This paper reports an electroencephalogram-based brain-actuated telepresence system to provide a user with presence in remote environments through a mobile robot, with access to the Internet. This system relies on a P300-based brain-computer interface (BCI) and a mobile robot with autonomous navigation and camera orientation capabilities. The shared-control strategy is built by the BCI decoding of task-related orders (selection of visible target destinations or exploration areas), which can be autonomously executed by the robot. The system was evaluated using five healthy participants in two consecutive steps: 1) screening and training of participants and 2) preestablished navigation and visual exploration telepresence tasks. On the basis of the results, the following evaluation studies are reported: 1) technical evaluation of the device and its main functionalities and 2) the users' behavior study. The overall result was that all participants were able to complete the designed tasks, reporting no failures, which shows the robustness of the system and its feasibility to solve tasks in real settings where joint navigation and visual exploration were needed. Furthermore, the participants showed great adaptation to the telepresence system.

Scan matching techniques have been widely used to compute the displacement of robots. This estimate is part of many algorithms addressing navigation and mapping. This paper addresses the scan matching problem in three dimensional workspaces. We propose an generalization of the Metric based Iterative Closest Point (MbICP) to the 3D case. The main contribution is the development of all the mathematical tools required to formulate the ICP with this new metric, including the derivation of point to plane distances based on the new metric. We also provide experimental results to evaluate the algorithms and different combinations of ICP and MbICP to illustrate the advantages of the metric based approach.

Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, there are situations in which this reward is hard to encode, and so would require a supervised approach of reinforcement learning, where a user directly types the reward on each trial. This paper proposes to use brain activity recorded by an EEG-based BCI system as reward signals. The idea is to obtain the reward from the activity generated while observing the robot solving the task. This process does not require an explicit model of the reward signal. Moreover, it is possible to capture subjective aspects which are specific to each user. To achieve this, we designed a new protocol to use brain activity related to the correct or wrong execution of the task. We showed that it is possible to detect and classify different levels of error in single trials. We also showed that it is possible to apply reinforcement learning algorithms to learn new similar tasks using the rewards obtained from brain activity.

This paper describes an EEG-based human brain-actuated robotic system, which allows performing navigation and visual exploration tasks between remote places via Internet, using only brain activity. In operation, two teleoperation modes can be combined: robot navigation and camera exploration. In both modes, the user faces a real-time video captured by the robot camera merged with augmented reality items. In this representation, the user concentrates on a target area to navigate to or visually explore; then, a visual stimulation process elicits the neurological phenomenon that enables the brain-computer system to decode the intentions of the user. In the navigation mode, the target destination is transferred to the autonomous navigation system, which drives the robot to the desired place while avoiding collisions with the obstacles detected by the laser scanner. In the camera mode, the camera is aligned with the target area to perform an active visual exploration of the remote scenario. In June 2008, within the framework of the experimental methodology, five healthy subjects performed pre-established navigation and visual exploration tasks for one week between two cities separated by 260 km. On the basis of the results, a technical evaluation of the device and its main functionalities is reported. The overall result is that all the subjects were able to successfully solve all the tasks reporting no failures, showing a high robustness of the system.

This paper describes a new non-invasive brain-actuated wheelchair that relies on a P300 neurophysiological protocol and automated navigation. In operation, the subject faces a screen with a real-time virtual reconstruction of the scenario, and concentrates on the area of the space to reach. A visual stimulation process elicits the neurological phenomenon and the EEG signal processing detects the target area. This target area represents a location that is given to the autonomous navigation system, which drives the wheelchair to the desired place while avoiding collisions with the obstacles detected by the laser scanner. The accuracy of the brain-computer interface is above 94% and the flexibility of the sensor-based motion system allows for navigation in non-prepared and populated scenarios. The prototype has been validated with five healthy subjects in three experimental sessions: screening (an analysis of three different interfaces and its implications on the performance of the users), virtual environment driving (training and instruction of the users) and driving sessions with the wheelchair (driving tests along pre-established circuits). On the basis of the results, this paper reports a technical evaluation of the device and a variability study. All the users were able to successfully use the device with relative ease showing a great adaptation.

In recent years, anytime algorithms have shown to be a good solution for planning a path in domains with severe restrictions regarding the time for deliberation. They typically operate by quickly finding a highly suboptimal path first, and then improving it until the available time runs out. In this paper, we propose a novel anytime approach called ABUG that performs much more efficiently than the competing strategies. ABUG is based on an improved version of a member of the popular family of algorithms known as Bug. A formal analysis of the planner is provided and several relevant properties of ABUG are identified. Besides, as done in some heuristic-based anytime approaches, we define bounds on the quality/length of the paths returned by the algorithm. Finally, in order to demonstrate the computational savings associated with the proposal, a comparative study involving a set of well-known path-planning techniques is also carried out.

This paper describes a new noninvasive brain-actuated wheelchair that relies on a P300 neurophysiological protocol and automated navigation. When in operation, the user faces a screen displaying a real-time virtual reconstruction of the scenario and concentrates on the location of the space to reach. A visual stimulation process elicits the neurological phenomenon, and the electroencephalogram (EEG) signal processing detects the target location. This location is transferred to the autonomous navigation system that drives the wheelchair to the desired location while avoiding collisions with obstacles in the environment detected by the laser scanner. This concept gives the user the flexibility to use the device in unknown and evolving scenarios. The prototype was validated with five healthy participants in three consecutive steps: screening (an analysis of three different groups of visual interface designs), virtual-environment driving, and driving sessions with the wheelchair. On the basis of the results, this paper reports the following evaluation studies: 1) a technical evaluation of the device and all functionalities; 2) a users' behavior study; and 3) a variability study. The overall result was that all the participants were able to successfully operate the device with relative ease, thus showing a great adaptation as well as a high robustness and low variability of the system.

Most collision avoidance methods do not consider the vehicle shape and its kinematic and dynamic constraints, assuming the robot to be point-like and omnidirectional with no acceleration constraints. The contribution of this paper is a methodology to consider the exact shape and kinematics, as well as the effects of dynamics in the collision avoidance layer, since the original avoidance method does not address them. This is achievable by abstracting the constraints from the avoidance methods in such a way that when the method is applied, the constraints already have been considered. This study is a starting point to extend the domain of applicability to a wide range of collision avoidance methods.

This paper presents a technique to improve the data association in the iterative closest point based scan matching. The method is based on a distance-filter constructed on the basis of an analysis of the set of solutions produced by the associations in the sensor configuration space. This leads to a robust strategy to filter all the associations that do not explain the principal motion of the scan (due to noise in the sensor, large odometry errors, spurious, occlusions or dynamic features for example). The experimental results suggest that the improvement of the data association leads to more robust and faster methods in the presence of wrong correspondences.

In this paper, we describe the adaptation of an autonomous robotic wheelchair for cognitive disabled children. The constraints imposed by these users require developing specific human-machine interfaces adapted to their limitations. In most cases it is necessary to develop additional tools to teach the children the spatial relations between the wheelchair, its motion and the environment. In addition to this, it is important to interact closely with the children and their educators. The paper describes the whole process followed to make the children use the autonomous wheelchair and the lessons learnt during the validation phase with the wheelchair and the children

This paper addresses the scan matching problem for mobile robot displacement estimation. The contribution is a new metric distance and all the tools necessary to be used within the iterative closest point framework. The metric distance is defined in the configuration space of the sensor, and takes into account both translation and rotation error of the sensor. The new scan matching technique ameliorates previous methods in terms of robustness, precision, convergence, and computational load. Furthermore, it has been extensively tested to validate and compare this technique with existing methods

This paper presents a metric-based matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The contribution is a geometric distance that takes into account the translation and orientation of the sensor at the same time. This result is used in the two steps of the matching - estimation process. The correspondences between scans are established with this measure and the minimization of the error is also carried out in terms of this distance. As a result, the translation and rotation are compensated in this framework simultaneously. In fact, this is the contribution with respect to previous work that addressed only translation or translation and rotation but separately. The new technique has been implemented and tested on a real vehicle. The experiments illustrate how it is more robust and accurate than prior techniques. At the end of the paper, we give an extension of our distance measure to 3D range-data matching problems.

This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information within a real sensor-based navigation system. The contribution in the modeling aspect is a formulation of the Detection and Tracking of Mobile Objects and the Simultaneous Localization and Map Building in such a way that the nature (static/dynamic) of the observations is included in the estimation process. This is achieved by a set of filters tracking the moving objects and a map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based navigation system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.

This paper addresses the obstacle avoidance problem in difficult scenarios that usually are dense, complex and cluttered. The proposal is a method called the obstacle-restriction. At each iteration of the control cycle, this method addresses the obstacle avoidance in two steps. First there is procedure to compute instantaneous subgoals in the obstacle structure (obtained by the sensors). The second step associates a motion restriction to each obstacle, which are managed next to compute the most promising motion direction. The advantage of this technique is that it avoids common limitations of previous obstacle avoidance methods, improving their navigation performance in difficult scenarios. Furthermore, we obtain similar results to the recent methods that achieve navigation in troublesome scenarios. However, the new method improves their behavior in open spaces. The performance of this method is illustrated with experimental results obtained with a robotic wheelchair vehicle.

This paper presents a sensor-based navigation system to safely drive vehicles in realistic scenarios. Three modules with the following functionalities compose the system: model builder, tactical planning and obstacle avoidance. These modules are integrated within a planner-reactor architecture that supervises and coordinates them in order to carry out the motion task. The emphasis of the paper is in the planning aspect and in its integration with the obstacle avoidance and modeling module. The advantage of this navigation system is to achieve a robust and trustworthy navigation in difficult scenarios, which remain troublesome for many of the existing systems. In order to validate the system, we present experiments with a wheelchair vehicle transporting a human among locations in an office type scenario.

This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence.

This paper addresses reactive collision avoidance for robots that move in arduous environments (i.e., very dense, complex and cluttered). To achieve this goal, the technique simplifies the difficulty of the navigation by a divide and conquer strategy, which is based on identifying navigational situations and applying the corresponding motion laws. The state of the art in reactive navigation still presents classic limitations such as trap situations due to U-shape obstacles, difficulty to achieve motion among very close obstacles, to obtain oscillation-free and stable motion, to move over directions far from the goal direction or towards the obstacles, or to tune the heuristic or internal parameters. This paper presents a method that overcomes all these limitations. As a result, navigation with this method is successfully achieved in scenarios where existing techniques present a high degree of difficulty to navigate. Outstanding navigation results are reported using a wheelchair vehicle. A discussion and comparison with existing techniques is provided.

We address here a sensor-based navigation system to safely drive vehicles in realistic scenarios. The system is composed of three modules with the following functionalities: model builder, planning and reactive motion. These modules are integrated within a planner-reactor architecture that supervises and coordinates them in order to carry out the motion task. The advantage of our system is to achieve a robust and trustworthy navigation in difficult scenarios, which remain troublesome for many of the existing systems. In order to validate the system, we present experiments with a wheelchair vehicle transporting a human among locations in an office type scenario.

This paper addresses the reactive collision avoidance for vehicles that move in very dense, cluttered, and complex scenarios. First, we describe the design of a reactive navigation method that uses a "divide and conquer" strategy based on situations to simplify the difficulty of the navigation. Many techniques could be used to implement this design (since it is described at symbolic level), leading to new reactive methods that must be able to navigate in arduous environments (as the difficulty of the navigation is simplified). We also propose a geometry-based implementation of our design called the nearness diagram navigation. The advantage of this reactive method is to successfully move robots in troublesome scenarios, where other methods present a high degree of difficulty in navigating. We show experimental results on a real vehicle to validate this research, and a discussion about the advantages and limitations of this new approach.

This paper presents a framework to use collision avoidance methods in the majority of existing mobile robots (that have any shape, kinematic and dynamic constraints). The solution proposed is a vehicle abstraction layer based on transforming the space where these methods work, onto another one in which the constraints are implicitly represented. This space incorporates the vehicle kinematics and dynamics in such a way that when the reactive navigation methods are used, the motions computed comply with the motion constraints. We validate the utility of this framework by applying a classic reactive method in a real vehicle with motion constraints (whereas the original method does not address the kinematics and dynamics).

We address the problem of applying reactive navigation methods to non-holonomic robots. Rather than embedding the motion constraints when designing a navigation method, we propose to introduce the robot's kinematic constraints directly in the spatial representation. In this space - the ego-kinematic space - the robot moves as a "free-flying object". Hence, standard reactive navigation methods applied to this space will automatically take into account the robot's kinematic constraints, without additional modifications. This methodology can be used with a large class of constrained mobile platforms (e.g. differential-driven robots, car-like robots, tri-cycle robots). We show experiments involving non-holonomic robots with two reactive navigation methods whose original formulation does not take the robot kinematic constraints into account (the Nearness Diagram Navigation and a Potential Field method).

We address the problem of applying reactive navigation methods for collision avoidance to systems where the dynamics cannot be neglected: mobile robots with slow dynamic capabilities, or systems working at high speeds. Rather than embedding the motion constraints when designing a navigation method, we propose to introduce the robot dynamic constraints directly into the spatial representation. In this space the dynamic capabilities of the robot are implicitly represented. With minor modifications, standard reactive navigation methods can be used in this space implicitly taking into account the robot dynamic constraints. To validate this framework, we show experimental results using two reactive navigation methods whose original formulation do not take the robot dynamic constraints into account (the nearness diagram navigation and the potential field method).

Presents the global nearness diagram navigation system for mobile robots. The GND generates motion commands to drive a robot safely between locations, whilst avoiding collisions. This system has all the advantages of using the reactive scheme nearness diagram (ND), while having the ability to reason and plan globally (reaching global convergence to the navigation problem). This framework has been extensively tested using a holonomic mobile base equipped with a laser range-finder. Experiments in unknown, unstructured, dynamic and complex environments are reported to validate the system.

Presents our project to develop a personal robot called Diligent. We first discuss the main issues involved in personal robotics. Then we analyze some of the key features implied by what we call "human-friendly navigation". Finally, we present the current state of Diligent. Diligent is already able to navigate safely and repeatedly in a relatively large environment. It is also capable of some preliminary human-robot interactions through very simple and intuitive commands.

This paper presents a new real-time collision avoidance approach for mobile robots. The nearness diagram method (ND) performs a high level information extraction and interpretation of the environment. Subsequently, this information is used to generate the motion commands. The proposed approach is well-suited to deal with unknown, unstructured and dynamic environments, where problems of other approaches are avoided. Some experimental results are shown using an holonomic mobile base to demonstrate the usefulness of the method.