Cooperation of multiple autonomous robots and analysis of their swarm behavior

Bogdan Czejdo,Sambit Bhattacharya,Waldemar Grabski,Wiktor Daszczuk

doi:10.24136/atest.2018.516

Abstract

In this paper, we extended previous studies of cooperating autonomous robots to include situations when environmental changes and changes in the number of robots in the swarm can affect the efficiency to execute tasks assigned to the swarm of robots. We have presented a novel approach based on partition of the robot behaviour. The sub-diagrams describing sub-routs allowed us to model advanced interactions between autonomous robots using limited number of state combinations avoiding combinatorial explosion of reachability. We identified the systems for which we can ensure the correctness of robots interactions. New techniques were presented to verify and analyze combined robots’ behaviour. The partitioned diagrams allowed us to model advanced interactions between autonomous robots and detect irregularities such as deadlocks, lack of termination etc. The techniques were presented to verify and analyze combined robots’ behaviour using model checking approach. The described system, Dedan verifier, is still under development. In the near future, timed and probabilistic verification are planned..

Highlights

The growing scope of applications of swarms of autonomous mobile devices is related with their natural ability to respond properly to malfunctions/collisions of individual robots and to environmental changes
We extended previous studies of cooperating autonomous robots to include situations when environmental changes and changes in the number of robots in the swarm can affect the efficiency to execute tasks assigned to the swarm of robots
In the paper we attempt to answer several research questions. a) How state diagrams can be used to efficiently describe the large number of robots? b) How much we can scale up our solutions to guarantee the proper cooperation for the swarms of robots in terms of distributed termination and deadlock avoidance. c) How much we can scale up our solutions to guarantee the proper cooperation for the robot swarms in terms of Mission objectives e.g. coverage of the area, frequency of checking each protected place etc. d) How much we can scale up our solutions to guarantee the proper cooperation for the robot swarms, when we respond adequately to failures/collisions of individual robots and environmental changes

Summary

Introduction

The growing scope of applications of swarms of autonomous mobile devices (robots) is related with their natural ability to respond properly to malfunctions/collisions of individual robots and to environmental changes. B) How much we can scale up our solutions to guarantee the proper cooperation for the swarms of robots in terms of distributed termination and deadlock avoidance. The model checking method for state diagrams can identify problems such as deadlocks or live-locks and offer the robots designer a set of ready-to-use algorithms and techniques for the analysis of complete swarm based system properties. Just as in a case of deadlock detection, dynamic (runtime) methods of termination detection require some instrumentation of a system It is typically sending messages reporting the states of individual processes, and a mechanism of combining them into a global decision on distributed termination [13] [19] [20]. We assume that each central chamber QNW, QNE, QSW and QSE has opening to two other central chambers and doors/opening for 2 side chambers

Utilities for Rapid Generation of State Diagrams for Swarm Robot Navigation

Conclusions