Abstract
In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in linear temporal logic, such as patrolling a set of rooms, we synthesize an automaton from which the robots can extract valid strategies. This centralized automaton is executed by the robots in the team at runtime, and in conjunction with a distributed motion planner that guarantees avoidance of moving obstacles. Our contribution is a correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles, guarantees satisfaction of the mission specification and resolves encountered deadlocks, where a moving obstacle blocks the robot temporally. Our method provides conditions under which deadlock will be avoided by identifying environment behaviors that, when encountered at runtime, may prevent the robot team from achieving its goals. In particular, (1) it identifies deadlock conditions; (2) it is able to check whether they can be resolved; and (3) the robots implement the deadlock resolution policy locally in a distributed manner. The approach is capable of synthesizing and executing plans even with a high density of dynamic obstacles. In contrast to many existing approaches to mission and motion planning, it is scalable with the number of moving obstacles. We demonstrate the approach in physical experiments with walking humanoids moving in 2D environments and in simulation with aerial vehicles (quadrotors) navigating in 2D and 3D environments.
Highlights
Mobile robots, such as package delivery robots, personal assistants, surveillance robots, cleaning robots, mobile manipulators or autonomous cars, execute possibly complex tasks and must share their workspace with other robots and humans
We describe an approach for navigation in dynamic environments that is able to satisfy a mission by resolving deadlocks, i.e. situations where a robot is temporally blocked by a dynamic obstacle and can not make progress towards achieving its mission, at runtime
It has been demonstrated that correct-by-construction synthesis from linear temporal logic (LTL) specifications has utility for composing basic actions to guarantee the task in response to sensor events (Kress-Gazit et al 2009; Ehlers et al 2015; Liu et al 2013; Wongpiromsarn et al 2012)
Summary
Mobile robots, such as package delivery robots, personal assistants, surveillance robots, cleaning robots, mobile manipulators or autonomous cars, execute possibly complex tasks and must share their workspace with other robots and humans. What makes this task challenging is that the environment in which the robots operate could be filled with static obstacles, as well as dynamic obstacles, such as people or doors, that could lead to collisions or block the robot. Many have focused on approaches for local motion planning (van den Berg et al 2009; AlonsoMora et al 2010) that offer collision avoidance in cluttered, dynamic environments. While these approaches are effective for point-to-point navigation, the planning is myopic and could fail when applied to complex tasks in complex workspaces. Such approaches are naturally conducive to mission specifications written in structured English (Kress-Gazit et al 2008), which are translatable into LTL formulas over variables representing the atomic actions and sensor events associated with the task
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