3,195 research outputs found
End-to-end Reinforcement Learning for Online Coverage Path Planning in Unknown Environments
Coverage path planning is the problem of finding the shortest path that
covers the entire free space of a given confined area, with applications
ranging from robotic lawn mowing and vacuum cleaning, to demining and
search-and-rescue tasks. While offline methods can find provably complete, and
in some cases optimal, paths for known environments, their value is limited in
online scenarios where the environment is not known beforehand, especially in
the presence of non-static obstacles. We propose an end-to-end reinforcement
learning-based approach in continuous state and action space, for the online
coverage path planning problem that can handle unknown environments. We
construct the observation space from both global maps and local sensory inputs,
allowing the agent to plan a long-term path, and simultaneously act on
short-term obstacle detections. To account for large-scale environments, we
propose to use a multi-scale map input representation. Furthermore, we propose
a novel total variation reward term for eliminating thin strips of uncovered
space in the learned path. To validate the effectiveness of our approach, we
perform extensive experiments in simulation with a distance sensor, surpassing
the performance of a recent reinforcement learning-based approach
Active Mapping and Robot Exploration: A Survey
Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government
Simultaneous localization and mapping by using Low-Cost Ultrasonic Sensor for Underwater crawler
Autonomous robots can help people explore parts of the ocean that would be
hard or impossible to get to otherwise. The increase in the availability of
low-cost components has made it possible to innovate, design, and implement new
and innovative ideas for underwater robotics. Cost-effective and open solutions
that are available today can be used to replace expensive robot systems. The
prototype of an autonomous robot system that functions in brackish waterways in
settings such as fish hatcheries is presented in this research. The system has
low-cost ultrasonic sensors that use a SLAM algorithm to map and move through
the environment. When compared to previous studies that used Lidar sensors,
this system's configuration was chosen to keep costs down. A comparison is
shown between ultrasonic and lidar sensors, showing their respective pros and
cons
Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.
This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture.
The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks.
In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd
A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment
The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology. An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization
Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images
We introduce a multi-sensor navigation system for autonomous surface vessels
(ASV) intended for water-quality monitoring in freshwater lakes. Our mission
planner uses satellite imagery as a prior map, formulating offline a
mission-level policy for global navigation of the ASV and enabling autonomous
online execution via local perception and local planning modules. A significant
challenge is posed by the inconsistencies in traversability estimation between
satellite images and real lakes, due to environmental effects such as wind,
aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we
specifically modelled these traversability uncertainties as stochastic edges in
a graph and optimized for a mission-level policy that minimizes the expected
total travel distance. To execute the policy, we propose a modern local planner
architecture that processes sensor inputs and plans paths to execute the
high-level policy under uncertain traversability conditions. Our system was
tested on three km-scale missions on a Northern Ontario lake, demonstrating
that our GPS-, vision-, and sonar-enabled ASV system can effectively execute
the mission-level policy and disambiguate the traversability of stochastic
edges. Finally, we provide insights gained from practical field experience and
offer several future directions to enhance the overall reliability of ASV
navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv
admin note: text overlap with arXiv:2209.1186
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