Abstract
This work addresses the problem of autonomous target navigation in indoor environments through wireless sensing. To accomplish accurate navigation, it proposes a novel yet simple localization algorithm based on basic geometry and Weighted Central Mass (WCM) by extracting range measurements from received wireless signals. To avoid obstacle collision in the considered indoor environments, the work proposes a new obstacle detection scheme that is based on wireless sensing, where abrupt signal fluctuations throughout the target's movement are exploited to detect and avoid obstructions. Therefore, integrating the two proposed solutions allows for partially autonomous target navigation in indoor environments without resorting to expensive and complex hardware, such as LiDARs or cameras. The proposed solutions are validated through both simulation and experimental test beds, that corroborate their effectiveness, both in terms of navigation and obstacle detection accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 2627-2641 |
| Number of pages | 15 |
| Journal | IEEE Open Journal of Vehicular Technology |
| Volume | 6 |
| DOIs | |
| Publication status | Published - 2025 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Funding
This work was supported in part by Fundação para a Ciência e Tecnologia under Project 2021.04180, in part by CEECIND under Grant UIDB/50008/2020 (10.54499/UIDB/50008/2020), in part by LASIGE Research Unit Ref. UID/00408/2025 - LASIGE and under the Grant SFRH/BD/00435/2025, in part by Instituto Lusófono de Investigação e Desenvolvimento COFAC/ILIND/COPELABS/4/2023, in part by the European Union’s Horizon Europe Research and Innovation Programme through Marie Skłodowska-Curie under Grant 101086387, in part by the Science Fund of the Republic of Serbia under Grant 221, and in part by Agile Drone Swarm Control based on Federated Reinforcement Learning and Optimization - ASCENT. SLAVISA TOMIC received the M.S. degree in traf-fic engineering according to the postal traffic and telecommunications study program from the Uni-versity of Novi Sad, Novi Sad, Serbia, in 2010, and the Ph.D. degree in electrical and computer engineering from the University Nova of Lisbon, Lisbon, Portugal, in 2017. He is currently an Asso-ciate Professor with Lusófona University, Lisbon. His research interests include target localization in wireless sensor networks, non-linear and convex optimization. He is one of the winners of the 4th edition of Scientific Employment Stimulus (CEEC Individual 2021) funded by Fundação para a Ciência e a Tecnologia. According to the methodology proposed by Stanford University, he was among the most influential researchers in the world between 2019 and 2023 when he joined the list of top 2% of scientists whose work is most cited by other colleagues in the field of Information and Communication Technologies, sub-area Networks and Telecommunications.
| Funders | Funder number |
|---|---|
| European Union’s Horizon Europe research and innovation programme | |
| Instituto Lusófono de Investigação e Desenvolvimento | COFAC/ILIND/COPELABS/4/2023 |
| LASIGE | SFRH/BD/00435/2025, UID/00408/2025 |
| FCT - Fundação para a Ciência e a Tecnologia | 2021.04180 |
| CEECIND | UIDB/50008/2020 |
| H2020 Marie Skłodowska-Curie Actions | 101086387 |
| Science Fund of the Republic of Serbia | 221 |
Keywords
- Autonomous navigation
- collision avoidance
- indoor navigation
- obstacle detection
- weighted central mass (WCM)
