2,880 research outputs found
DMVP: Foremost Waypoint Coverage of Time-Varying Graphs
We consider the Dynamic Map Visitation Problem (DMVP), in which a team of
agents must visit a collection of critical locations as quickly as possible, in
an environment that may change rapidly and unpredictably during the agents'
navigation. We apply recent formulations of time-varying graphs (TVGs) to DMVP,
shedding new light on the computational hierarchy of TVG classes by analyzing them in the
context of graph navigation. We provide hardness results for all three classes,
and for several restricted topologies, we show a separation between the classes
by showing severe inapproximability in , limited approximability
in , and tractability in . We also give topologies in
which DMVP in is fixed parameter tractable, which may serve as a
first step toward fully characterizing the features that make DMVP difficult.Comment: 24 pages. Full version of paper from Proceedings of WG 2014, LNCS,
Springer-Verla
System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams
NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots.
The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model.
A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern.
After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand.
This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals
Random Neural Networks and Optimisation
In this thesis we introduce new models and learning algorithms for the Random
Neural Network (RNN), and we develop RNN-based and other approaches for the
solution of emergency management optimisation problems.
With respect to RNN developments, two novel supervised learning algorithms are
proposed. The first, is a gradient descent algorithm for an RNN extension model
that we have introduced, the RNN with synchronised interactions (RNNSI), which
was inspired from the synchronised firing activity observed in brain neural circuits.
The second algorithm is based on modelling the signal-flow equations in RNN as a
nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory
quasi-Newton algorithm specifically designed for the RNN case.
Regarding the investigation of emergency management optimisation problems,
we examine combinatorial assignment problems that require fast, distributed and
close to optimal solution, under information uncertainty. We consider three different
problems with the above characteristics associated with the assignment of
emergency units to incidents with injured civilians (AEUI), the assignment of assets
to tasks under execution uncertainty (ATAU), and the deployment of a robotic
network to establish communication with trapped civilians (DRNCTC).
AEUI is solved by training an RNN tool with instances of the optimisation problem
and then using the trained RNN for decision making; training is achieved using
the developed learning algorithms. For the solution of ATAU problem, we introduce
two different approaches. The first is based on mapping parameters of the
optimisation problem to RNN parameters, and the second on solving a sequence of
minimum cost flow problems on appropriately constructed networks with estimated
arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer
linear programming formulation, which is based on network flows. Finally, we design
and implement distributed heuristic algorithms for the deployment of robots
when the civilian locations are known or uncertain
Path planning with spatiotemporal optimal stopping for stochastic mission monitoring
© 2017 IEEE. We consider an optimal stopping formulation of the mission monitoring problem, in which a monitor vehicle must remain in close proximity to an autonomous robot that stochastically follows a predicted trajectory. This problem arises in a diverse range of scenarios, such as autonomous underwater vehicles supervised by surface vessels, pedestrians monitored by aerial vehicles, and animals monitored by agricultural robots. The key problem characteristics we consider are that the monitor must remain stationary while observing the robot, robot motion is modeled in general as a stochastic process, and observations are modeled as a spatial probability distribution. We propose a resolution-complete algorithm that runs in a polynomial time. The algorithm is based on a sweep-plane approach and generates a motion plan that maximizes the expected observation time and value. A variety of stochastic models may be used to represent the robot trajectory. We present results with data drawn from real AUV missions, a real pedestrian trajectory dataset and Monte Carlo simulations. Our results demonstrate the performance and behavior of our algorithm, and relevance to a variety of applications
Robot-assisted Soil Apparent Electrical Conductivity Measurements in Orchards
Soil apparent electrical conductivity (ECa) is a vital metric in Precision
Agriculture and Smart Farming, as it is used for optimal water content
management, geological mapping, and yield prediction. Several existing methods
seeking to estimate soil electrical conductivity are available, including
physical soil sampling, ground sensor installation and monitoring, and the use
of sensors that can obtain proximal ECa estimates. However, such methods can be
either very laborious and/or too costly for practical use over larger field
canopies. Robot-assisted ECa measurements, in contrast, may offer a scalable
and cost-effective solution. In this work, we present one such solution that
involves a ground mobile robot equipped with a customized and adjustable
platform to hold an Electromagnetic Induction (EMI) sensor to perform
semi-autonomous and on-demand ECa measurements under various field conditions.
The platform is designed to be easily re-configurable in terms of sensor
placement; results from testing for traversability and robot-to-sensor
interference across multiple case studies help establish appropriate tradeoffs
for sensor placement. Further, a developed simulation software package enables
rapid and accessible estimation of terrain traversability in relation to
desired EMI sensor placement. Extensive experimental evaluation across
different fields demonstrates that the obtained robot-assisted ECa measurements
are of high linearity compared with the ground truth (data collected manually
by a handheld EMI sensor) by scoring more than in Pearson correlation
coefficient in both plot measurements and estimated ECa maps generated by
kriging interpolation. The proposed robotic solution supports autonomous
behavior development in the field since it utilizes the ROS navigation stack
along with the RTK GNSS positioning data and features various ranging sensors.Comment: 15 pages, 16 figure
Developing an online cooperative police patrol routing strategy
A cooperative routing strategy for daily operations is necessary to maintain the effects of hotspot policing and to reduce crime and disorder. Existing robot patrol routing strategies are not suitable, as they omit the peculiarities and challenges of daily police patrol including minimising the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability to patrol routes. In this research, we propose a set of guidelines for patrol routing strategies to meet the challenges of police patrol. Following these guidelines, we develop an innovative heuristic-based and Bayesian-inspired real-time strategy for cooperative routing police patrols. Using two real-world cases and a benchmark patrol strategy, an online agent-based simulation has been implemented to testify the efficiency, flexibility, scalability, unpredictability, and robustness of the proposed strategy and the usability of the proposed guidelines
Autonomous Navigation in Complex Indoor and Outdoor Environments with Micro Aerial Vehicles
Micro aerial vehicles (MAVs) are ideal platforms for surveillance and search and rescue in confined indoor and outdoor environments due to their small size, superior mobility, and hover capability. In such missions, it is essential that the MAV is capable of autonomous flight to minimize operator workload. Despite recent successes in commercialization of GPS-based autonomous MAVs, autonomous navigation in complex and possibly GPS-denied environments gives rise to challenging engineering problems that require an integrated approach to perception, estimation, planning, control, and high level situational awareness. Among these, state estimation is the first and most critical component for autonomous flight, especially because of the inherently fast dynamics of MAVs and the possibly unknown environmental conditions. In this thesis, we present methodologies and system designs, with a focus on state estimation, that enable a light-weight off-the-shelf quadrotor MAV to autonomously navigate complex unknown indoor and outdoor environments using only onboard sensing and computation. We start by developing laser and vision-based state estimation methodologies for indoor autonomous flight. We then investigate fusion from heterogeneous sensors to improve robustness and enable operations in complex indoor and outdoor environments. We further propose estimation algorithms for on-the-fly initialization and online failure recovery. Finally, we present planning, control, and environment coverage strategies for integrated high-level autonomy behaviors. Extensive online experimental results are presented throughout the thesis. We conclude by proposing future research opportunities
Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach
In this paper, we aim to design sparse D-optimal (determinantoptimal)
pose-graph SLAM problems through the synthesis of sparse graphs with the
maximum weighted number of spanning trees. Characterizing graphs with the
maximum number of spanning trees is an open problem in general. To tackle this
problem, several new theoretical results are established in this paper,
including the monotone log-submodularity of the weighted number of spanning
trees. By exploiting these structures, we design a complementary pair of
near-optimal efficient approximation algorithms with provable guarantees. Our
theoretical results are validated using random graphs and a publicly available
pose-graph SLAM dataset.Comment: WAFR 201
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