René Zapata

Combined Path-following and Obstacle Avoidance Control of a Wheeled Robot

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.

Reactive behaviors of fast mobile robots

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.

Self-adaptive monte carlo localization for mobile robots using range finders

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.

Self-adaptive Monte Carlo localization for mobile robots using range sensors

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.

True cooperation of robots in multi-arms tasks

This paper deals with the problem of true cooperation of robots working on the same task. We propose a method to carry out various tasks where both robots move together a same rigid object. Firstly we develop the theorical aspect and then we present the exchange problem between two grippers and an assembly task using two arms.

A guaranteed obstacle avoidance guidance system

This paper presents a practical solution to the guidance of a unicycle type robot, including path following, obstacle avoidance and the respect of wheeled actuation saturation constraint, without planning procedure. These results are based on an extension of previous results on path following control including actuation saturation constraints. New solution for obstacle avoidance, with guaranteed performance, is proposed.

Internet enhanced teleoperation toward a remote supervised delay regulator

Network based control of remote systems has been widely tackled and numerous methods have been proposed to deal with small and or constant delays. However, on long distance network, bandwidth and network delays may notably vary (beyond hundreds of milliseconds) according to events occurring along the transmission lines. This paper presents the long distance remote control of a small mobile robot. To face important network delay variations and possible connection rupture, a supervised delay regulator based architecture is proposed. Results of experiences that have been performed from Mexico to France are given and explained.

On the use of low-discrepancy sequences in non-holonomic motion planning

In this article, a recently developed approach for robot motion planning is extended and applied to non-holonomic mobile robots. This approach replace random sampling by deterministic one. We present several implementations of PRM-based planners: 1) Classical PRM with deterministic sampling and random sampling, 2) Deterministic and random Lazy-PRM, and 3) Lattice-based PRM. We have used several low-discrepancy sequences (Halton, Hammersley, Faure, and Sobol) and low-discrepancy lattices. Experimental results show that the deterministic variants of the PRM offer performance advantages in comparison to the original PRM.

Collision avoidance for a two-arm robot by reflex actions: Simulations and experimentations

This paper addresses the problem of collision avoidance between the two arms of a two-arm robot. In our case, the problem is solved through the reflex action theory. In fact, one arm is protected by virtual protection zones. When an unscheduled event occurs, in other words when the second arm is going to collide with the first one, the shape of the protection zones is modified. Then, a motion is computed for the first arm to rebuild the initial shape of the protection zones. The use of different shapes for the protection zones is explained and justified. Several strategies to compute the motion are proposed and discussed. Finally, this method is tested in simulation and on real experiments for different kinds of two-arm robots: planar manipulators, PUMA 560 robots and parallelogram robots.

Self-adaptive monte carlo for single-robot and multi-robot localization

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 is able to solve the multi-robot localization problem as well as the single-robot localization problem. By employing a pre-caching technique to reduce the on-line computational burden, SAMCL is more efficient than regular MCL. We define the concept of Similar Energy Region (SER), which is a set of 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. Thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to need, SAMCL can solve position tracking, global localization and the kidnapped robot problem together. SAMCL can be extended to handle multi-robot localization through a Position Mapping (PM) algorithm. This algorithm enables one robot to calculate its possible positions according to positions of other robots and mutual relations between each other. The validity and the efficiency of our algorithm are demonstrated by experiments carried out with different intentions. Extensive experiment results are also given in this paper.