Multi-robot exploration of unknown environments with identification of exploration completion and post-exploration rendez-vous using ant algorithms

International audienceThis paper presents a new ant algorithm for the navigation of several robots, whose objective is to autonomously explore an unknown environment. When the coverage is com- pleted, all robots move to a previously defined meeting point. The approach that we propose in this paper for solving this problem, considers that the robots build, while moving, a common and shared representation of the environment. In this representation, the environment is viewed as a graph (typically a set of connected cells in a regular grid), each grid cell having a local memory able to store a limited amount of data. A robot can write numbers on the cell on which it is lying. It can also read the values of the cells in its neighborhood, and perform some simple operations, such as computing the minimum of a set of values. Each robot is capable, contrary to most ant- based approaches, to determine, in a distributed way, when the environment coverage has completed. Few ant algorithms can do that. Brick&Mortar is one of them and this is why it retains a central place in our proposition. The novelty of our approach is that, due to an emerging property of the underlying algorithm, agents will finish their exploration at a pre-defined evacuation point. In addition, several improvements of the original Brick&Mortar algorithm are proposed in this paper, such as the possibility to use better local strategies at the robot level (using, for example, LRTA*). The paper also presents a set of benchmarks against the best existing ant algorithms on several widespread graph topologies

Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

Stupid robot tricks : a behavior-based distributed algorithm library for programming swarms of robots

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Page 127 blank.Includes bibliographical references (p. 123-126).As robots become ubiquitous, multiple robots dedicated to a single task will become commonplace. Groups of robots can solve problems in fundamentally different ways than individuals while achieving higher levels of performance, but present unique challenges for programming and coordination. This work presents a set of communication techniques and a library of behaviors useful for programming large groups, or swarms, of robots to work together. The gradient-flood communications algorithms presented are resilient to the constantly changing network topology of the Swarm. They provide real-time information that is used to communicate data and to guide robots around the physical environment. Special attention is paid to ensure orderly removal of messages. Decomposing swarm actions into individual behaviors is a daunting task. Complex and subtle local interactions among individuals produce global behaviors, sometimes unexpectedly so. The behavior library presented provides group behavior "building blocks" that interact in predictable manner and can be combined to build complex applications. The underlying distributed algorithms are scaleable, robust, and self-stabilizing. The library of behaviors is designed with an eye towards practical applications, such as exploration, searching, and coordinated motion. All algorithms have been developed and tested on a swarm of 100 physical robots. Data is presented on algorithm correctness and efficiency. stabilizing. The library of behaviors is designed with an eye towards practical applications, such as exploration, searching, and coordinated motion. All algorithms have been developed and tested on a swarm of 100 physical robots. Data is presented on algorithm correctness and efficiency.by James D. McLurkin.S.M

SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIOR

Swarm intelligence in multi-robot systems has become an important area of research within collective robotics. Researchers have gained inspiration from biological systems and proposed a variety of industrial, commercial, and military robotics applications. In order to bridge the gap between theory and application, a strong focus is required on robotic implementation of swarm intelligence. To date, theoretical research and computer simulations in the field have dominated, with few successful demonstrations of swarm-intelligent robotic systems. In this thesis, a study of intelligent foraging behavior via indirect communication between simple individual agents is presented. Models of foraging are reviewed and analyzed with respect to the system dynamics and dependence on important parameters. Computer simulations are also conducted to gain an understanding of foraging behavior in systems with large populations. Finally, a novel robotic implementation is presented. The experiment successfully demonstrates cooperative group foraging behavior without direct communication. Trail-laying and trail-following are employed to produce the required stigmergic cooperation. Real robots are shown to achieve increased task efficiency, as a group, resulting from indirect interactions. Experimental results also confirm that trail-based group foraging systems can adapt to dynamic environments