21,061 research outputs found
Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network
IntroductionSince tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.MethodsA new fractional exponential activation function is designed in this study, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of the finite-time convergence zeroing neural network model is investigated under different error disturbances.Results and discussionNumerical experiments of tracking an eight-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robots even in a disturbance environment
Vision-based Autonomous Tracking of a Non-cooperative Mobile Robot by a Low-cost Quadrotor Vehicle
The goal of this thesis is the detection and tracking of a ground vehicle, in particular a car-like robot, by a quadrotor. The first challenge to address in any pursuit or tracking scenario is the detection and unique identification of the target. From this first challenge, comes the need to precisely localize the target in a coordinate system that is common to the tracking and tracked vehicles. In most real-life scenarios, the tracked vehicle does not directly communicate information such as its position to the tracking one. From this fact, arises a non-cooperative constraint problem. The autonomous tracking aspect of the mission requires, for both the aerial and ground vehicles, robust pose estimation during the mission. The primary and crucial functions to achieve autonomous behaviors are control and navigation. The principal-agent being the quadrotor, this thesis explains in detail the derivation and analysis of the equations of motion that govern its natural behavior along with the control methods that permit to achieve desired performances. The analysis of these equations reveals a naturally unstable system, subject to non-linearities. Therefore, we explored three different control methods capable of guaranteeing stability while mitigating non-linearities. The first two control methods operate in the linear region and consist of the intuitive Proportional Integrate Derivative controller (PID). The second linear control strategy is represented by an optimal controller that is the Linear Quadratic Regulator controller (LQR). The last and final control method is a nonlinear controller designed from the Sliding Mode Control Theory. In addition to the in-depth analysis, we provide assets and limitations of each control method. In order to achieve the tracking mission, we address the detection and localization problems using respectively visual servoing and frame transform techniques. The pose estimation challenge for the aerial robot is cleared up using Kalman Filtering estimation methods that are also explored in depth. The same estimation method is used to mitigate the ground vehicle’s real-time pose estimation and tracking problem. Analysis results are illustrated using Matlab. A simulation and a real implementation using the Robot Operating System are used to support the obtained results
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning
Reinforcement Learning (RL) allows learning non-trivial robot control laws
purely from data. However, many successful applications of RL have relied on
ad-hoc regularizations, such as hand-crafted curricula, to regularize the
learning performance. In this paper, we pair a recent algorithm for
automatically building curricula with RL on massively parallelized simulations
to learn a tracking controller for a spherical pendulum on a robotic arm via
RL. Through an improved optimization scheme that better respects the
non-Euclidean task structure, we allow the method to reliably generate
curricula of trajectories to be tracked, resulting in faster and more robust
learning compared to an RL baseline that does not exploit this form of
structured learning. The learned policy matches the performance of an optimal
control baseline on the real system, demonstrating the potential of curriculum
RL to jointly learn state estimation and control for non-linear tracking tasks
Fast Simulation of Vehicles with Non-deformable Tracks
This paper presents a novel technique that allows for both computationally
fast and sufficiently plausible simulation of vehicles with non-deformable
tracks. The method is based on an effect we have called Contact Surface Motion.
A comparison with several other methods for simulation of tracked vehicle
dynamics is presented with the aim to evaluate methods that are available
off-the-shelf or with minimum effort in general-purpose robotics simulators.
The proposed method is implemented as a plugin for the open-source
physics-based simulator Gazebo using the Open Dynamics Engine.Comment: Submitted to IROS 201
Learning the dynamics of articulated tracked vehicles
In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV
A Hybrid Approach for Trajectory Control Design
This work presents a methodology to design trajectory tracking feedback
control laws, which embed non-parametric statistical models, such as Gaussian
Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired
slippages. The proposed approach has the benefit of avoiding complex
terramechanics analysis to directly estimate from data the robot dynamics on a
wide class of trajectories. Experiments in both real and simulated environments
prove that the proposed methodology is promising.Comment: 9 pages, 11 figure
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
We show dynamic locomotion strategies for wheeled quadrupedal robots, which
combine the advantages of both walking and driving. The developed optimization
framework tightly integrates the additional degrees of freedom introduced by
the wheels. Our approach relies on a zero-moment point based motion
optimization which continuously updates reference trajectories. The reference
motions are tracked by a hierarchical whole-body controller which computes
optimal generalized accelerations and contact forces by solving a sequence of
prioritized tasks including the nonholonomic rolling constraints. Our approach
has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled
including the non-steerable wheels attached to its legs. We conducted
experiments on flat and inclined terrains as well as over steps, whereby we
show that integrating the wheels into the motion control and planning framework
results in intuitive motion trajectories, which enable more robust and dynamic
locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4
m/s and a reduction of the cost of transport by 83 % we prove the superiority
of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter
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