The recent development of a new kind of public transportation system relies on a particular double-steering kinematic structure enhancing maneuverability in cluttered environments such as downtown areas. We call bi-steerable car a vehicle showing this kind of kinematics. We present experimental results, from map-building to trajectory tracking, aiming at validating the theoretical considerations obtained recently for the general bi-steerable system. These results are a first step towards the motion autonomy of this kind of transportation system.

One of the common ways of localization in robotics is the triangulation using a system composed of a sensor and some landmarks (which can be artificial or natural). This paper presents a framework, namely the localization space, in order to deal with problems such as the landmark placement and motion planning including the localization constraint. Based on this framework, we present general approaches to the optimal distribution of the landmarks or to the computation of reliable trajectories. The case of a mobile robot equipped with an orientable sensor (such as a pan vision system) is presented to illustrate the formal concepts and to show the practical relevance of the proposed tools.

This paper focuses on real-time global motion planning in a dynamic environment. Most of the existing approaches suffer from heavy computation and cannot satisfy real-time constraints. In this paper we present a novel approach, based on the Non-Linear Velocity Obstacle Concept, that maps the positions of the obstacles and their known or estimated trajectories directly in the space of the velocities admissible by our robot, taking into account its kinematic and dynamics constraints. The result is a map of all the collision free velocities. We present a few improvements to this approach and introduce the notion of risk to perform local goal-oriented obstacle-avoidance. Combining it with graph-expansion techniques, we propose to extend the system to an incremental global motion planner in a dynamic environment.

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First, when no identifying marks axe set on the landmarks, their identification by a robust algorithm is a complex problem which may be solved thanks to correspondence graphs. Second, when the localization system has no a priori information about its environment, it has to build its own map in parallel with estimating its position, a problem known as the simultaneous localization and mapping (SLAM). Recent works have proposed to solve this problem based on building a map made of invariant features. This paper describes the algorithms and data structure needed to deal with landmark matching, robot localization and map building in a single efficient process, unifying the previous approaches. Experimental results axe presented using an outdoor robot car equipped with a 2D scanning laser sensor.

This paper generalizes the concept of velocity obstacles given by Fiorini et al. (1998) to obstacles moving along arbitrary trajectories. We introduce the nonlinear velocity obstacle, which takes into account the shape, velocity and path curvature of the moving obstacle. The nonlinear v-obstacle allows selecting a single avoidance maneuver (if one exists) that avoids any number of obstacles moving on any known trajectories. For unknown trajectories, the nonlinear v-obstacles can be used to generate local avoidance maneuvers based on the current velocity and path curvature of the moving obstacle. This elevates the planning strategy to a second order method, compared to the first order avoidance using the linear v-obstacle, and zero order avoidance using only position information. Analytic expressions for the nonlinear v-obstacle are derived for general trajectories in the plane. The nonlinear v-obstacles are demonstrated in a complex traffic example.

This paper presents a novel control architecture for a car-like vehicle moving in a dynamic and partially known environment. The key idea is to plan and carry out sensor-based maneuvers. The paper focuses on the reactive part of the architecture that features control experts, i.e., parametrized control programs adapted to a specific maneuver and capable to react in real-time to unforeseen events. This paper aims to show why and how we made use of artificial neural network to improve the performance of our control architecture. Simulation and experimental results obtained with an automatic car are presented to illustrate the advantages of our approach.

The Cycab robot is a new mobile platform already used in several research labs and meant for different applications such as public transportation in airport terminals, self-service cars in pedestrian zones, etc. From a kinematic point of view, the Cycab specificity is the turning of its rear wheels as a linear function of the steering angle of the front wheels. This feature enhances the maneuverability of the Cycab in cluttered environments. However, the associated kinematic model is new and different from the ones commonly treated in the robotics literature (such as the car-like, tractor-trailer, etc). To our knowledge such a system has not yet been studied and especially, there is no existing motion planner for this system. We tackle the study of this new nonholonomic system, by establishing its kinematic model and proving its differential flatness property. From this study we deduce a first motion planner for the system.

One of the approaches to collision-free nonholonomic motion planning is the "approximation method". The corresponding planners compute first a holonomic path among obstacles before approximating it by a concatenation of feasible collision-free paths. These methods are one of the rare ones which can lead to exact and complete planners. However, the performance of these planners (in terms of computation time and the complexity of the solution) is highly influenced by the "quality" of the first geometric path. We suggest a way to estimate this quality and present the first general approach leading to an improvement of the quality of the geometric path. This approach is based on local deformations of a holonomic potential field with respect to the nonholonomic constraints of the system.

Optimal paths are in many ways interesting to motion planning. Not only they are obviously the most interesting ones with respect to the optimized criterion, but also they offer a way of studying the topological aspects associated to the controllability of the system. Many works have been carried out successfully on the optimal paths for unicyle and carlike system. This paper aims to extend those results when adding trailers. Characterizing the time optimal paths for a mobile robot with n-trailers is an ambitious task. As a first step, we give an explicit description of the abnormal and singular extremals for a mobile robot with one trailer. This is done with the help of the maximum principle.

This paper deals with motion planning and control for mobile robots. The various components of an integrated architecture for the mobile robot Hilare pulling a trailer are presented. The nonholonomic path planner is based on an original steering method accounting for the small-time controllability of the system. Then the path is transformed into a trajectory by including the dynamical constraints of the system (bounded velocity and bounded acceleration). Finally, the motion control is addressed. Due to the geometric transformation for a virtual robot, we show how to reduce the problem to a classical approach of trajectory tracking for a mobile robot moving forward only. The experimental results presented include two types of robot-trailer connection systems.

Deals with nonholonomic motion planning including obstacle avoidance capabilities. We show that the methods developed in the absence of obstacles can be extended to the problem of obstacle avoidance, provided that they verify a topological property. Such steering methods allow us to design exact and complete collision-free path planners for a large family of systems. We show that the steering method using sinusoidal inputs applied to chained form systems fulfills the required conditions, and we illustrate its integration in collision-free path planning schemes through the tractor-trailers example.

This paper describes a real experiment in nonholonomic motion planning and control. It was performed on the mobile robot Hilare-2-bis pulling a trailer.

This paper deals with nonholonomic motion planning including obstacle avoidance capabilities. We show that the methods developed in absence of obstacles can be extended to the problem of obstacle avoidance, provided that they verify a topological property. This property is proved for the method using sinusoidal inputs applied to chained form systems, and illustrated through the tractor-trailer example.