Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866912135336951808 |
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| author | Amadasun, Uyiosa Philip McNamee, Patrick Ahmadabadi, Zahra Nili Naseradinmousavi, Peiman |
| author_facet | Amadasun, Uyiosa Philip McNamee, Patrick Ahmadabadi, Zahra Nili Naseradinmousavi, Peiman |
| contents | In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. The existing experimental works have not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique that reduces halts or disruptions in continuous chaotic trajectories, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This study addresses these problems by developing a novel applied framework for real-world applications of chaotic coverage path planners while providing techniques for effective obstacle avoidance, chaotic trajectory dispersal, and accurate real-time coverage calculation. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal path planner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_01834 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning Amadasun, Uyiosa Philip McNamee, Patrick Ahmadabadi, Zahra Nili Naseradinmousavi, Peiman Robotics Artificial Intelligence In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. The existing experimental works have not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique that reduces halts or disruptions in continuous chaotic trajectories, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This study addresses these problems by developing a novel applied framework for real-world applications of chaotic coverage path planners while providing techniques for effective obstacle avoidance, chaotic trajectory dispersal, and accurate real-time coverage calculation. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal path planner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations. |
| title | Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2305.01834 |