2,674 research outputs found
Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader
This paper addresses a multi-pursuer single-evader pursuit-evasion game where
the free-moving evader moves faster than the pursuers. Most of the existing
works impose constraints on the faster evader such as limited moving area and
moving direction. When the faster evader is allowed to move freely without any
constraint, the main issues are how to form an encirclement to trap the evader
into the capture domain, how to balance between forming an encirclement and
approaching the faster evader, and what conditions make the capture possible.
In this paper, a distributed pursuit algorithm is proposed to enable pursuers
to form an encirclement and approach the faster evader. An algorithm that
balances between forming an encirclement and approaching the faster evader is
proposed. Moreover, sufficient capture conditions are derived based on the
initial spatial distribution and the speed ratios of the pursuers and the
evader. Simulation and experimental results on ground robots validate the
effectiveness and practicability of the proposed method
Kervolutional Neural Networks
Convolutional neural networks (CNNs) have enabled the state-of-the-art
performance in many computer vision tasks. However, little effort has been
devoted to establishing convolution in non-linear space. Existing works mainly
leverage on the activation layers, which can only provide point-wise
non-linearity. To solve this problem, a new operation, kervolution (kernel
convolution), is introduced to approximate complex behaviors of human
perception systems leveraging on the kernel trick. It generalizes convolution,
enhances the model capacity, and captures higher order interactions of
features, via patch-wise kernel functions, but without introducing additional
parameters. Extensive experiments show that kervolutional neural networks (KNN)
achieve higher accuracy and faster convergence than baseline CNN.Comment: oral paper in CVPR 201
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
Robust velocity and position estimation is crucial for autonomous robot
navigation. The optical flow based methods for autonomous navigation have been
receiving increasing attentions in tandem with the development of micro
unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC)
based algorithm to determine optical flow using a monocular camera, which is
named as correlation flow (CF). Correlation flow is able to provide reliable
and accurate velocity estimation and is robust to motion blur. In addition, it
can also estimate the altitude velocity and yaw rate, which are not available
by traditional methods. Autonomous flight tests on a quadcopter show that
correlation flow can provide robust trajectory estimation with very low
processing power. The source codes are released based on the ROS framework.Comment: 2018 International Conference on Robotics and Automation (ICRA 2018
Graph Optimization Approach to Range-based Localization
In this paper, we propose a general graph optimization based framework for
localization, which can accommodate different types of measurements with
varying measurement time intervals. Special emphasis will be on range-based
localization. Range and trajectory smoothness constraints are constructed in a
position graph, then the robot trajectory over a sliding window is estimated by
a graph based optimization algorithm. Moreover, convergence analysis of the
algorithm is provided, and the effects of the number of iterations and window
size in the optimization on the localization accuracy are analyzed. Extensive
experiments on quadcopter under a variety of scenarios verify the effectiveness
of the proposed algorithm and demonstrate a much higher localization accuracy
than the existing range-based localization methods, especially in the altitude
direction
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