2,131 research outputs found
A modular software architecture for UAVs
There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module
MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices
The Internet of Things (IoT) is part of Future Internet and will comprise
many billions of Internet Connected Objects (ICO) or `things' where things can
sense, communicate, compute and potentially actuate as well as have
intelligence, multi-modal interfaces, physical/ virtual identities and
attributes. Collecting data from these objects is an important task as it
allows software systems to understand the environment better. Many different
hardware devices may involve in the process of collecting and uploading sensor
data to the cloud where complex processing can occur. Further, we cannot expect
all these objects to be connected to the computers due to technical and
economical reasons. Therefore, we should be able to utilize resource
constrained devices to collect data from these ICOs. On the other hand, it is
critical to process the collected sensor data before sending them to the cloud
to make sure the sustainability of the infrastructure due to energy
constraints. This requires to move the sensor data processing tasks towards the
resource constrained computational devices (e.g. mobile phones). In this paper,
we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT
middleware for mobile devices, that allows to collect and process sensor data
without programming efforts. Our architecture also supports sensing as a
service model. We present the results of the evaluations that demonstrate its
suitability towards real world deployments. Our proposed middleware is built on
Android platform
PEP4Django - A Policy Enforcement Point for Python Web Applications
Traditionally, access control mechanisms have been hard-coded into
application components. Such approach is error-prone, mixing business logic with access control concerns, and affecting the flexibility of security policies, as is the case with IFRN SUAP Django-based system. The externalization of access control rules allows their decoupling from business logic, through the use of authorization servers where access control policies are stored and queried for computing access decisions. In this context, this paper presents an approach that allows a Django Web application to delegate access control decisions to an external authorization server. The approach has been integrated into an enterprise level system, which has been used for experimentation. The results obtained indicate a negligible overhead, while allowing the modification of access control policies without interrupting the system
Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs
Energy efficiency is becoming a pressing issue, especially in large data centers where it entails, at the same time, a non-negligible management cost, an enhancement of hardware fault probability, and a significant environmental footprint. In this paper, we study how Software Transactional Memories (STM) can provide benefits on both power saving and the overall applicationsâ execution performance. This is related to the fact that encapsulating shared-data accesses within transactions gives the freedom to the STM middleware to both ensure consistency and reduce the actual data contention, the latter having been shown to affect the overall power needed to complete the applicationâs execution.
We have selected a set of self-adaptive extensions to existing STM middlewares (namely, TinySTM and R-STM) to prove how self-adapting computation can capture the actual degree of parallelism and/or logical contention on shared data in a better way, enhancing even more the intrinsic benefits provided by STM. Of course, this benefit comes at a cost, which is the actual execution time required by the proposed approaches to precisely tune the execution parameters for reducing power consumption and enhancing execution performance. Nevertheless, the results hereby provided show that adaptivity is a strictly necessary requirement to reduce energy consumption in STM systems: Without it, it is not possible to reach any acceptable level of energy efficiency at all
A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots
Re-configurable modular robotic (RMR) systems are advantageous for their
reconfigurability and versatility. A new modular robot can be built for a
specific task by using modules as building blocks. However, constructing a
kinematic model for a newly conceived robot requires significant work. Due to
the finite size of module-types, models of all module-types can be built
individually and stored in a database beforehand. With this priori knowledge,
the model construction process can be automated by detecting the modules and
their corresponding interconnections. Previous literature proposed theoretical
frameworks for constructing kinematic models of modular robots, assuming that
such information was known a priori. While well-devised mechanisms and built-in
sensors can be employed to detect these parameters automatically, they
significantly complicate the module design and thus are expensive. In this
paper, we propose a vision-based method to identify kinematic chains and
automatically construct robot models for modular robots. Each module is affixed
with augmented reality (AR) tags that are encoded with unique IDs. An image of
a modular robot is taken and the detected modules are recognized by querying a
database that maintains all module information. The poses of detected modules
are used to compute: (i) the connection between modules and (ii) joint angles
of joint-modules. Finally, the robot serial-link chain is identified and the
kinematic model constructed and visualized. Our experimental results validate
the effectiveness of our approach. While implementation with only our RMR is
shown, our method can be applied to other RMRs where self-identification is not
possible
MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning
This paper describes a framework called MaestROB. It is designed to make the
robots perform complex tasks with high precision by simple high-level
instructions given by natural language or demonstration. To realize this, it
handles a hierarchical structure by using the knowledge stored in the forms of
ontology and rules for bridging among different levels of instructions.
Accordingly, the framework has multiple layers of processing components;
perception and actuation control at the low level, symbolic planner and Watson
APIs for cognitive capabilities and semantic understanding, and orchestration
of these components by a new open source robot middleware called Project Intu
at its core. We show how this framework can be used in a complex scenario where
multiple actors (human, a communication robot, and an industrial robot)
collaborate to perform a common industrial task. Human teaches an assembly task
to Pepper (a humanoid robot from SoftBank Robotics) using natural language
conversation and demonstration. Our framework helps Pepper perceive the human
demonstration and generate a sequence of actions for UR5 (collaborative robot
arm from Universal Robots), which ultimately performs the assembly (e.g.
insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018.
Video: https://www.youtube.com/watch?v=19JsdZi0TW
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