UGSim: Autonomous Buoyancy-Driven Underwater Glider Simulator with LQR Control Strategy and Recursive Guidance System
- URL: http://arxiv.org/abs/2501.17851v1
- Date: Wed, 29 Jan 2025 18:50:41 GMT
- Title: UGSim: Autonomous Buoyancy-Driven Underwater Glider Simulator with LQR Control Strategy and Recursive Guidance System
- Authors: Zhizun Xu, Yang Song, Jiabao Zhu, Weichao Shi,
- Abstract summary: This paper presents the UGSim, a simulator for buoyancy-driven gliders.
It is designed to address unique challenges that come from the complex hydrodynamic and hydrostatic impacts on buoyancy-driven gliders.
The simulator is provided to accelerate the development and the evaluation of algorithms that would otherwise require expensive and time-consuming operations at sea.
- Score: 3.8632181427836945
- License:
- Abstract: This paper presents the UGSim, a simulator for buoyancy-driven gliders, with a LQR control strategy, and a recursive guidance system. Building on the top of the DAVE and the UUVsim, it is designed to address unique challenges that come from the complex hydrodynamic and hydrostatic impacts on buoyancy-driven gliders, which conventional robotics simulators can't deal with. Since distinguishing features of the class of vehicles, general controllers and guidance systems developed for underwater robotics are infeasible. The simulator is provided to accelerate the development and the evaluation of algorithms that would otherwise require expensive and time-consuming operations at sea. It consists of a basic kinetic module, a LQR control module and a recursive guidance module, which allows the user to concentrate on the single problem rather than the whole robotics system and the software infrastructure. We demonstrate the usage of the simulator through an example, loading the configuration of the buoyancy-driven glider named Petrel-II, presenting its dynamics simulation, performances of the control strategy and the guidance system.
Related papers
- Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping [2.9109581496560044]
This paper examines the robustness of benchmark deep reinforcement learning (RL) algorithms, implemented for inland waterway transport (IWT) within an autonomous shipping simulator.
We show that a model-free approach can achieve an adequate policy in the simulator, successfully navigating port environments never encountered during training.
arXiv Detail & Related papers (2024-11-07T17:55:07Z) - Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers.
Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy.
We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
arXiv Detail & Related papers (2024-09-12T11:50:06Z) - Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks.
We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks.
In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
arXiv Detail & Related papers (2024-06-21T13:48:37Z) - Reinforcement-learning robotic sailboats: simulator and preliminary
results [0.37918614538294315]
This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins.
We introduce the key features for building virtual worlds, considering using Reinforcement Learning (RL) agents for autonomous navigation and control.
arXiv Detail & Related papers (2024-01-16T09:04:05Z) - Learning to Fly in Seconds [7.259696592534715]
We show how curriculum learning and a highly optimized simulator enhance sample complexity and lead to fast training times.
Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct control after only 18 seconds of training on a consumer-grade laptop.
arXiv Detail & Related papers (2023-11-22T01:06:45Z) - FastRLAP: A System for Learning High-Speed Driving via Deep RL and
Autonomous Practicing [71.76084256567599]
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL)
Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations.
The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
arXiv Detail & Related papers (2023-04-19T17:33:47Z) - Residual Physics Learning and System Identification for Sim-to-real
Transfer of Policies on Buoyancy Assisted Legged Robots [14.760426243769308]
In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification.
Rather than relying on standard supervised learning formulations, we utilize deep reinforcement learning to train an external force policy.
We analyze the improved simulation fidelity by comparing the simulation trajectories against the real-world ones.
arXiv Detail & Related papers (2023-03-16T18:49:05Z) - A Hybrid Tracking Control Strategy for an Unmanned Underwater Vehicle
Aided with Bioinspired Neural Dynamics [14.66072990853587]
This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired neural dynamics model.
An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands.
Then, a novel sliding mode control is proposed, which is capable of providing smooth and continuous torque commands.
arXiv Detail & Related papers (2022-09-03T19:18:54Z) - Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle.
In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
arXiv Detail & Related papers (2022-07-05T16:33:20Z) - DriveGAN: Towards a Controllable High-Quality Neural Simulation [147.6822288981004]
We introduce a novel high-quality neural simulator referred to as DriveGAN.
DriveGAN achieves controllability by disentangling different components without supervision.
We train DriveGAN on multiple datasets, including 160 hours of real-world driving data.
arXiv Detail & Related papers (2021-04-30T15:30:05Z) - A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the
First CARLA Autonomous Driving Challenge [49.976633450740145]
This paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment.
Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge.
arXiv Detail & Related papers (2020-10-23T18:07:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.