The increasing number of robots in home environments leads to an emerging coexistence between humans and robots. Robots undertake common tasks and support the residents in their everyday life. People appreciate the presence of robots in their environment as long as they keep the control over them. One important aspect is the control of a robot's workspace. Therefore, we introduce virtual borders to precisely and flexibly define the workspace of mobile robots. First, we propose a novel framework that allows a person to interactively restrict a mobile robot's workspace. To show the validity of this framework, a concrete implementation based on visual markers is implemented. Afterwards, the mobile robot is capable of performing its tasks while respecting the new virtual borders. The approach is accurate, flexible and less time consuming than explicit robot programming. Hence, even non-experts are able to teach virtual borders to their robots which is especially interesting in domains like vacuuming or service robots in home environments.
In contrast to these direct approaches to incorporate additional information into maps, several works focused on the implicit integration of further information, e.g. social information @cite_25 . O' @cite_27 learn human motion patterns and update the robot's navigational map according to humans' trajectories. In addition, @cite_2 use a map learned from robots' sensors and human trajectory observations to navigate to any goal in the environment. Human motion maps are proposed by @cite_14 to represent the motion distribution in particular areas. Furthermore, @cite_22 learn about human behaviors associated with areas in the environment. Another approach based on implicit observations is proposed by @cite_4 who learn the locations of doors and staircases by observing humans' interactions around them. Virtual border teaching, as we use this term throughout the paper, is a certain form of semantic mapping @cite_23 in that we give semantics, and space, to certain areas.
@cite_14 @cite_4 @cite_22 @cite_27 @cite_23 @cite_2 @cite_25
2803780405 2054245952 2283534560 2523049145
This paper considers motion planning for a six-legged walking robot in rough terrain, considering both the geometry of the terrain and its semantic labeling. The semantic labels allow the robot to distinguish between different types of surfaces it can walk on, and identify areas that cannot be negotiated due to their physical nature. The proposed environment map provides to the planner information about the shape of the terrain, and the terrain class labels. Such labels as “wall” and “plant” denote areas that have to be avoided, whereas other labels, “grass”, “sand”, “concrete”, etc. represent negotiable areas of different properties. We test popular classification algorithms: Support Vector Machine and Random Trees in the task of producing proper terrain labeling from RGB-D data acquired by the robot. The motion planner uses the A∗ algorithm to guide the RRT-Connect method, which yields detailed motion plans for the multi-d.o.f. legged robot. As the A∗ planner takes into account the terrain semantic labels, the robot avoids areas which are potentially risky and chooses paths crossing mostly the preferred terrain types. We report experimental results that show the ability of the new approach to avoid areas that are considered risky for legged locomotion. Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework. We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70 compared to a closest frontier exploration strategy and 57 compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state. Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level of robot intelligence and intuitive user interaction, maps need to extend beyond geometry and appearance — they need to contain semantics. We address this challenge by combining Convolutional Neural Networks (CNNs) and a state-of-the-art dense Simultaneous Localization and Mapping (SLAM) system, ElasticFusion, which provides long-term dense correspondences between frames of indoor RGB-D video even during loopy scanning trajectories. These correspondences allow the CNN's semantic predictions from multiple view points to be probabilistically fused into a map. This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions. We also show that for a smaller reconstruction dataset with larger variation in prediction viewpoint, the improvement over single frame segmentation increases. Our system is efficient enough to allow real-time interactive use at frame-rates of ≈25Hz.