A robot, in order to be autonomous, needs some kind of representation of its surrounding environment. From a general point of view, basic robotic tasks (such as localization, mapping, object handling, etc.) can be carried out with only very simple geometric primitives, usually extracted from raw sensor data. But whenever an interaction with a human being is involved, robots must have an understanding of concepts expressed in human natural language. In most approaches, this is done through a prebuilt ontology. In this paper, we try to bridge the gap between data driven methods and semantic based approaches by introducing a 3-layer environment model based on “instances”: sensor data based observations of concepts stored in a knowledge graph. We will focus on our original object-oriented ontology construction and illustrate the flow of our model in a simple showcase.

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.

The use of wireless network signal strength proposed to estimate the relative position between vehicles in a robot formation set. We propose a relative leader-follower model which allows us to use the reception signal strength as control variable for robot stabilization in the formation. Vehicle sensors consist of wheel speed and steering angle, the WiFi data consist of reception signal strength (RSS) and the angle of the maximal RSS with respect to the robot orientation. A nonholonomic nonlinear model vehicle is considered, due to this nonlinearities an extended Kalman filter EKF is used. Simulation and experimental results of the proposed estimation strategy are presented.

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 work proposes a control architecture for the consistent motions of a group of nonholonomic robotic vehicles. We present a decentralized control strategy that permits each vehicle to autonomously compute an optimal trajectory by using only locally generated information. In addition, to reach the desired positions in the robot formation, each robot follows a trajectory that is adapted thanks to reactive terms incorporated in the path planning process. This allows the robot to avoid obstacles and to maintain communication with the rest of the formation. We provide the proof of the convergence of this algorithm. Simulation results validate and highlight the efficiency and relevance of this method.

International audience

The main goal of this paper is to define, study and analyse a remote control architecture for a set of nonholonomic robotic vehicles. This project (ACCORD) gathers three laboratories and the French Army Research Office. Each of these laboratories deals with a part of this multidisciplinary project which includes coordinated control, the control architecture, control with time delay and monitoring of the wireless network. In this paper, we propose to present the whole goal of this project including the basis experimental setup developed to validate our control algorithm. Secondly, we focus on a new decentralized control strategy that uses the leader-follower principle. The originality of this paper stems from the use of the signal level of the wireless connection as a control vector. Indeed, each vehicle is fitted with two wireless devices. One of them is equipped with a sector antenna fitted on a DC-motor to track the direction of better receiving level. Thus, it allows us to know the relative angular position of the follower pointing out the leader. By using the wireless technology as sensor instead of vision for instance, allows a longer distance of coordinated control loop between each vehicle (approx. 100 m) even if the GPS information is not available.

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.

This work deals with the sensor-based motion planning problem for car-like robots. Sensor-based versions of Lazy DRM and Lazy LRM are used to exploit the information obtained from sensors and to compute a feasible collision-free path. The algorithm tries to reach the goal, executing the local method in the known free region. If it succeeds, a path to the goal is found and the algorithm finishes. Otherwise, the algorithm executes more scans to extend its free space, an so on. We have performed some simulations that show the promise of our approach.

The aim of this article is to compare experimentally the use of quasi-random sampling techniques for nonholonomic path planning. The experiments are evaluated in the context of the probabilistic roadmap methods (PRM). Two quasi-random variants of PRM-based planners are proposed: (1) a classical PRM with quasi-random sampling, and (2) a quasi-random lazy-PRM. Both have been implemented for car-like robots, and are shown through experimental results to offer some performance advantages in comparison to their randomized counterparts.

We deal with the cooperation problem of two nonholomic mobile manipulators. We propose a leader-follower architecture for implementing robot coordination and a control algorithm for motion planning and force control. The Deformable Virtual Zone (DVZ) method is proposed to resolve the problem through the reflex action theory. The formulation of the DVZ principle is given and an extension to the mobile manipulator tasks is detailed.

This paper addresses the problem of reactive behaviours of mobile manipulators evolving in dynamic and unknown environments. The deformable virtual zone (DVZ) method is proposed to resolve the problem through the reflex action theory. The formulation of the DVZ principle is given and the extension to mobile manipulators is detailed. This approach allows to implement fast control laws and can be seen as an efficient low level algorithm for controlling motions to avoid unscheduled obstacles collisions. Simulation results are discussed, showing the effectiveness of the proposed method.

This paper deals with dynamic equilibrium of biped robots. We focus our attention on balance of a planar biped standing upon one small foot. The trunk and leg on contact can be seen as a double-inverted pendulum without actuators at the contact point but with a controlled counterweight corresponding to the free leg. We identify all the singular points of this structure before making a linearization and deriving a state feedback control law. A description of the prototype is then described.

The artificial potential fields (APF) method is attractive for mobile robot navigation, and solutions to the difficulties of their application are investigated here. The basic problem induced by early convergence in local minima is solved by using a systematic local reshaping of the APF. It enables one to destabilize, and to extract the robot from the local basin of attraction. A theoretical analysis of kinematic and dynamic constraints has led to the use of Speed Dependent APF (SDAPF) for avoidance. This concept allows one to generate a controlled centrifugal force that respects the constraints. Simulation results for data close to sensor capabilities are very satisfactory.

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.>

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.