3D Path planning using a fuzzy logic navigational map for Planetary Surface Rovers

This work proposes an innovative app navigation path-planning problem exploration rovers by including terrain characteristics. The objective is to enhance the typical 2D arithmetical cost function by adding 3D information computed from the laser-scanned terrain such as terrain height, slopes, shadows, orientation and terrain roughness. This paper describes the algorithm developed by UPM and GMV and the tests made at the GMV outdoor test facilities using the Moon-Hound rover. This rover is a 50 Kg rover including a Sick laser mounted on a pan&tilt unit for generation of 3D Digital Elevation Models (DEM’s). Experimental results have shown the effectiveness of the proposed approach

Study of the applicability of cognitive path algorithms for 3D indoor navigation

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Evaluating distributed cognitive resources for wayfinding in a desktop virtual environment.

As 3D interfaces, and in particular virtual environments, become increasingly realistic there is a need to investigate the location and configuration of information resources, as distributed in the humancomputer system, to support any required activities. It is important for the designer of 3D interfaces to be aware of information resource availability and distribution when considering issues such as cognitive load on the user. This paper explores how a model of distributed resources can support the design of alternative aids to virtual environment wayfinding with varying levels of cognitive load. The wayfinding aids have been implemented and evaluated in a desktop virtual environment

Learning to Prevent Monocular SLAM Failure using Reinforcement Learning

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.Comment: Accepted at the 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) 2018 More info can be found at the project page at https://robotics.iiit.ac.in/people/vignesh.prasad/SLAMSafePlanner.html and the supplementary video can be found at https://www.youtube.com/watch?v=420QmM_Z8v

Virtual Borders: Accurate Definition of a Mobile Robot's Workspace Using Augmented Reality

We address the problem of interactively controlling the workspace of a mobile robot to ensure a human-aware navigation. This is especially of relevance for non-expert users living in human-robot shared spaces, e.g. home environments, since they want to keep the control of their mobile robots, such as vacuum cleaning or companion robots. Therefore, we introduce virtual borders that are respected by a robot while performing its tasks. For this purpose, we employ a RGB-D Google Tango tablet as human-robot interface in combination with an augmented reality application to flexibly define virtual borders. We evaluated our system with 15 non-expert users concerning accuracy, teaching time and correctness and compared the results with other baseline methods based on visual markers and a laser pointer. The experimental results show that our method features an equally high accuracy while reducing the teaching time significantly compared to the baseline methods. This holds for different border lengths, shapes and variations in the teaching process. Finally, we demonstrated the correctness of the approach, i.e. the mobile robot changes its navigational behavior according to the user-defined virtual borders.Comment: Accepted on 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), supplementary video: https://youtu.be/oQO8sQ0JBR

Changes in navigational behaviour produced by a wide field of view and a high fidelity visual scene

The difficulties people frequently have navigating in virtual environments (VEs) are well known. Usually these difficulties are quantified in terms of performance (e.g., time taken or number of errors made in following a path), with these data used to compare navigation in VEs to equivalent real-world settings. However, an important cause of any performance differences is changes in people’s navigational behaviour. This paper reports a study that investigated the effect of visual scene fidelity and field of view (FOV) on participants’ behaviour in a navigational search task, to help identify the thresholds of fidelity that are required for efficient VE navigation. With a wide FOV (144 degrees), participants spent significantly larger proportion of their time travelling through the VE, whereas participants who used a normal FOV (48 degrees) spent significantly longer standing in one place planning where to travel. Also, participants who used a wide FOV and a high fidelity scene came significantly closer to conducting the search "perfectly" (visiting each place once). In an earlier real-world study, participants completed 93% of their searches perfectly and planned where to travel while they moved. Thus, navigating a high fidelity VE with a wide FOV increased the similarity between VE and real-world navigational behaviour, which has important implications for both VE design and understanding human navigation. Detailed analysis of the errors that participants made during their non-perfect searches highlighted a dramatic difference between the two FOVs. With a narrow FOV participants often travelled right past a target without it appearing on the display, whereas with the wide FOV targets that were displayed towards the sides of participants overall FOV were often not searched, indicating a problem with the demands made by such a wide FOV display on human visual attention