In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization using self-adaptive samples, abbreviated as SAMCL. This algorithm employs a pre-caching technique to reduce the on-line computational burden. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all these three sub-problems together thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of our algorithm are demonstrated by experiments carried out with different intentions. Extensive experiment results and comparisons are also given in this paper.

This paper proposes an algorithm that drives a unicycle type robot to a desired path, including obstacle avoidance capabilities. The path following control design relies on Lyapunov theory, backstepping techniques and deals explicitly with vehicle dynamics. Furthermore, it overcomes initial condition constraint present in a number of path following control strategies described in the literature. This is done by controlling explicitly the rate of progression of a "virtual target" to be tracked along the path; thus bypassing the problems that arise when the position of the path target point is simply defined as the closest point on the path. The obstacle avoidance part is using the deformable virtual zone principle, that defines a safety zone around the vehicle, in which the presence of an obstacle induces an "intrusion of information" that drives the vehicle reaction. The overall algorithm is combined with a guidance solution that embeds the path following requirements in a desired intrusion information function, that steers the vehicle to the desired path while the DVZ is virtually keeping a minimal contact with the obstacle, implicitly bypassing it. Simulation and experimental results illustrate the performance of the control system proposed.

This paper addresses the design, development and implementation of a fast mobile robot (> 5m/s) evolving in a crowded and ill-structured environment. It then considers the intelligent observation of ill-structured environments. The purpose of the experiments carried out is to emphasize some aspects of the motion planning problem, namely fast collision avoidance procedures, low level control loops and state observation. A prototype developed around a classical hardware structure completely fulfils the initial requirements.>