We present to the robotic community a fully autonomous navigation solution for mobile robots operating in urban pedestrian areas. We introduce our robots and the experimental zone, overview the architecture of the navigation framework, and present the results after 3.5km of autonomous navigation. We expose the main lessons learnt by the scientific team and identify the issues to improve future works.

This paper presents an asynchronous particle filter algorithm for mobile robot position tracking, taking into account time considerations when integrating observations being delayed or advanced from the prior estiamate time point. The interest of that filter lies in cooperative environments and in fast vehicles. The paper studies the first case, where a sensor network shares perception data with running robots that receive accurate obeservations with large delays due to acquisition, processing and wireless communications. Promising simulated results comparing a basic particle filter and the proposed one are shown. The paper also investigates a situation where a robot is tracking its position, fusing only odometry and observations from a camera network partially covering the robot path.

This paper presents a novel and efficient framework to the active map-based global localization problem for mobile robots operating in large and cooperative environments. The paper proposes a rational criteria to select the action that minimizes the expected number of remaining position hypotheses, for the single robot case and for the cooperative case, where the lost robot takes advantage of observations coming from a sensor network deployed on the environment or from other localized robots. Efficiency in time complexity is achieved thanks to reasoning in terms of the number of hypotheses instead of in terms of the belief function. Simulation results in a real outdoor environment of 10.000m2are presented validating the presented approach and showing different behaviours for the single robot case and for the cooperative one.

Tensegrity structures appeared in the science community about half a century ago, but they have already been applied to several heterogeneous research fields, such as architecture, civil engineering, space and even biology. Such structures keep a stable volume in space due to an intricate balance of forces between a disjoint set of rigid elements (bars) and a continuous set of tensile elements (cables). The use of tensegrity structures in robotics is still new and there exist only a handful of works about this subject. Some of their main features such as light weight, flexibility, energetic efficiency and redundancy, make them interesting candidates for both mobile robots and manipulators. In this paper, a new method to detect and avoid both internal collisions between the structure members and external collisions with the environment is presented. In this way, we are providing a fundamental tool to develop more complete form-finding procedures and path-planning strategies for tensegrity structures.

Solving the navigation issue for a mobile robot in a 2D space requires using internal and external sensors, so researchers try to fuse data from different sensors using methods as for example Kalman filtering. Those methods need an estimation of the uncertainty in the pose estimates obtained from the sensory system, usually expressed by a covariance matrix and obtained from experimental data. In a previous work, a general method to obtain the uncertainty in the odometry pose estimate was proposed. Here, with the aim of assessing the generality of the method, the general formulation is particularized for a given differential driven robot. Its kinematic model relates two internal measurements: the instantaneous displacement of both, right and left wheels. The obtained formulation is validated experimentally and compared against Kalman filtering.

Tensegrity structures can give a new approach to the construction of mobile robots with different shapes and properties that usual robots, wheeled or legged, do not have. Tensegrity are light, deformable structures that may be able to adapt their form to unconstrained environments. The main issue of this paper is to present the dynamic equations of motion for such structures, analysed from a Lagrangian point of view, and thinking in using them as mobile robots of arbitrary form and size.

This article presents a data fusion method which seeks to obtain better pose estimation of a mobile robot through obtaining a more accurate covariance uncertainty matrix. We seek to compute the state covariance without using the first-order linear approximations of the extended Kalman filter. We consider, unlike standard work done in error propagation and data fusion, the possible correlation between the different sensor pose estimates, odometry and DGPS for the present work, and the autocorrelation of some of the variables involved in the fusion (DGPS data, for the particular case herein presented). Computation of the covariances of each sensor data vector is presented so it takes into account the vehicle kinematics, and hence, its particular characteristics. In order to validate the presented approach, a real outdoor navigation experiment is presented fusing odometry and DGPS data.

The uncertainty of the odometry position estimate for a given vehicle can be obtained from the definition of the covariance matrix by using its kinematic model as well as its sensory system data. This communication item extends a previous work inby giving the appropriate guidelines so as to compute the cross-covariance terms between the previous position estimate and the actual increments of position. This is necessary in general because the orientation errors on the robot's previous position will affect the calculation of the actual increments of position.

Using internal and external sensors to provide position estimates in a two-dimensional space is necessary to solve the localization and navigation problems for a robot or an autonomous vehicle (AV). Usually, a unique source of position information is not enough, so researchers try to fuse data from different sensors using several methods, as, for example, Kalman filtering. Those methods need an estimation of the uncertainty in the position estimates obtained from the sensory system. This uncertainty is expressed by a covariance matrix, which is usually obtained from experimental data, assuming, by the nature of this matrix, general and unconstrained motion. We propose in this paper a closed-form expression for the uncertainty in the odometry position estimate of a mobile vehicle, using a covariance matrix whose form is derived from the cinematic model. We then particularize for a nonholonomic Ackerman driving-type AV. Its cinematic model relates the two measures being obtained for internal sensors: the velocity, translated into the instantaneous displacement; and the instantaneous steering angle. The proposed method is validated experimentally, and compared against Kalman filtering.