5,173 research outputs found
Open World Assistive Grasping Using Laser Selection
Many people with motor disabilities are unable to complete activities of
daily living (ADLs) without assistance. This paper describes a complete robotic
system developed to provide mobile grasping assistance for ADLs. The system is
comprised of a robot arm from a Rethink Robotics Baxter robot mounted to an
assistive mobility device, a control system for that arm, and a user interface
with a variety of access methods for selecting desired objects. The system uses
grasp detection to allow previously unseen objects to be picked up by the
system. The grasp detection algorithms also allow for objects to be grasped in
cluttered environments. We evaluate our system in a number of experiments on a
large variety of objects. Overall, we achieve an object selection success rate
of 88% and a grasp detection success rate of 90% in a non-mobile scenario, and
success rates of 89% and 72% in a mobile scenario
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
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
Multimodal Planning under Uncertainty: Task-Motion Planning and Collision Avoidance
openIn this thesis we investigate the problem of motion planning under environment uncertainty.
Specifically, we focus on Task-Motion Planning (TMP) and probabilistic collision avoidance
which are presented as two parts in this thesis. Though the two parts are largely self-contained,
collision avoidance is an integral part of TMP or any robot motion planning problem in
general. The problem of TMP which is the subject of Part I is by itself challenging and hence
in Part I, collision computation is not the main focus and is addressed with a deterministic
approach. Moreover, motion planning is performed offline since we assume static obstacles
in the environment. Online TMP, incorporating dynamic obstacles or other environment
changes is rather difficult due to the computational challenges associated with updating the
changing task domain. As such, we devote Part II entirely to the field of online probabilistic
collision avoidance motion planning.
Of late, TMP for manipulation has attracted significant interest resulting in a proliferation
of different approaches. In contrast, TMP for navigation has received considerably less
attention. Autonomous robots operating in real-world complex scenarios require planning
in the discrete (task) space and the continuous (motion) space. In knowledge-intensive
domains, on the one hand, a robot has to reason at the highest-level, for example, the
objects to procure, the regions to navigate to in order to acquire them; on the other hand, the
feasibility of the respective navigation tasks have to be checked at the execution level. This
presents a need for motion-planning-aware task planners. In Part I of this thesis, we discuss a
probabilistically complete approach that leverages this task-motion interaction for navigating
in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The
framework is intended for motion planning under motion and sensing uncertainty, which is
formally known as Belief Space Planning (BSP). The underlying methodology is validated in
simulation, in an office environment and its scalability is tested in the larger Willow Garage
world. A reasonable comparison with a work that is closest to our approach is also provided.
We also demonstrate the adaptability of our method by considering a building floor navigation
domain. Finally, we also discuss the limitations of our approach and put forward suggestions
for improvements and future work.
In Part II of this thesis, we present a BSP framework that accounts for the landmark
uncertainties during robot localization. We further extend the state-of-the-art by computing
an exact expression for the collision probability under Gaussian motion and perception
uncertainties. Existing BSP approaches assume that the landmark locations are well known
or are known with little uncertainty. However, this might not be true in practice. Noisy
sensors and imperfect motions compound to the errors originating from the estimate of
environment features. Moreover, possible occlusions and dynamic objects in the environment
render imperfect landmark estimation. Consequently, not considering this uncertainty can
result in wrongly localizing the robot, leading to inefficient plans. Our approach incorporates
the landmark uncertainty within the Bayes filter framework. We also analyze the effect
of considering this uncertainty and delineate the conditions under which it can be ignored.
Furthermore, we also investigate the problem of safe motion planning under Gaussian motion
and sensing uncertainties. Existing approaches approximate the collision probability using
upper-bounds that can lead to overly conservative estimate and thereby suboptimal plans.
We formulate the collision probability process as a quadratic form in random variables.
Under Gaussian distribution assumptions, an exact expression for collision probability is
thus obtained which is computable in real-time. Further, we compute a tight upper bound
for fast online computation of collision probability and also derive a collision avoidance
constraint to be used in an optimization setting. We demonstrate and evaluate our approach
using a theoretical example and simulations in single and multi-robot settings using mobile
and aerial robots. A comparison of our approach to different state-of-the-art methods are also
provided.openXXXIII CICLO - BIOINGEGNERIA E ROBOTICA - BIOENGINEERING AND ROBOTICSThomas, Anton
Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation
Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning
- …