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Feature representation is critical not only for pattern recognition tasks but also for reinforcement learning (RL) methods to solve learning control problems under uncertainties. In this paper, a manifold-based RL approach using the principle of locally linear reconstruction (LLR) is proposed for Markov decision processes with large or continuous state spaces. In the proposed approach, an LLR-based...
In this paper, we implement a novel discrete-time Terminal Sliding Mode Control (TSMC) for real-time precise speed control of a two-link Carangiform robotic fish. First, using Newton's laws of motion, combined with Lighthill's slender body theory, we develop a dynamical model to analyse the robotic fish motion. The model attained is highly non-linear and non-affine in control input which is why many...
In security surveillance video (SSV), foreign object occlusion is increasingly common. Automatic detection of suspicious occlusion has become important. In this paper, a banner occlusion detection approach is proposed. The proposed approach first detects the banner in the image using both color feature and shape feature. More specifically, the proposed approach exploits the HSV color space to extract...
Path planning of a mobile robot under dynamic environment is a difficult part of robot navigation. In this paper, a new path planning method based on improved Q-learning (IQL) algorithm and some heuristic searching strategies is proposed for mobile robot in dynamic environment. A new exploration strategy which combines ε-greedy exploration with Boltzmann exploration is used in IQL. In addition, the...
Intelligent control of autonomous vehicles has been an important research topic due to the model uncertainties and complexities of vehicle dynamics. In this paper, we proposed an approximate dynamic programming (ADP) approach for path following control of an autonomous vehicle. The idea is to use the Dual Heuristic Programming (DHP) algorithm, which is an efficient class of ADP methods, to directly...
During the past decade, Rapidly-exploring Random Tree (RRT) and its variants are shown to be powerful sampling based single query path planning approaches for robots in high-dimensional configuration space. However, the performance of such tree-based planners that rely on uniform sampling strategy degrades significantly when narrow passages are contained in the configuration space. Given the assumption...
In this work, a dual-loop iterative learning control (ILC) scheme is designed for a class of nonlinear systems with hysteresis input uncertainty. The two ILC loops are applied to the nominal part and the hysteresis part respectively, to learn their unknown dynamics. Based on the convergence analysis for each single loop, a composite energy function method is then adopted to prove the learning convergence...
Most existing anycast routing algorithms are based on shortest path algorithm. In this paper, an adaptable anycast routing algorithm based on density and proximity is proposed. While determining anycast member, both the proximity factor and nearby anycast members number of the target (density factor) should be considered. Density is calculated on the base of field theory. In comparison with proximity-based...
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