogic constraints
 The International Journal of Robotics Research
"... 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 pr ..."
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Cited by 22 (7 self)
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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 which 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 which computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road network platform. 1
Robust multirobot optimal path planning with temporal logic constraints,”
 in Proc ICRA, St.
, 2012
"... AbstractIn this paper we present a method for automatically planning robust optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the environment is modeled as a weighted transition system, and the mission is given as a Linear Tempo ..."
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Cited by 12 (3 self)
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AbstractIn this paper we present a method for automatically planning robust optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the environment is modeled as a weighted transition system, and the mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied by the regions of the environment. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition while ensuring that the LTL formula is satisfied even with uncertainty in the robots' traveling times. We characterize a class of LTL formulas that are robust to robot timing errors, for which we generate optimal paths if no timing errors are present, and we present bounds on the deviation from the optimal values in the presence of errors. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.
Revising Motion Planning under Linear Temporal Logic Specifications
 in Partially Known Workspaces. IEEE International Conference on Robotics and Automation
, 2013
"... Abstract — In this paper we propose a generic framework for realtime motion planning based on modelchecking and revision. The task specification is given as a Linear Temporal Logic formula over a finite abstraction of the robot motion. A preliminary motion plan is first generated based on the init ..."
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Cited by 11 (4 self)
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Abstract — In this paper we propose a generic framework for realtime motion planning based on modelchecking and revision. The task specification is given as a Linear Temporal Logic formula over a finite abstraction of the robot motion. A preliminary motion plan is first generated based on the initial knowledge of the system model. Then realtime information obtained during the runtime is used to update the system model, verify and further revise the motion plan. The implementation and revision of the motion plan are performed in realtime. This framework can be applied to partiallyknown workspaces and workspaces with large uncertainties. Computer simulations are presented to demonstrate the efficiency of the framework. I.
Optimal Control with Weighted Average Costs and Temporal Logic Specifications
"... Abstract—We consider optimal control for a system subject to temporal logic constraints. We minimize a weighted average cost function that generalizes the commonly used average cost function from discretetime optimal control. Dynamic programming algorithms are used to construct an optimal trajector ..."
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Cited by 9 (5 self)
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Abstract—We consider optimal control for a system subject to temporal logic constraints. We minimize a weighted average cost function that generalizes the commonly used average cost function from discretetime optimal control. Dynamic programming algorithms are used to construct an optimal trajectory for the system that minimizes the cost function while satisfying a temporal logic specification. Constructing an optimal trajectory takes only polynomially more time than constructing a feasible trajectory. We demonstrate our methods on simulations of autonomous driving and robotic surveillance tasks. I.
Efficient reactive controller synthesis for a fragment of linear temporal logic
 in IEEE International Conference on Robotics and Automation (ICRA
, 2013
"... Abstract — Motivated by robotic motion planning, we develop a framework for control policy synthesis for both nondeterministic transition systems and Markov decision processes that are subject to temporal logic task specifications. We introduce a fragment of linear temporal logic that can be used ..."
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Cited by 7 (2 self)
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Abstract — Motivated by robotic motion planning, we develop a framework for control policy synthesis for both nondeterministic transition systems and Markov decision processes that are subject to temporal logic task specifications. We introduce a fragment of linear temporal logic that can be used to specify common motion planning tasks such as safe navigation, response to the environment, persistent coverage, and surveillance. This fragment is computationally efficient; the complexity of control policy synthesis is a doublyexponential improvement over standard linear temporal logic for both nondeterministic transition systems and Markov decision processes. This improvement is possible because we compute directly on the original system, as opposed to the automatabased approach commonly used. We give simulation results for representative motion planning tasks and compare to generalized reactivity(1). I.
Designing Petri Net Supervisors from LTL Specifications
"... Abstract—We present a methodology to build a Petri net realization of a supervisor that, given a Petri net model of a (multi)robot system and a linear temporal logic (LTL) specification, forces the system to fulfil the specification. The methodology includes composing the Petri net model with the B ..."
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Cited by 7 (2 self)
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Abstract—We present a methodology to build a Petri net realization of a supervisor that, given a Petri net model of a (multi)robot system and a linear temporal logic (LTL) specification, forces the system to fulfil the specification. The methodology includes composing the Petri net model with the Büchi automaton representing the LTL formula and trimming the result using a known method to reduce the size of the supervisor. Furthermore, we guarantee that the obtained supervisors are admissible by construction by restricting the LTL formulas that can be written to an appropriate subset. To illustrate the method, we provide an example on how to specify coordination rules for a team of simulated soccer robots. I.
Optimal multirobot path planning with temporal logic constraints
 in IEEE/RSJ International Conference on Intelligent Robots and Systems,, 2011
"... Abstract — In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification. Each robot’s motion in the environment is modeled as a weighted transition system. The mission is given as a Linear Temporal Logic form ..."
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Cited by 7 (1 self)
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Abstract — In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification. Each robot’s motion in the environment is modeled as a weighted transition system. The mission is given as a Linear Temporal Logic formula. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition. Our method is guaranteed to compute an optimal set of robot paths. We utilize a timed automaton representation in order to capture the relative position of the robots in the environment. We then obtain a bisimulation of this timed automaton as a finite transition system that captures the joint behavior of the robots and apply our earlier algorithm for the single robot case to optimize the group motion. We present a simulation of a persistent monitoring task in a road network environment. I.
Motion and Action Planning under LTL Specification using Navigation Functions and Action Description Language
 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS
"... Abstract — We propose a novel framework to combine modelcheckingbased robot motion planning with action planning using action description languages, aiming to tackle task specifications given as Linear Temporal Logic (LTL) formulas. The specifications implicitly require both sequential regions to ..."
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Cited by 5 (2 self)
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Abstract — We propose a novel framework to combine modelcheckingbased robot motion planning with action planning using action description languages, aiming to tackle task specifications given as Linear Temporal Logic (LTL) formulas. The specifications implicitly require both sequential regions to visit and the desired actions to perform at these regions. The robot’s motion is abstracted based on sphere regions of interest in the workspace and the structure of navigation function(NF)based controllers, while the robot’s action map is constructed based on precondition and effect functions associated with the actions the robot is capable of. An optimal planner is designed that generates the discrete motionandaction plan fulfilling the task specification, as well as the lowlevel hybrid controllers that implement this plan. The whole framework is demonstrated by a case study. I.
Leastviolating control strategy synthesis with safety rules
 In Proc. of 16th Int. Conf. on Hybrid Systems: Computation and Control (HSCC
, 2013
"... We consider the problem of automatic control strategy synthesis, for discrete models of robotic systems, to fulfill a task that requires reaching a goal state while obeying a given set of safety rules. In this paper, we focus on the case when the said task is not feasible without temporarily violat ..."
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Cited by 5 (1 self)
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We consider the problem of automatic control strategy synthesis, for discrete models of robotic systems, to fulfill a task that requires reaching a goal state while obeying a given set of safety rules. In this paper, we focus on the case when the said task is not feasible without temporarily violating some of the rules. We propose an algorithm that synthesizes a motion which violates only lowest priority rules for the shortest amount of time. Although the proposed algorithm can be applied in a variety of control problems, throughout the paper, we motivate this problem with an autonomous car navigating in an urban environment while abiding by the rules of the road, such as “always stay in the right lane ” and “do not enter the sidewalk. ” We evaluate the algorithm on a case study with several illustrative scenarios.
A receding horizon approach to multiagent planning from LTL specifications
 American Control Conference (ACC
"... Abstract — We study the problem of control synthesis for multiagent systems, to achieve complex, highlevel, longterm goals that are assigned to each agent individually. As the agents might not be capable of satisfying their respective goals by themselves, requests for other agents ’ collaboration ..."
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Cited by 4 (4 self)
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Abstract — We study the problem of control synthesis for multiagent systems, to achieve complex, highlevel, longterm goals that are assigned to each agent individually. As the agents might not be capable of satisfying their respective goals by themselves, requests for other agents ’ collaborations are a part of the task descriptions. Particularly, we consider that the task specification takes a form of a linear temporal logic formula, which may contain requirements and constraints on the other agent’s behavior. A traditional automatabased approach to multiagent strategy synthesis from such specifications builds on centralized planning for the whole team and thus suffers from extreme computational demands. In this work, we aim at reducing the computational complexity by decomposing the strategy synthesis problem into short horizon planning problems that are solved iteratively, upon the run of the agents. We discuss the correctness of the solution and find assumptions, under which the proposed iterative algorithm leads to provable eventual satisfaction of the desired specifications. I.