Dynamic hierarchical reactive controller synthesis

Abstract

In the formal approach to reactive controller synthesis, a symbolic controller for a possibly hybrid system is obtained by algorithmically computing a winning strategy in a two-player game. Such game-solving algorithms scale poorly as the size of the game graph increases. However, in many applications, the game graph has a natural hierarchical structure. In this paper, we propose a modeling formalism and a synthesis algorithm that exploits this hierarchical structure for more scalable synthesis. We define local games on hierarchical graphs as a modeling formalism that decomposes a large-scale reactive synthesis problem in two dimensions. First, the construction of a hierarchical game graph introduces abstraction layers, where each layer is again a two-player game graph. Second, every such layer is decomposed into multiple local game graphs, each corresponding to a node in the higher level game graph. While local games have the potential to reduce the state space for controller synthesis, they lead to more complex synthesis problems where strategies computed for one local game can impose additional requirements on lower-level local games. Our second contribution is a procedure to construct a dynamic controller for local game graphs over hierarchies. The controller computes assume-admissible winning strategies that satisfy local specifications in the presence of environment assumptions, and dynamically updates specifications and strategies due to interactions between games at different abstraction layers at each step of the play. We show that our synthesis procedure is sound: the controller constructs a play that satisfies all local specifications. We illustrate our results through an example controlling an autonomous robot in a building with known floor plan and provide simulation results using an implementation of our algorithm on top of LTLMoP.

Logic programming for deliberative robotic task planning

“…A relevant tool for LTL in the robotic community is LTLMoP by Finucane et al (2010), to implement temporal task specifications in the ROS framework. LTLMoP has been used for multi-target navigation (Kumar and Kala 2016); task planning in a grocery store with an Aldebaran Nao robot (Jing et al 2012); multi-robot cooperative task planning Kwon 2012, 2014); search-and-rescue mission in partially known environment with a Pioneer 3-DX robot (Sarid et al 2013); task planning for modular robots (Jing et al 2016); multi-task planning for a patrol robot (Liu et al 2017); reactive task planning based on game theory (Schmuck et al 2017). Also relevant is the application of LTLMoP to the problem of explanation of unsynthetizable plans for robotics (Raman and Kress-Gazit 2012), which is useful to enhance the interpretability of the robotic system.…”