Event-Based Signal Temporal Logic Synthesis for Single and Multi-Robot Tasks

Gundana, David; Kress-Gazit, Hadas

doi:10.1109/lra.2021.3064220

“…Another drawback of these approaches is that the number of time steps needed might be too large for longhorizon planning. The proposed approach tackles this problem by using time-stamped waypoints instead of a fixed time step, which is also similar to event-based control (e.g., [18]) in the sense that each waypoint can be viewed as an event and between two consecutive events the control input does not change. A similar idea has been studied in [19], where the authors use zeroth-order hold control, i.e., the control signal is held at a time instant (waypoint) for a variable interval.…”

Section: A Related Workmentioning

confidence: 99%

Multi-Agent Motion Planning From Signal Temporal Logic Specifications

Sun

¹

,

Chen

²

,

Mitra

³

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonholonomic dynamics. Existing methods in multi-agent motion planning, especially those based on discrete abstractions and model predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the planning horizon. We present a method based on timed waypoints to address this issue. We show that timed waypoints can help abstract nonlinear behaviors of the system as safety envelopes around the reference path defined by those waypoints. Then the search for waypoints satisfying the STL specifications can be inductively encoded as a mixedinteger linear program. The agents following the synthesized timed waypoints have their tasks automatically allocated, and are guaranteed to satisfy the STL specifications while avoiding collisions. We evaluate the algorithm on a wide variety of benchmarks. Results show that it supports multi-agent planning from complex specification over long planning horizons, and significantly outperforms state-of-the-art abstraction-based and MPC-based motion planning methods. The implementation is available at https://github.com/sundw2014/STLPlanning.

show abstract

“…Another drawback of these approaches is that the number of time steps needed might be too large for longhorizon planning. The proposed approach tackles this problem by using time-stamped waypoints instead of a fixed time step, which is also similar to event-based control (e.g., [18]) in the sense that each waypoint can be viewed as an event and between two consecutive events the control input does not change. Similar idea has been studied in [19], where the users use zeroth-order hold control, i.e., the control signal is held at a time instant (waypoint) for a variable interval.…”

Section: A Related Workmentioning

confidence: 99%

Multi-agent Motion Planning from Signal Temporal Logic Specifications

Sun¹,

Chen²,

Mitra³

et al. 2022

Preprint

View full text Add to dashboard Cite

We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonholonomic dynamics. Existing methods in multi-agent motion planning, especially those based on discrete abstractions and model predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the planning horizon. We present a method based on timed waypoints to address this issue. We show that timed waypoints can help abstract nonlinear behaviors of the system as safety envelopes around the reference path defined by those waypoints. Then the search for waypoints satisfying the STL specifications can be inductively encoded as a mixedinteger linear program. The agents following the synthesized timed waypoints have their tasks automatically allocated, and are guaranteed to satisfy the STL specifications while avoiding collisions. We evaluate the algorithm on a wide variety of benchmarks. Results show that it supports multi-agent planning from complex specification over long planning horizons, and significantly outperforms state-of-the-art abstraction-based and MPC-based motion planning methods. The implementation is available at https://github.com/sundw2014/STLPlanning.

show abstract

“…The construction of the CBF based on navigation function ( 12 ) provides number of advantages compared to existing CBF-based STL motion planning methods [e.g., ( Lindemann and Dimarogonas, 2018 )]; First note that since the navigation function can encode unsafe regions (obstacles) through ζ ( x ), it obviates the need for the explicit definition of additional logical predicates corresponds to such unsafe regions, thus reducing the size of the STL specification. This reduction in the size of STL is particularly useful if this method is used in conjunction with a reactive STL (event-based STL) motion planning methodology ( Gundana and Kress-Gazit, 2021 ) that includes a prior higher-level automata synthesis step. Another advantage of the nonsmooth formulation is that not only paves the way for covering larger class of STL compared to those considered by Lindemann and Dimarogonas (2018) , but also eliminates the conservatism associated with under-approximation of minimum operator for the sake of smoothness (see Section 5.2 ).…”

Section: Technical Approachmentioning

confidence: 99%

“…The starting thread of work on STL motion planning relies on a computationally demanding mixed-integer linear programming process ( Raman et al, 2014 ; Sadraddini and Belta, 2015 ; Liu et al, 2017 ). The computational complexity of these methods makes real-time implementation particularly challenging, especially in the presence of dynamic obstacles ( Gundana and Kress-Gazit, 2021 ). Not surprisingly, Jones et al (2019) pre-compute the control before execution to overcome real-time implementation issues, at the cost of sensitivity to run-time disturbances.…”

Section: Introductionmentioning

confidence: 99%

Non-Smooth Control Barrier Navigation Functions for STL Motion Planning

Zehfroosh

¹

,

Tanner

²

2022

Front. Robot. AI

3

0

View full text Add to dashboard Cite

This paper reports on a new approach to Signal Temporal Logic (STL) control synthesis, that 1) utilizes a navigation function as the basis to construct a Control Barrier Function (CBF), and 2) composes navigation function-based barrier functions using nonsmooth mappings to encode Boolean operations between the predicates that those barrier functions encode. Because of these two key features, the reported approach 1) covers a larger fragment of STL compared to existing approaches, 2) alleviates the computational cost associated with evaluation of the control law for the system in existing STL control barrier function methodologies, and 3) simultaneously relaxes some of the conservativeness of smooth combinations of barrier functions as a means of implementing Boolean operators. The paper demonstrates the efficacy of this new approach with three simulation case studies, one aiming at illustrating how complex STL motion planning specification can be realized, the second highlights the less-conservativeness of the approach in comparison to the existing methods, and another that shows how this technology can be brought to bear to push the envelope in the context of human-robot social interaction.

show abstract

Event-Based Signal Temporal Logic Synthesis for Single and Multi-Robot Tasks

Cited by 33 publications

References 30 publications

Multi-Agent Motion Planning From Signal Temporal Logic Specifications

Multi-Agent Motion Planning From Signal Temporal Logic Specifications

Multi-agent Motion Planning from Signal Temporal Logic Specifications

Non-Smooth Control Barrier Navigation Functions for STL Motion Planning

Contact Info

Product

Resources

About