2,922 research outputs found
A Framework for Interactive Teaching of Virtual Borders to Mobile Robots
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.Comment: 7 pages, 6 figure
3D environment mapping using the Kinect V2 and path planning based on RRT algorithms
This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees variant (RRT*), for a safe and optimal navigation of robots in 3D spaces. Results on RGB-D segmentation and recognition, point cloud processing, and comparisons between different RRT* algorithms, are presented.Peer ReviewedPostprint (published version
Directed Exploration using a Modified Distance Transform
Mobile robots operating in unknown environments need to build maps. To do so they must have an exploration algorithm to plan a path. This algorithm should guarantee that the whole of the environment, or at least some designated area, will be mapped. The path should also be optimal in some sense and not simply a "random walk" which is clearly inefficient. When multiple robots are involved, the algorithm also needs to take advantage of the fact that the robots can share the task. In this paper we discuss a modification to the well-known distance transform that satisfies these requirements
Distributed 3D TSDF Manifold Mapping for Multi-Robot Systems
International audienceThis paper presents a new method to perform collaborative real-time dense 3D mapping in a distributed way for a multi-robot system. This method associates a Truncated Signed Distance Function (TSDF) representation with a manifold structure. Each robot owns a private map which is composed of a collection of local TSDF sub-maps called patches that are locally consistent. This private map can be shared to build a public map collecting all the patches created by the robots of the fleet. In order to maintain consistency in the global map, a mechanism of patch alignment and fusion has been added. This work has been integrated in real-time into a mapping stack, which can be used for autonomous navigation in unknown and cluttered environment. Experimental results on a team of wheeled mobile robots are reported to demonstrate the practical interest of the proposed system, in particular for the exploration of unknown areas
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different
tasks in an autonomous agent, such as a robot or a virtual entity in a computer
game. BTs are a very efficient way of creating complex systems that are both
modular and reactive. These properties are crucial in many applications, which
has led to the spread of BT from computer game programming to many branches of
AI and Robotics. In this book, we will first give an introduction to BTs, then
we describe how BTs relate to, and in many cases generalize, earlier switching
structures. These ideas are then used as a foundation for a set of efficient
and easy to use design principles. Properties such as safety, robustness, and
efficiency are important for an autonomous system, and we describe a set of
tools for formally analyzing these using a state space description of BTs. With
the new analysis tools, we can formalize the descriptions of how BTs generalize
earlier approaches. We also show the use of BTs in automated planning and
machine learning. Finally, we describe an extended set of tools to capture the
behavior of Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success probabilities
and time to completion
Autonomous task-based grasping for mobile manipulators
A fully integrated grasping system for a mobile manipulator to grasp an unknown object of interest (OI) in an unknown environment is presented. The system autonomously scans its environment, models the OI, plans and executes a grasp, while taking into account base pose uncertainty and obstacles in its way to reach the object. Due to inherent line of sight limitations in sensing, a single scan of the OI often does not reveal enough information to complete grasp analysis; as a result, our system autonomously builds a model of an object via multiple scans from different locations until a grasp can be performed. A volumetric next-best-view (NBV) algorithm is used to model an arbitrary object and terminates modelling when grasp poses are discovered on a partially observed object. Two key sets of experiments are presented: i) modelling and registration error in the OI point cloud model is reduced by selecting viewpoints with more scan overlap, and ii) model construction and grasps are successfully achieved while experiencing base pose uncertainty. A generalized algorithm is presented to discover grasp pose solutions for multiple grasp types for a multi-fingered mechanical gripper using sensed point clouds. The algorithm introduces two key ideas: 1) a histogram of finger contact normals is used to represent a grasp “shape” to guide a gripper orientation search in a histogram of object(s) surface normals, and 2) voxel grid representations of gripper and object(s) are cross-correlated to match finger contact points, i.e. grasp “size”, to discover a grasp pose. Constraints, such as collisions with neighbouring objects, are incorporated in the cross-correlation computation. Simulations and preliminary experiments show that 1) grasp poses for three grasp types are found in near real-time, 2) grasp pose solutions are consistent with respect to voxel resolution changes for both partial and complete point cloud scans, 3) a planned grasp pose is executed with a mechanical gripper, and 4) grasp overlap is presented as a feature to identify regions on a partial object model ideal for object transfer or securing an object
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