Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles

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 correctby-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.

Reactive Temporal Logic Planning for Multiple Robots in Unknown Environments

“…Reactive temporal logic planning algorithms that can account for map uncertainty in terms of incomplete environment models have been developed in [31]- [38]. Particularly, [31], [32] model the environment as transition systems which are partially known.…”

“…The works in [35], [36] propose methods to locally patch paths as transition systems, modeling the environment, change so that GR(1) (General Reactivity of Rank 1) specifications are satisfied. Reactive to LTL specifications planning algorithms are proposed in [37], [38], as well. Specifically, in [37], [38] the robot reacts to the environment while the task specification captures this reactivity.…”

Honeycomb Map: A Bioinspired Topological Map for Indoor Search and Rescue Unmanned Aerial Vehicles

“…With these policies, each robot makes the decision to change or wait for another one. A correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles are approach in [29], where has done an integration of a high-level mission planner with a local planner that guarantees collision-free motion in three-dimensional workspaces, when faced with both static and dynamic obstacles.…”

Cooperative Collision Avoidance for Nonholonomic Robots

“…This does not avoid deadlocks between two or more robots. Multi-robot deadlock situations can be resolved by employing a global path planner that guides the robot towards the goal [46] or a mission planner for global coordination and mission satisfaction [47].…”