Reactive and human-in-the-loop planning and control of multi-robot systems under LTL specifications in dynamic environments

TL;DR

This paper presents reactive and human-in-the-loop planning strategies for multi-robot systems operating in dynamic environments with moving obstacles, ensuring task satisfaction under LTL specifications through online re-planning and control methods.

Contribution

It introduces novel online re-planning algorithms for multi-robot systems in dynamic environments, including local trajectory generation and model predictive control, with human-in-the-loop integration.

Findings

Proposed algorithms effectively handle dynamic obstacles and environment changes.

Guarantees task satisfaction under feasible conditions.

Validated through experiments using ROS software package.

Abstract

This paper investigates the planning and control problems for multi-robot systems under linear temporal logic (LTL) specifications. In contrast to most of existing literature, which presumes a static and known environment, our study focuses on dynamic environments that can have unknown moving obstacles like humans walking through. Depending on whether local communication is allowed between robots, we consider two different online re-planning approaches. When local communication is allowed, we propose a local trajectory generation algorithm for each robot to resolve conflicts that are detected on-line. In the other case, i.e., no communication is allowed, we develop a model predictive controller to reactively avoid potential collisions. In both cases, task satisfaction is guaranteed whenever it is feasible. In addition, we consider the human-in-the-loop scenario where humans may additionally take control of one or multiple robots. We design a mixed initiative controller for each robot to prevent unsafe human behaviors while guarantee the LTL satisfaction. Using our previous developed ROS software package, several experiments are conducted to demonstrate the effectiveness and the applicability of the proposed strategies.