2,138 research outputs found
A USB3.0 FPGA Event-based Filtering and Tracking Framework for Dynamic Vision Sensors
Dynamic vision sensors (DVS) are frame-free sensors
with an asynchronous variable-rate output that is ideal for hard
real-time dynamic vision applications under power and latency
constraints. Post-processing of the digital sensor output can
reduce sensor noise, extract low level features, and track objects
using simple algorithms that have previously been implemented
in software. In this paper we present an FPGA-based framework
for event-based processing that allows uncorrelated-event noise
removal and real-time tracking of multiple objects, with dynamic
capabilities to adapt itself to fast or slow and large or small
objects. This framework uses a new hardware platform based on
a Lattice FPGA which filters the sensor output and which then
transmits the results through a super-speed Cypress FX3 USB
microcontroller interface to a host computer. The packets of
events and timestamps are transmitted to the host computer at
rates of 10 Mega events per second. Experimental results are
presented that demonstrate a low latency of 10us for tracking
and computing the center of mass of a detected object.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
Navigation, Path Planning, and Task Allocation Framework For Mobile Co-Robotic Service Applications in Indoor Building Environments
Recent advances in computing and robotics offer significant potential for improved autonomy in the operation and utilization of today’s buildings. Examples of such building environment functions that could be improved through automation include: a) building performance monitoring for real-time system control and long-term asset management; and b) assisted indoor navigation for improved accessibility and wayfinding. To enable such autonomy, algorithms related to task allocation, path planning, and navigation are required as fundamental technical capabilities. Existing algorithms in these domains have primarily been developed for outdoor environments. However, key technical challenges that prevent the adoption of such algorithms to indoor environments include: a) the inability of the widely adopted outdoor positioning method (Global Positioning System - GPS) to work indoors; and b) the incompleteness of graph networks formed based on indoor environments due to physical access constraints not encountered outdoors.
The objective of this dissertation is to develop general and scalable task allocation, path planning, and navigation algorithms for indoor mobile co-robots that are immune to the aforementioned challenges. The primary contributions of this research are: a) route planning and task allocation algorithms for centrally-located mobile co-robots charged with spatiotemporal tasks in arbitrary built environments; b) path planning algorithms that take preferential and pragmatic constraints (e.g., wheelchair ramps) into consideration to determine optimal accessible paths in building environments; and c) navigation and drift correction algorithms for autonomous mobile robotic data collection in buildings.
The developed methods and the resulting computational framework have been validated through several simulated experiments and physical deployments in real building environments. Specifically, a scenario analysis is conducted to compare the performance of existing outdoor methods with the developed approach for indoor multi-robotic task allocation and route planning. A simulated case study is performed along with a pilot experiment in an indoor built environment to test the efficiency of the path planning algorithm and the performance of the assisted navigation interface developed considering people with physical disabilities (i.e., wheelchair users) as building occupants and visitors. Furthermore, a case study is performed to demonstrate the informed retrofit decision-making process with the help of data collected by an intelligent multi-sensor fused robot that is subsequently used in an EnergyPlus simulation. The results demonstrate the feasibility of the proposed methods in a range of applications involving constraints on both the environment (e.g., path obstructions) and robot capabilities (e.g., maximum travel distance on a single charge). By focusing on the technical capabilities required for safe and efficient indoor robot operation, this dissertation contributes to the fundamental science that will make mobile co-robots ubiquitous in building environments in the near future.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143969/1/baddu_1.pd
Range-only SLAM schemes exploiting robot-sensor network cooperation
Simultaneous localization and mapping (SLAM) is a key problem in robotics. A robot with no previous knowledge of the environment builds a map of this environment and localizes itself in that map. Range-only SLAM is a particularization of the SLAM problem which only uses the information provided by range sensors. This PhD Thesis describes the design, integration, evaluation and validation of a set of schemes for accurate and e_cient range-only simultaneous localization and mapping exploiting the cooperation between robots and sensor networks. This PhD Thesis proposes a general architecture for range-only simultaneous localization and mapping (RO-SLAM) with cooperation between robots and sensor networks. The adopted architecture has two main characteristics. First, it exploits the sensing, computational and communication capabilities of sensor network nodes. Both, the robot and the beacons actively participate in the execution of the RO-SLAM _lter. Second, it integrates not only robot-beacon measurements but also range measurements between two di_erent beacons, the so-called inter-beacon measurements. Most reported RO-SLAM methods are executed in a centralized manner in the robot. In these methods all tasks in RO-SLAM are executed in the robot, including measurement gathering, integration of measurements in RO-SLAM and the Prediction stage. These fully centralized RO-SLAM methods require high computational burden in the robot and have very poor scalability. This PhD Thesis proposes three di_erent schemes that works under the aforementioned architecture. These schemes exploit the advantages of cooperation between robots and sensor networks and intend to minimize the drawbacks of this cooperation. The _rst scheme proposed in this PhD Thesis is a RO-SLAM scheme with dynamically con_gurable measurement gathering. Integrating inter-beacon measurements in RO-SLAM signi_cantly improves map estimation but involves high consumption of resources, such as the energy required to gather and transmit measurements, the bandwidth required by the measurement collection protocol and the computational burden necessary to integrate the larger number of measurements. The objective of this scheme is to reduce the increment in resource consumption resulting from the integration of inter-beacon measurements by adopting a centralized mechanism running in the robot that adapts measurement gathering. The second scheme of this PhD Thesis consists in a distributed RO-SLAM scheme based on the Sparse Extended Information Filter (SEIF). This scheme reduces the increment in resource consumption resulting from the integration of inter-beacon measurements by adopting a distributed SLAM _lter in which each beacon is responsible for gathering its measurements to the robot and to other beacons and computing the SLAM Update stage in order to integrate its measurements in SLAM. Moreover, it inherits the scalability of the SEIF. The third scheme of this PhD Thesis is a resource-constrained RO-SLAM scheme based on the distributed SEIF previously presented. This scheme includes the two mechanisms developed in the previous contributions {measurement gathering control and distribution of RO-SLAM Update stage between beacons{ in order to reduce the increment in resource consumption resulting from the integration of inter-beacon measurements. This scheme exploits robot-beacon cooperation to improve SLAM accuracy and e_ciency while meeting a given resource consumption bound. The resource consumption bound is expressed in terms of the maximum number of measurements that can be integrated in SLAM per iteration. The sensing channel capacity used, the beacon energy consumed or the computational capacity employed, among others, are proportional to the number of measurements that are gathered and integrated in SLAM. The performance of the proposed schemes have been analyzed and compared with each other and with existing works. The proposed schemes are validated in real experiments with aerial robots. This PhD Thesis proves that the cooperation between robots and sensor networks provides many advantages to solve the RO-SLAM problem. Resource consumption is an important constraint in sensor networks. The proposed architecture allows the exploitation of the cooperation advantages. On the other hand, the proposed schemes give solutions to the resource limitation without degrading performance
NFV Based Gateways for Virtualized Wireless Sensors Networks: A Case Study
Virtualization enables the sharing of a same wireless sensor network (WSN) by
multiple applications. However, in heterogeneous environments, virtualized
wireless sensor networks (VWSN) raises new challenges such as the need for
on-the-fly, dynamic, elastic and scalable provisioning of gateways. Network
Functions Virtualization (NFV) is an emerging paradigm that can certainly aid
in tackling these new challenges. It leverages standard virtualization
technology to consolidate special-purpose network elements on top of commodity
hardware. This article presents a case study on NFV based gateways for VWSNs.
In the study, a VWSN gateway provider, operates and manages an NFV based
infrastructure. We use two different brands of wireless sensors. The NFV
infrastructure makes possible the dynamic, elastic and scalable deployment of
gateway modules in this heterogeneous VWSN environment. The prototype built
with Openstack as platform is described
ED-Scorbot: A Robotic test-bed Framework for FPGA-based Neuromorphic systems
Neuromorphic engineering is a growing and
promising discipline nowadays. Neuro-inspiration and
brain understanding applied to solve engineering
problems is boosting new architectures, solutions and
products today. The biological brain and neural systems
process information at relatively low speeds through
small components, called neurons, and it is impressive how
they connect each other to construct complex
architectures to solve in a quasi-instantaneous way
visual and audio processing tasks, object detection and
tracking, target approximation, grasping…, etc., with very
low power. Neuromorphs are beginning to be very promising
for a new era in the development of new sensors,
processors, robots and software systems that mimic
these biological systems. The event-driven Scorbot (EDScorbot)
is a robotic arm plus a set of FPGA / microcontroller’s
boards and a library of FPGA logic joined in a completely
event-based framework (spike-based) from the sensors to the
actuators. It is located in Seville (University of Seville) and
can be used remotely. Spike-based commands, through
neuro-inspired motor controllers, can be sent to the
robot after visual processing object detection and
tracking for grasping or manipulation, after complex
visual and audio-visual sensory fusion, or after performing
a learning task. Thanks to the cascade FPGA
architecture through the Address-Event-Representation
(AER) bus, supported by specialized boards, resources for
algorithms implementation are not limited.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
MIRRAX: A Reconfigurable Robot for Limited Access Environments
The development of mobile robot platforms for inspection has gained traction
in recent years with the rapid advancement in hardware and software. However,
conventional mobile robots are unable to address the challenge of operating in
extreme environments where the robot is required to traverse narrow gaps in
highly cluttered areas with restricted access. This paper presents MIRRAX, a
robot that has been designed to meet these challenges with the capability of
re-configuring itself to both access restricted environments through narrow
ports and navigate through tightly spaced obstacles. Controllers for the robot
are detailed, along with an analysis on the controllability of the robot given
the use of Mecanum wheels in a variable configuration. Characterisation on the
robot's performance identified suitable configurations for operating in narrow
environments. The minimum lateral footprint width achievable for stable
configuration (~roll) was 0.19~m. Experimental validation of the
robot's controllability shows good agreement with the theoretical analysis. A
further series of experiments shows the feasibility of the robot in addressing
the challenges above: the capability to reconfigure itself for restricted entry
through ports as small as 150mm diameter, and navigating through cluttered
environments. The paper also presents results from a deployment in a Magnox
facility at the Sellafield nuclear site in the UK -- the first robot to ever do
so, for remote inspection and mapping.Comment: 10 pages, Under review for IEEE Transactions on Robotic
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