366 research outputs found
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
Jointly Optimizing Placement and Inference for Beacon-based Localization
The ability of robots to estimate their location is crucial for a wide
variety of autonomous operations. In settings where GPS is unavailable,
measurements of transmissions from fixed beacons provide an effective means of
estimating a robot's location as it navigates. The accuracy of such a
beacon-based localization system depends both on how beacons are distributed in
the environment, and how the robot's location is inferred based on noisy and
potentially ambiguous measurements. We propose an approach for making these
design decisions automatically and without expert supervision, by explicitly
searching for the placement and inference strategies that, together, are
optimal for a given environment. Since this search is computationally
expensive, our approach encodes beacon placement as a differential neural layer
that interfaces with a neural network for inference. This formulation allows us
to employ standard techniques for training neural networks to carry out the
joint optimization. We evaluate this approach on a variety of environments and
settings, and find that it is able to discover designs that enable high
localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and
Systems (IROS
Sensors, SLAM and Long-term Autonomy: A Review
Simultaneous Localization and Mapping, commonly known as SLAM, has been an active research area in the field of Robotics over the past three decades. For solving the SLAM problem, every robot is equipped with either a single sensor or a combination of similar/different sensors. This paper attempts to review, discuss, evaluate and compare these sensors. Keeping an eye on future, this paper also assesses the characteristics of these sensors against factors critical to the long-term autonomy challenge
Look, no Beacons! Optimal All-in-One EchoSLAM
We study the problem of simultaneously reconstructing a polygonal room and a
trajectory of a device equipped with a (nearly) collocated omnidirectional
source and receiver. The device measures arrival times of echoes of pulses
emitted by the source and picked up by the receiver. No prior knowledge about
the device's trajectory is required. Most existing approaches addressing this
problem assume multiple sources or receivers, or they assume that some of these
are static, serving as beacons. Unlike earlier approaches, we take into account
the measurement noise and various constraints on the geometry by formulating
the solution as a minimizer of a cost function similar to \emph{stress} in
multidimensional scaling. We study uniqueness of the reconstruction from
first-order echoes, and we show that in addition to the usual invariance to
rigid motions, new ambiguities arise for important classes of rooms and
trajectories. We support our theoretical developments with a number of
numerical experiments.Comment: 5 pages, 6 figures, submitted to Asilomar Conference on Signals,
Systems, and Computers Websit
Range-only SLAM with a mobile robot and a Wireless Sensor Networks
This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of a Wireless Sensor Network using only range measurements. The robot can estimate the distance to nearby nodes of the Wireless Sensor Network by measuring the Received Signal Strength Indicator (RSSI) of the received radio messages. The RSSI measure is very noisy, especially in an indoor environment due to interference and reflections of the radio signals. We adopted an Extended Kalman Filter SLAM algorithm to integrate RSSI measurements from the different nodes over time, while the robot moves in the environment. A simple pre-processing filter helps in reducing the RSSI variations due to interference and reflections. Successful experiments are reported in which an average localization error less than 1 m is obtained when the SLAM algorithm has no a priori knowledge on the wireless node positions, while a localization error less than 0.5 m can be achieved when the position of the node is initialized close to the their actual position. These results are obtained using a generic path loss model for the transmission channel. Moreover, no internode communication is necessary in the WSN. This can save energy and enables to apply the proposed system also to fully disconnected networks
Towards Autonomous Inspection of Space Systems Using Mobile Robotic Sensor Platforms
The space transportation systems required to support NASA's Exploration Initiative will demand a high degree of reliability to ensure mission success. This reliability can be realized through autonomous fault/damage detection and repair capabilities. It is crucial that such capabilities are incorporated into these systems since it will be impractical to rely upon Extra-Vehicular Activity (EVA), visual inspection or tele-operation due to the costly, labor-intensive and time-consuming nature of these methods. One approach to achieving this capability is through the use of an autonomous inspection system comprised of miniature mobile sensor platforms that will cooperatively perform high confidence inspection of space vehicles and habitats. This paper will discuss the efforts to develop a small scale demonstration test-bed to investigate the feasibility of using autonomous mobile sensor platforms to perform inspection operations. Progress will be discussed in technology areas including: the hardware implementation and demonstration of robotic sensor platforms, the implementation of a hardware test-bed facility, and the investigation of collaborative control algorithms
Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation
This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the technological constraints of the robot and beacons, many applications impose further resource consumption limitations. This paper presents a scalable distributed RO-SLAM scheme for resource-constrained operation. It is capable of exploiting robot-beacon cooperation in order to improve SLAM accuracy while meeting a given resource consumption bound expressed as the maximum number of measurements that are integrated in SLAM per iteration. The proposed scheme combines a Sparse Extended Information Filter (SEIF) SLAM method, in which each beacon gathers and integrates robot-beacon and inter-beacon measurements, and a distributed information-driven measurement allocation tool that dynamically selects the measurements that are integrated in SLAM, balancing uncertainty improvement and resource consumption. The scheme adopts a robot-beacon distributed approach in which each beacon participates in the selection, gathering and integration in SLAM of robot-beacon and inter-beacon measurements, resulting in significant estimation accuracies, resource-consumption efficiency and scalability. It has been integrated in an octorotor Unmanned Aerial System (UAS) and evaluated in 3D SLAM outdoor experiments. The experimental results obtained show its performance and robustness and evidence its advantages over existing methodsMinisterio de Educación, Cultura y Deportes DPI2014-59383-C2-1-
3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization
This work presents a 3D multi-robot exploration framework for a team of UGVs
moving on uneven terrains. The framework was designed by casting the two-level
coordination strategy presented in [1] into the context of multi-robot
exploration. The resulting distributed exploration technique minimizes and
explicitly manages the occurrence of conflicts and interferences in the robot
team. Each robot selects where to scan next by using a receding horizon
next-best-view approach [2]. A sampling-based tree is directly expanded on
segmented traversable regions of the terrain 3D map to generate the candidate
next viewpoints. During the exploration, users can assign locations with higher
priorities on-demand to steer the robot exploration toward areas of interest.
The proposed framework can be also used to perform coverage tasks in the case a
map of the environment is a priori provided as input. An open-source
implementation is available online
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