1,254 research outputs found
Velocity constrained trajectory generation for a collinear Mecanum wheeled robot
While much research has been conducted into the generation of smooth trajectories for underactuated unstable aerial vehicles such as quadrotors, less attention has been paid to the application of the same techniques to ground based omnidirectional dynamically balancing robots. These systems have more control authority over their linear accelerations than aerial vehicles, meaning trajectory smoothness is less of a critical design parameter. However, when operating in indoor environments these systems must often adhere to relatively low velocity constraints, resulting in very conservative trajectories when enforced using existing trajectory optimisation methods. This paper makes two contributions; this gap is bridged by the extension of these existing methods to create a fast velocity constrained trajectory planner, with trajectory timing characteristics derived from the optimal minimum-time solution of a simplified acceleration and velocity constrained model. Next, a differentially flat model of an omnidirectional balancing robot utilizing a collinear Mecanum drive is derived, which is used to allow an experimental prototype of this configuration to smoothly follow these velocity constrained trajectories
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes
This paper presents two variations of a novel stochastic prediction algorithm
that enables mobile robots to accurately and robustly predict the future state
of complex dynamic scenes. The proposed algorithm uses a variational
autoencoder to predict a range of possible future states of the environment.
The algorithm takes full advantage of the motion of the robot itself, the
motion of dynamic objects, and the geometry of static objects in the scene to
improve prediction accuracy. Three simulated and real-world datasets collected
by different robot models are used to demonstrate that the proposed algorithm
is able to achieve more accurate and robust prediction performance than other
prediction algorithms. Furthermore, a predictive uncertainty-aware planner is
proposed to demonstrate the effectiveness of the proposed predictor in
simulation and real-world navigation experiments. Implementations are open
source at https://github.com/TempleRAIL/SOGMP.Comment: Accepted by 7th Annual Conference on Robot Learning (CoRL), 202
Planning Framework for Robotic Pizza Dough Stretching with a Rolling Pin
Stretching a pizza dough with a rolling pin is a nonprehensile manipulation. Since the object is deformable, force closure cannot be established, and the manipulation is carried out in a nonprehensile way. The framework of this pizza dough stretching application that is explained in this chapter consists of four sub-procedures: (i) recognition of the pizza dough on a plate, (ii) planning the necessary steps to shape the pizza dough to the desired form, (iii) path generation for a rolling pin to execute the output of the pizza dough planner, and (iv) inverse kinematics for the bi-manual robot to grasp and control the rolling pin properly. Using the deformable object model described in Chap. 3, each sub-procedure of the proposed framework is explained sequentially
RIACS
The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on June 6, 1983. RIACS is privately operated by USRA, a consortium of universities that serves as a bridge between NASA and the academic community. Under a five-year co-operative agreement with NASA, research at RIACS is focused on areas that are strategically enabling to the Ames Research Center's role as NASA's Center of Excellence for Information Technology. The primary mission of RIACS is charted to carry out research and development in computer science. This work is devoted in the main to tasks that are strategically enabling with respect to NASA's bold mission in space exploration and aeronautics. There are three foci for this work: (1) Automated Reasoning. (2) Human-Centered Computing. and (3) High Performance Computing and Networking. RIACS has the additional goal of broadening the base of researcher in these areas of importance to the nation's space and aeronautics enterprises. Through its visiting scientist program, RIACS facilitates the participation of university-based researchers, including both faculty and students, in the research activities of NASA and RIACS. RIACS researchers work in close collaboration with NASA computer scientists on projects such as the Remote Agent Experiment on Deep Space One mission, and Super-Resolution Surface Modeling
深層強化学習を用いた動的環境下における事前知識不要なロボットナビゲーションに関する研究
Tohoku University博士(工学)thesi
A 3D dynamic model of a spherical wheeled self-balancing robot
Mobility through balancing on spherical wheels has recently received some attention in the robotics literature. Unlike traditional wheeled platforms, the operation of such platforms depends heavily on understanding and working with system dynamics, which have so far been approximated with simple planar models and their decoupled extension to three dimensions. Unfortunately, such models cannot capture inherently spatial aspects of motion such as yaw motion arising from the wheel rolling motion or coupled inertial effects for fast maneuvers. In this paper, we describe a novel, fully-coupled 3D model for such spherical wheeled platforms and show that it not only captures relevant spatial aspects of motion, but also provides a basis for controllers better informed by system dynamics. We focus our evaluations to simulations with this model and use circular paths to reveal advantages of this model in dynamically rich situations. © 2012 IEEE
Homeostatic action selection for simultaneous multi-tasking
Mobile robots are rapidly developing and gaining in competence, but the potential
of available hardware still far outstrips our ability to harness. Domain-specific
applications are most successful due to customised programming tailored to a
narrow area of application. Resulting systems lack extensibility and autonomy,
leading to increased cost of development.
This thesis investigates the possibility of designing and implementing a general
framework capable of simultaneously coordinating multiple tasks that can be added
or removed in a plug and play manner. A homeostatic mechanism is proposed for
resolving the contentions inevitably arising between tasks competing for the use of
the same robot actuators.
In order to evaluate the developed system, demonstrator tasks are constructed to
reach a goal location, prevent collision, follow a contour around obstacles and
balance a ball within a spherical bowl atop the robot.
Experiments show preliminary success with the homeostatic coordination mechanism
but a restriction to local search causes issues that preclude conclusive evaluation.
Future work identifies avenues for further research and suggests switching to a
planner with the sufficient foresight to continue evaluation."This work was supported by the Engineering and Physical Sciences Research Council
[grant number EP/K503162/1]." -- Acknowledgement
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