Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team
Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Robotics |
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| Online Access: | https://www.mdpi.com/2218-6581/11/3/64 |
| _version_ | 1827656800891043840 |
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| author | Shan Yu Ku Goldie Nejat Beno Benhabib |
| author_facet | Shan Yu Ku Goldie Nejat Beno Benhabib |
| author_sort | Shan Yu Ku |
| collection | DOAJ |
| description | Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an <i>a priori</i> known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of <i>a priori</i> known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of <i>a priori</i> known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates. |
| first_indexed | 2024-03-09T22:34:25Z |
| format | Article |
| id | doaj.art-04a86a58f56f41839ba1f14f50d69e74 |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-03-09T22:34:25Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-04a86a58f56f41839ba1f14f50d69e742023-11-23T18:50:25ZengMDPI AGRobotics2218-65812022-06-011136410.3390/robotics11030064Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot TeamShan Yu Ku0Goldie Nejat1Beno Benhabib2Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, CanadaDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, CanadaDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, CanadaMobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an <i>a priori</i> known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of <i>a priori</i> known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of <i>a priori</i> known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates.https://www.mdpi.com/2218-6581/11/3/64multimodal robotsautonomous mobile-target searchwilderness search and rescueiso-probability curvesMonte Carlo tree search |
| spellingShingle | Shan Yu Ku Goldie Nejat Beno Benhabib Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team Robotics multimodal robots autonomous mobile-target search wilderness search and rescue iso-probability curves Monte Carlo tree search |
| title | Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team |
| title_full | Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team |
| title_fullStr | Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team |
| title_full_unstemmed | Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team |
| title_short | Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team |
| title_sort | wilderness search for lost persons using a multimodal aerial terrestrial robot team |
| topic | multimodal robots autonomous mobile-target search wilderness search and rescue iso-probability curves Monte Carlo tree search |
| url | https://www.mdpi.com/2218-6581/11/3/64 |
| work_keys_str_mv | AT shanyuku wildernesssearchforlostpersonsusingamultimodalaerialterrestrialrobotteam AT goldienejat wildernesssearchforlostpersonsusingamultimodalaerialterrestrialrobotteam AT benobenhabib wildernesssearchforlostpersonsusingamultimodalaerialterrestrialrobotteam |