Prediction and Planning are essential elements of successful human driving, making them equally important for autonomously driving systems. Many approaches achieve planning based on built-in world-knowledge. However, we show how a learning-based system can be extended to planning, needing little a priori knowledge. A car-like robot is trained by a human driver by constructing a database, where ...

In addition to the theoretical value of challenging optimal control problmes, recent progress in autonomous vehicles mandates further research in optimal motion planning for wheeled vehicles. Since current numerical optimal control techniques suffer from either the curse of dimensionality, e.g. the Hamilton-Jacobi-Bellman equation, or the curse of complexity, e.g. pseudospectral optimal control...

The path following is the automatic control of the mobile robot along the specified path without human interference. The proposed path following applies for the robot navigation in unknown environments, where the robot has no preliminary information about obstacles. This paper presents an innovative idea for the path following control that is to integrate the basic path following control with t...

The paper presents a navigation algorithm for dynamic, uncertain environment. The static environment is unknown, while moving pedestrians are detected and tracked on-line. The planning algorithm is based on an extension of the Rapidly-exploring Random Tree algorithm, where the likelihood of the obstacles trajectory and the probability of collision is explicitly taken into account. The algorithm...

An iterative learning controller for steering chained-form systems is presented. The learning algorithm relies on chained-form systems being linear under piecewise-constant inputs. The proposed control scheme requires the execution of a limited number of experiments in order to reach the desired state in finite time, with nice convergence and robustness properties with respect to modeling inacc...

The paper addresses the problem of autonomous navigation of a car-like robot evolving in an urban environment. Such an environment exhibits an heterogeneous geometry and is cluttered with moving obstacles. Furthermore, in this context, motion safety is a critical issue. The proposed approach to the problem lies in the design of perception and planning modules that consider explicitely the dynam...

Point clouds, made available through laser range finders, stereo cameras, or time of flight cameras, are frequently used in robot navigation systems. However, no unsupervised machine perception algorithm exists to provide understanding of the data; e.g. that a particular blob of points looks roughly like, say, a car. In this paper, we take steps towards such an algorithm based on sparse coding....

This paper addresses the problem of planning and controlling the actions of groups of mobile robots during the execution of cooperative tasks. We propose a general framework that transforms several cooperative tasks to the same basic problem, thus enabling a single solution for all of them. Using this approach a single team of robots is capable to perform many different tasks by providing each ...

A new technique for stabilizing nonholonomic systems to trajectories is presented. It is well known (see 2]) that such systems cannot be stabilized to a point using smooth-static state feedback. In this paper we suggest the use of control laws for stabilizing a system about a trajectory, instead of a point. Given a nonlinear system and a desired (nominal) feasible trajec-tory, the paper gives a...