A Multi-robot System for Adaptive Exploration of a Fast-changing Environment: Probabilistic Modeling and Experimental Study

This paper presents an experiment in collective robotics which investigates the influence of communication, of learning and of the number of robots in a specific task, namely learning the topography of an environment whose features change frequently. We propose a theoretical framework based on probabilistic modeling to describe the system's dynamics. The adaptive multi-robot system and its dynamic environment are modeled through a set of probabilistic equations which give an explicit description of the influence of the different variables of the system on the data-collecting performance of the group. Further, we implement the multi-robot system in experiments with a group of Khepera robots and in simulation using Webots, a three-dimensional simulator of Khepera robots. The robots are controlled by a distributed architecture with an associative-memory type of learning algorithm. Results show that the algorithm allows a group of robots to keep an up-to-date account of the environmental state when this chang...

[1]  Philip Mächler.  Robot Odometry Correction Using Grid Lines on the Floor , 1998 .

[2]  Aude Billard,et al.  DRAMA, a Connectionist Architecture for Control and Learning in Autonomous Robots , 1999, Adapt. Behav..

[3]  Maja J. Mataric,et al.  Using communication to reduce locality in distributed multiagent learning , 1997, J. Exp. Theor. Artif. Intell..

[4]  Rolf Pfeifer,et al.  Sensory - motor coordination: The metaphor and beyond , 1997, Robotics Auton. Syst..

[5]  Maja J. Mataric,et al.  Issues and approaches in the design of collective autonomous agents , 1995, Robotics Auton. Syst..

[6]  Sebastian Thrun,et al.  Explanation-based neural network learning a lifelong learning approach , 1995 .

[7]  Aude Billard,et al.  Experiments in social robotics: grounding and use of communication in autonomous agents , 2000 .

[8]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[9]  Nicholas Gessler.  Growing Artificial Societies—Social Science from the Bottom Up. , 1997, Artificial Life.

[10]  Lynne E. Parker,et al.  Heterogeneous multi-robot cooperation , 1994 .

[11]  Alcherio Martinoli,et al.  Towards a Reliable Set-Up for Bio-Inspired Collective Experiments with Real Robots , 1997, ISER.

[12]  Dario Floreano,et al.  Evolutionary Robotics in Artificial Life and Behavior Engineering , 1998 .

[13]  R. Siegwart,et al.  ROBOT POSITIONING BY SUPERVISED AND UNSUPERVISED ODOMETRY CORRECTION , 1998 .

[14]  Gregory Dudek,et al.  Multi-Robot Exploration of an Unknown Environment, Efficiently Reducing the Odometry Error , 1997, IJCAI.

[15]  Lynne E. Parker.  An Experiment in Mobile Robotic Cooperation , 1994 .

[16]  Tucker R. Balch,et al.  Communication in reactive multiagent robotic systems , 1995, Auton. Robots.

[17]  William W. Cohen.  Adaptive mapping and navigation by teams of simple robots , 1996, Robotics Auton. Syst..

[18]  Alcherio Martinoli,et al.  Swarm intelligence in autonomous collective robotics , 1999 .

[19]  Eiichi Yoshida,et al.  Local communication of multiple mobile robots: Design of optimal communication area for cooperative tasks , 1998, J. Field Robotics.

[20]  Michael R. M. Jenkin,et al.  A taxonomy for multi-agent robotics , 1996, Auton. Robots.

[21]  E. Bonabeau,et al.  Fixed response thresholds and the regulation of division of labor in insect societies , 1998 .

[22]  Francesco Mondada,et al.  Understanding collective aggregation mechanisms: From probabilistic modelling to experiments with real robots , 1999, Robotics Auton. Syst..