Indoor Localization for Autonomous Robot Navigation
- URL: http://arxiv.org/abs/2502.20731v1
- Date: Fri, 28 Feb 2025 05:25:04 GMT
- Title: Indoor Localization for Autonomous Robot Navigation
- Authors: Sean Kouma, Rachel Masters,
- Abstract summary: This paper explores using indoor positioning systems (IPSs) for the indoor navigation of an autonomous robot.<n>We developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions.<n>After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we aim to explore using IPSs for the indoor navigation of an autonomous robot. We collected a dataset and trained models to test on a robot. We also developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions. After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time. The findings of this paper indicate that using IPSs for autonomous robots is a promising area of future research.
Related papers
- Autonomous Systems: Autonomous Systems: Indoor Drone Navigation [0.0]
The system creates a simulated quadcopter capable of travelling autonomously in an indoor environment.
The goal is to use the slam toolbox for ROS and the Nav2 navigation system framework to construct a simulated drone.
arXiv Detail & Related papers (2023-04-18T10:40:00Z) - GNM: A General Navigation Model to Drive Any Robot [67.40225397212717]
General goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots.
We analyze the necessary design decisions for effective data sharing across robots.
We deploy the trained GNM on a range of new robots, including an under quadrotor.
arXiv Detail & Related papers (2022-10-07T07:26:41Z) - Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human
Supervision [72.4735163268491]
Commercial and industrial deployments of robot fleets often fall back on remote human teleoperators during execution.
We formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors.
We propose Fleet-DAgger, a family of IFL algorithms, and compare a novel Fleet-DAgger algorithm to 4 baselines in simulation.
arXiv Detail & Related papers (2022-06-29T01:23:57Z) - Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of
Demonstrations for Social Navigation [92.66286342108934]
Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a'socially compliant' manner in the presence of other intelligent agents such as humans.
Our dataset contains 8.7 hours, 138 trajectories, 25 miles of socially compliant, human teleoperated driving demonstrations.
arXiv Detail & Related papers (2022-03-28T19:09:11Z) - 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) - Autonomous Navigation in Dynamic Environments: Deep Learning-Based
Approach [0.0]
This thesis studies different deep learning-based approaches, highlighting the advantages and disadvantages of each scheme.
One of the deep learning methods based on convolutional neural network (CNN) is realized by software implementations.
We propose a low-cost approach, for indoor applications such as restaurants, museums, etc, on the base of using a monocular camera instead of a laser scanner.
arXiv Detail & Related papers (2021-02-03T23:20:20Z) - LaND: Learning to Navigate from Disengagements [158.6392333480079]
We present a reinforcement learning approach for learning to navigate from disengagements, or LaND.
LaND learns a neural network model that predicts which actions lead to disengagements given the current sensory observation, and then at test time plans and executes actions that avoid disengagements.
Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches.
arXiv Detail & Related papers (2020-10-09T17:21:42Z) - OpenBot: Turning Smartphones into Robots [95.94432031144716]
Current robots are either expensive or make significant compromises on sensory richness, computational power, and communication capabilities.
We propose to leverage smartphones to equip robots with extensive sensor suites, powerful computational abilities, state-of-the-art communication channels, and access to a thriving software ecosystem.
We design a small electric vehicle that costs $50 and serves as a robot body for standard Android smartphones.
arXiv Detail & Related papers (2020-08-24T18:04:50Z) - Deep Reinforcement learning for real autonomous mobile robot navigation
in indoor environments [0.0]
We present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner.
The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the goal.
The output actions of an Asynchronous Advantage Actor-Critic network (GA3C) are the linear and angular velocities for the robot.
arXiv Detail & Related papers (2020-05-28T09:15:14Z)
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.