2,956 research outputs found
Active Object Classification from 3D Range Data with Mobile Robots
This thesis addresses the problem of how to improve the acquisition of 3D range data with a mobile robot for the task of object classification. Establishing the identities of objects in unknown environments is fundamental for robotic systems and helps enable many abilities such as grasping, manipulation, or semantic mapping. Objects are recognised by data obtained from sensor observations, however, data is highly dependent on viewpoint; the variation in position and orientation of the sensor relative to an object can result in large variation in the perception quality. Additionally, cluttered environments present a further challenge because key data may be missing. These issues are not always solved by traditional passive systems where data are collected from a fixed navigation process then fed into a perception pipeline. This thesis considers an active approach to data collection by deciding where is most appropriate to make observations for the perception task. The core contributions of this thesis are a non-myopic planning strategy to collect data efficiently under resource constraints, and supporting viewpoint prediction and evaluation methods for object classification. Our approach to planning uses Monte Carlo methods coupled with a classifier based on non-parametric Bayesian regression. We present a novel anytime and non-myopic planning algorithm, Monte Carlo active perception, that extends Monte Carlo tree search to partially observable environments and the active perception problem. This is combined with a particle-based estimation process and a learned observation likelihood model that uses Gaussian process regression. To support planning, we present 3D point cloud prediction algorithms and utility functions that measure the quality of viewpoints by their discriminatory ability and effectiveness under occlusion. The utility of viewpoints is quantified by information-theoretic metrics, such as mutual information, and an alternative utility function that exploits learned data is developed for special cases. The algorithms in this thesis are demonstrated in a variety of scenarios. We extensively test our online planning and classification methods in simulation as well as with indoor and outdoor datasets. Furthermore, we perform hardware experiments with different mobile platforms equipped with different types of sensors. Most significantly, our hardware experiments with an outdoor robot are to our knowledge the first demonstrations of online active perception in a real outdoor environment. Active perception has broad significance in many applications. This thesis emphasises the advantages of an active approach to object classification and presents its assimilation with a wide range of robotic systems, sensors, and perception algorithms. By demonstration of performance enhancements and diversity, our hope is that the concept of considering perception and planning in an integrated manner will be of benefit in improving current systems that rely on passive data collection
Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes
Civil and maritime engineering systems, among others, from bridges to
offshore platforms and wind turbines, must be efficiently managed as they are
exposed to deterioration mechanisms throughout their operational life, such as
fatigue or corrosion. Identifying optimal inspection and maintenance policies
demands the solution of a complex sequential decision-making problem under
uncertainty, with the main objective of efficiently controlling the risk
associated with structural failures. Addressing this complexity, risk-based
inspection planning methodologies, supported often by dynamic Bayesian
networks, evaluate a set of pre-defined heuristic decision rules to reasonably
simplify the decision problem. However, the resulting policies may be
compromised by the limited space considered in the definition of the decision
rules. Avoiding this limitation, Partially Observable Markov Decision Processes
(POMDPs) provide a principled mathematical methodology for stochastic optimal
control under uncertain action outcomes and observations, in which the optimal
actions are prescribed as a function of the entire, dynamically updated, state
probability distribution. In this paper, we combine dynamic Bayesian networks
with POMDPs in a joint framework for optimal inspection and maintenance
planning, and we provide the formulation for developing both infinite and
finite horizon POMDPs in a structural reliability context. The proposed
methodology is implemented and tested for the case of a structural component
subject to fatigue deterioration, demonstrating the capability of
state-of-the-art point-based POMDP solvers for solving the underlying planning
optimization problem. Within the numerical experiments, POMDP and
heuristic-based policies are thoroughly compared, and results showcase that
POMDPs achieve substantially lower costs as compared to their counterparts,
even for traditional problem settings
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