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 the initial condition constraints 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 uses the Deformable Virtual Zone (DVZ) principle. This principle 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, which steers the vehicle to the desired path while the DVZ ensures minimal contact with the obstacle, implicitly bypassing it. Simulation and experimental results illustrate the performance of the control system proposed.

Abstract-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 algorithm using self-adaptive samples, abbreviated as SAMCL. By employing a pre-caching technique to reduce the on-line computational burden, SAMCL is more efficient than regular MCL. 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 the SAMCL algorithm are demonstrated by both simulations and 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 technics 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 article addresses the modeling of the reactive behaviors of a mobile robot moving in unstructured and dynamic environments. The main focus is on the software structure devoted to obstacle avoidance when geometric models do not exist and when obstacles can be dynamic. The first part concerns the model of the robot/environment interaction. This model is based on the definition of the interaction component (deformable virtual zone) of an internal state of the robot and leads to avoidance‐oriented control laws. The second part describes the derivation of the state equation and one of its implementations on a fast outdoor mobile robot. © 1994 John Wiley & Sons, Inc.

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