Dynamic Autonomous Surface Vehicle Control and Applications in
Environmental Monitoring
- URL: http://arxiv.org/abs/2103.15951v1
- Date: Mon, 29 Mar 2021 20:55:52 GMT
- Title: Dynamic Autonomous Surface Vehicle Control and Applications in
Environmental Monitoring
- Authors: Nare Karapetyan, Jason Moulton, and Ioannis Rekleitis
- Abstract summary: This paper addresses the problem of robotic operations in the presence of adversarial forces.
The presence of wind and/or currents produces external forces acting on the vehicle which quite often divert it from its intended path.
By measuring these phenomena, wind and current, and modelling their impact on the vessel, actions can be taken to alleviate their effect.
- Score: 1.6774978731594548
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper addresses the problem of robotic operations in the presence of
adversarial forces. We presents a complete framework for survey operations:
waypoint generation,modelling of forces and tuning the control. In many
applications of environmental monitoring, search and exploration, and
bathymetric mapping, the vehicle has to traverse in straight lines parallel to
each other, ensuring there are no gaps and no redundant coverage. During
operations with an Autonomous Surface Vehicle (ASV) however, the presence of
wind and/or currents produces external forces acting on the vehicle which quite
often divert it from its intended path. Similar issues have been encountered
during aerial or underwater operations. By measuring these phenomena, wind and
current, and modelling their impact on the vessel, actions can be taken to
alleviate their effect and ensure the correct trajectory is followed.
Related papers
- Autonomous Hiking Trail Navigation via Semantic Segmentation and Geometric Analysis [2.1149122372776743]
This work introduces a novel approach to autonomous hiking trail navigation that balances trail adherence with the flexibility to adapt to off-trail routes when necessary.
The solution is a Traversability Analysis module that integrates semantic data from camera images with geometric information from LiDAR to create a comprehensive understanding of the surrounding terrain.
A planner uses this traversability map to navigate safely, adhering to trails while allowing off-trail movement when necessary to avoid on-trail hazards or for safe off-trail shortcuts.
arXiv Detail & Related papers (2024-09-24T02:21:10Z) - Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - Vision-Based Autonomous Navigation for Unmanned Surface Vessel in
Extreme Marine Conditions [2.8983738640808645]
This paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions.
The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog.
The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset.
arXiv Detail & Related papers (2023-08-08T14:25:13Z) - Aeolus Ocean -- A simulation environment for the autonomous
COLREG-compliant navigation of Unmanned Surface Vehicles using Deep
Reinforcement Learning and Maritime Object Detection [0.0]
navigational autonomy in unmanned surface vehicles (USVs) in the maritime sector can lead to safer waters as well as reduced operating costs.
We describe the novel development of a COLREG-compliant DRL-based collision avoidant navigational system with CV-based awareness in a realistic ocean simulation environment.
arXiv Detail & Related papers (2023-07-13T11:20:18Z) - Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts,
Datasets and Metrics [77.34726150561087]
This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles.
Concepts and characteristics related to both sensors, as well as to their fusion, are presented.
We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
arXiv Detail & Related papers (2023-03-08T00:48:32Z) - Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing.
We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
arXiv Detail & Related papers (2022-06-28T17:08:31Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention.
Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
arXiv Detail & Related papers (2021-04-19T11:48:13Z) - Studying Person-Specific Pointing and Gaze Behavior for Multimodal
Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing.
Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints.
We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
arXiv Detail & Related papers (2020-09-23T14:56:19Z) - Probabilistic End-to-End Vehicle Navigation in Complex Dynamic
Environments with Multimodal Sensor Fusion [16.018962965273495]
All-day and all-weather navigation is a critical capability for autonomous driving.
We propose a probabilistic driving model with ultiperception capability utilizing the information from the camera, lidar and radar.
The results suggest that our proposed model outperforms baselines and achieves excellent generalization performance in unseen environments.
arXiv Detail & Related papers (2020-05-05T03:48:10Z)
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.