Robot Collaboration for Simultaneous Map Building and Localization

Collaboration has been acknowledged as an excellent tool to compensate for limitations and shortcoming of individuals in order to achieve complex tasks. Yet, robotics collaboration has been recognized as an independent entity on its own within robotics community as suggested by the emerging literature and the growing applications like RoboCup, FIRA competitions (Kitaneo, 1997), autonomous vehicles for space/submarine exploration (Todd and Pomerleau, 1996). This promises a leading future for this field in a medium term. However, the development of effective collaboration schemes is subject to several challenges. This concerns aspects related to robot localization (absolute and/or relative localization), environment map building, sensor modelling and fusion, game-theoretic scenarios, collaboration/cooperation modes, user’s interface and control modes, among others, see, for instance, (Mataric, 1998). This chapter aims to contribute at least to the first two aspects of the aforementioned challenges where the issue of dynamic localization and map building using two miniature Khepera® robots is tackled. An extendedKalman filter based approach is developed and implemented in order to model the state of the robot and various observations as well as to determine and update the positioning estimates of both robots together with the identified landmarks in the environment. A virtual representation of the map and robots is also put forward using OpenGL for 3D representation. While the developed interface uses enhanced help capabilities in case of unsafe or non-tolerated manipulations by the user. The issue of mobile localization and map building has been a challenging issue that faced the robotics community since the eighties due the debatable issues related to the state and observation modelling, map initialization and building, and convergence of the estimation process, among others. This led to the development of several techniques to overcome the above challenges. Since the pioneering work of Smith and Cheesman (1986), a bridge from geometrical features and stochastic models has been established, which led to a variety of algorithms, mainly using Kalman filter (Geb, 1986) or its variants, whose feasibility and satisfactory performances have been demonstrated both from theoretical and practical perspectives through the convergence properties of the algorithms and the successful applications. The concept of robot localization and map building is often referred to as SLAM (Simultaneous Localization And Mapping), in which both the environment map represented as a set of landmarks and the robot states are estimated simultaneously by augmenting the state vector to