Coupled Multi-Robot Systems Under Linear Temporal Logic and Signal Temporal Logic Tasks

Lindemann, Lars; Nowak, Jakub; Schönbächler, Lukas; Guo, Meng; Tůmová, Jana; Dimarogonas, Dimos V.

doi:10.1109/tcst.2019.2955628

Cited by 25 publications

(10 citation statements)

References 33 publications

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“…. is generated by applying (1). We denote the output signal starting at timestep t by (y, t) = y t , y t+1 , y t+2 , .…”

Section: A System Definitionsmentioning

confidence: 99%

“…S IGNAL Temporal Logic (STL) is a powerful means of expressing complex control objectives. STL combines boolean operators ("and", "or", "not") with temporal operators ("always", "eventually", "until") and is defined over continuous-valued signals, making it an appealing choice for dynamical systems ranging from mobile robots [1]- [3] and quadrotors [4] to high-DoF manipulators [5] and traffic networks [6], [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mixed-Integer Programming for Signal Temporal Logic with Fewer Binary Variables

Kurtz¹,

Lin²

2022

Preprint

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Signal Temporal Logic (STL) provides a convenient way of encoding complex control objectives for robotic and cyberphysical systems. The state-of-the-art in trajectory synthesis for STL is based on Mixed-Integer Convex Programming (MICP). The MICP approach is sound and complete, but has limited scalability due to exponential complexity in the number of binary variables. In this letter, we propose a more efficient MICP encoding for STL. Our new encoding is based on the insight that disjunction can be encoded using a logarithmic number of binary variables and conjunction can be encoded without binary variables. We demonstrate in simulation examples that our proposed approach significantly outperforms the state-of-theart for long and complex specifications. Open-source software is available: https://stlpy.readthedocs.io.

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“…. is generated by applying (1). We denote the output signal starting at timestep t by (y, t) = y t , y t+1 , y t+2 , .…”

Section: A System Definitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mixed-Integer Programming for Signal Temporal Logic with Fewer Binary Variables

Kurtz¹,

Lin²

2022

Preprint

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“…STL can be thought of as Boolean logic with added temporal operators to capture temporal behavior. It allows the succinct and unambiguous specification of a wide variety of complex system behaviors over time [2], [3], [4] and has been used extensively to formalize control objectives, e.g. [4].…”

Section: Technical Preliminariesmentioning

confidence: 99%

“…It allows the succinct and unambiguous specification of a wide variety of complex system behaviors over time [2], [3], [4] and has been used extensively to formalize control objectives, e.g. [4]. Due to space limitations we refer the reader to [1] for formal semantics; we introduce STL via examples.…”

Section: Technical Preliminariesmentioning

confidence: 99%

FairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace

Kurtz

Abbas

2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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We present a solution to the problem of fairly planning a fleet of Unmanned Aerial Vehicles (UAVs) that have different missions and operators, such that no one operator unfairly gets to finish its missions early at the expense of others -unless this was explicitly negotiated. When hundreds of UAVs share an urban airspace, the relevant authorities should allocate corridors to them such that they complete their missions, but no one vehicle is accidentally given an exceptionally fast path at the expense of another, which is thus forced to wait and waste energy. Our solution, FairFly, addresses the fair planning question for general autonomous systems, including UAV fleets, subject to complex missions typical of urban applications. FairFly formalizes each mission in temporal logic. An offline search finds the fairest paths that satisfy the missions and can be flown by the UAVs, leading to lighter online control load. It allows explicit negotiation between UAVs to enable imbalanced path durations if desired. We present three fairness notions, including one that reduces energy consumption. We validate our results in simulation, and demonstrate a lighter computational load and less UAV energy consumption as a result of flying fair trajectories.

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“…Recently, multi-robot control problems with temporal logic tasks have been studied in broad areas including civilian and industrial applications [1][2][3][4]. For example, in the patrol missions, each robot is required to reach its goal regions within a specified time interval while avoiding collision with other robots and obstacles in the environment all the time.…”

Section: Introductionmentioning

confidence: 99%

Distributed model predictive control for multi‐robot systems with conflicting signal temporal logic tasks

Zhou

Zou

et al. 2022

IET Control Theory & Appl

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In this paper, the control problem of multi-robot systems under temporal logic tasks with limited time and logic constraints is studied, where each robot is required to reach the specified region in a given time and avoid collision all the time. Since the cooperative collision avoidance task of one robot depends on other robots' behaviors, the satisfaction of all the tasks may be conflicting. In this work, a distributed model predictive control (DMPC) strategy is proposed for conflicting temporal logic tasks. First, signal temporal logic (STL) is adopted to formally describe the temporal logic tasks. Based on robust semantics of STL formulas, a reference trajectory for the satisfaction degree of the task is designed to determine the short-term task in the optimization horizon. In the DMPC optimization problem, the compatibility constraints are introduced to redesign the collision avoidance constraints, such that the collision avoidance tasks can be fulfilled using neighbouring robots' predicted information of the last time instant. Then, the terminal constraint is designed by the shortterm motion task which enforces each robot to move towards the goal region within the specified time interval. For conflicting tasks, a slack parameter is introduced in the terminal set to relax the motion task of each robot. The recursive feasibility of the DMPC algorithm is guaranteed, and the relaxed temporal logic tasks are fulfilled. In the proposed method, the discrete events including limited time and logic requirements are incorporated into the DMPC optimization problem, such that the motion task can be satisfied as much as possible and safety requirements are fulfilled. The effectiveness of the algorithm is illustrated by simulation and experiment results.

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Coupled Multi-Robot Systems Under Linear Temporal Logic and Signal Temporal Logic Tasks

Cited by 25 publications

References 33 publications

Mixed-Integer Programming for Signal Temporal Logic with Fewer Binary Variables

Mixed-Integer Programming for Signal Temporal Logic with Fewer Binary Variables

FairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace

Distributed model predictive control for multi‐robot systems with conflicting signal temporal logic tasks

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