Josep M. Mirats Tur

Control and simulation of a tensegrity-based mobile robot

Tensegrity structures can provide 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 twofold, first, to derive appropriate and general dynamic equations of motion to study the movement of such structures in the space; second to demonstrate, by means of simulation, that a tensegrity structure can execute any desired trajectory path by actuating some or all of its elements.

“Erratum to: A Closed-Form Expression for the Uncertainty in Odometry Position Estimate of an Autonomous Vehicle”

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.

Geographical information systems for map based navigation in urban environments

In order to solve most of the existing mobile robotics applications, the robot needs some information about its spatial environment encoded in what it has been commonly called a map. The knowledge contained in such a map, whatever approach is used to obtain it, will mainly be used by the robot to gain the ability to navigate in a given environment. We are describing in this paper, a method that allows a robot or team of robots to navigate in large urban areas for which an existing map in a standard human understandable fashion is available. As detailed maps of most urban areas already exist, it will be assumed that a map of the zone where the robot is supposed to work is given, which has not been constructed using the robots own sensors. We propose in this paper, the use of an existing Geographical Information System based map of an urban zone so that a robot or a team of robots can connect to this map and use it for navigation purposes. Details of the implemented system architecture as well as a position tracking experiment in a real outdoor environment, a University Campus, are provided.

Action evaluation for mobile robot global localization in cooperative environments

This work is about solving the global localization issue for mobile robots, operating in large and cooperative environments. It tackles the problem of estimating the pose of a robot, or team of robots in a map reference frame, given the map, the real-time data from the robot onboard sensors, and the real-time data coming from other robots or sensors in the environment. After a first step of position hypotheses generation, an efficient probabilistic active strategy selects an action, for a single lost robot case, or two joint actions when two lost robots are in a line of sight, so that the hypotheses set is best disambiguated. The action set is adapted to the multi-hypothesis situation, and action evaluation takes into account remote observations available in robot network systems. This paper presents the theoretical formulation for both non-cooperative, and cooperative cases. An implementation of the proposed strategy is discussed, and simulation results presented.

Efficient active global localization for mobile robots operating in large and cooperative environments

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 10.000 m2 are presented validating the presented approach and showing different behaviours for the single robot case and for the cooperative one.

VARIABLE SELECTION PROCEDURES AND EFFICIENT SUBOPTIMAL MASK SEARCH ALGORITHMS IN FUZZY INDUCTIVE REASONING

This paper describes two new suboptimal mask search algorithms for Fuzzy inductive reasoning (FIR), a technique for modelling dynamic systems from observations of their input/output behaviour. Inductive modelling is by its very nature an optimisation problem. Modelling large-scale systems in this fashion involves solving a high-dimensional optimisation problem, a task that invariably carries a high computational cost. Suboptimal search algorithms are therefore important. One of the two proposed algorithms is a new variant of a directed hill-climbing method. The other algorithm is a statistical technique based on spectral coherence functions. The utility of the two techniques is demonstrated by means of an industrial example. A garbage incinerator process is inductively modelled from observations of 20 variable trajectories. Both suboptimal search algorithms lead to similarly good models. Each of the algorithms carries a computational cost that is in the order of a few percent of the cost of solving the complete optimisation problem. Both algorithms can also be used to filter out variables of lesser importance, i.e. they can be used as variable selection tools.

Real-Time Software for Mobile Robot Simulation and Experimentation in Cooperative Environments

This paper presents the software being developed at IRI (Institut de Robotica i Informatica Industrial) for mobile robot autonomous navigation in the context of the european project URUS (Ubiquitous Robots in Urban Settings). In order that a deployed sensor network and robots operating in the environment cooperate in terms of information sharing, main requirements are real-time performance and the integration of information coming from remote machines not onboard the robot. Moreover, the project involves a group of eleven industrial and academic partners, therefore software integration issues are critical. The proposed software framework is based on the YARP middleware and has been tested in real and simulated experiments.

Dynamic equations of motion for a 3-bar tensegrity based mobile robot

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.

On the selection of variables for qualitative modelling of dynamical systems

Behavioural modelling of physical systems from observations of their input/output behaviour is an important task in engineering. Such models are needed for fault monitoring as well as intelligent control of these systems. The paper addresses one subtask of behavioural modelling, namely the selection of input variables to be used in predicting the behaviour of an output variable. A technique that is well suited for qualitative behavioural modelling and simulation of physical systems is Fuzzy Inductive Reasoning (FIR), a methodology based on General System Theory. Yet, the FIR modelling methodology is of exponential computational complexity, and therefore, it may be useful to consider other approaches as booster techniques for FIR. Different variable selection algorithms: the method of the unreconstructed variance for the best reconstruction, methods based on regression coefficients (OLS, PCR and PLS) and other methods as Multiple Correlation Coefficients (MCC), Principal Components Analysis (PCA) and Cluster analysis are discussed and compared to each other for use in predicting the behaviour of a steam generator. The different variable selection algorithms previously named are then used as booster techniques for FIR. Some of the used linear techniques have been found to be non-effective in the task of selecting variables in order to compute a posterior FIR model. Methods based on clustering seem particularly well suited for pre-selecting subsets of variables to be used in a FIR modelling and simulation effort.

IMU and cable encoder data fusion for in-pipe mobile robot localization

Inner pipe inspection of sewer networks is a hard and tedious task, due to the nature of the environment, which is narrow, dark, wet and dirty. So, mobile robots can play an important role to solve condition assessment of such huge civil infrastructures, resulting in a clear benefit for citizens. One of the fundamental tasks that a mobile robot should solve is localization, but in such environments GPS signal is completely denied, so alternative methods have to be developed. Visual odometry and visual SLAM are promising techniques to be applied in such environments, but they require a populated set of visual feature tracks, which is a requirement that can not be fulfilled in such environments in a continuous way. With the aim of designing robust and reliable robot systems, this paper proposes and evaluates a complementary approach to localize a mobile robot, which is based on sensor data fusion of an inertial measurement unit and of a cable encoder, which measures the length of an unfolded cable, from the starting point of operations up to the tethered robot. Data fusion is based on optimization of a set of windowed states given the sensor measurements in that window. The paper details theoretical basis, practical implementation issues and results obtained in testing pipe scenarios.