Pre-prints

  1. Şenbaşlar, B., Luiz, P., Hönig, W., & Sukhatme, G. S. (2023). MRNAV: Multi-robot aware planning and control stack for collision and deadlock-free navigation in cluttered environments. In arXiv preprint arXiv:2308.13499. [paper] [video]
  2. Şenbaşlar, B., & Sukhatme, G. S. (2023). Probabilistic Trajectory Planning for Static and Interaction-aware Dynamic Obstacle Avoidance. In arXiv preprint arXiv:2302.12873. [paper] [video]

Theses

  1. Şenbaşlar, B. (2023). Decentralized Real-Time Trajectory Planning for Multi-Robot Navigation in Cluttered Environments [PhD thesis]. University of Southern California. [thesis]

Journals

  1. Şenbaşlar, B., & Sukhatme, G. S. (2024). DREAM: Decentralized real-time asynchronous probabilistic trajectory planning for collision-free multi-robot navigation in cluttered environments. ArXiv Preprint ArXiv:2307.15887. (Accepted at IEEE Transactions in Robotics (T-RO). To appear.) [paper] [video]
  2. Denniston, C. E., Şenbaşlar, B., & Sukhatme, G. S. (2024). Active Signal Emitter Placement In Complex Environments. ArXiv Preprint ArXiv:2405.02719. (Accepted at IEEE Robotics and Automation Letters (RA-L). To appear.) [paper]
  3. Şenbaşlar, B., Hönig, W., & Ayanian, N. (2023). RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations. Autonomous Robots, 1–26. [paper] [video]

Conferences

  1. Huang, Z., Yang, Z., Krupani, R., Şenbaşlar, B., Batra, S., & Sukhatme, G. S. (2024). Collision avoidance and navigation for a quadrotor swarm using end-to-end deep reinforcement learning. 2024 IEEE International Conference on Robotics and Automation (ICRA), 300–306. [paper] [code] [website]
  2. Şenbaşlar, B., & Sukhatme, G. S. (2022). Asynchronous Real-time Decentralized Multi-Robot Trajectory Planning. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9972–9979. [paper]
  3. Ren, J., Sathiyanarayanan, V., Ewing, E., Şenbaşlar, B., & Ayanian, N. (2021). MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding Using Shortest Path Embeddings. Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, 1055–1063. [paper] [code]
  4. Şenbaşlar, B., Hönig, W., & Ayanian, N. (2019). Robust trajectory execution for multi-robot teams using distributed real-time replanning. Distributed Autonomous Robotic Systems: The 14th International Symposium, 167–181. [paper] [code] [video]

Workshops, Symposia, and Abstracts

  1. Şenbaşlar, B., Hönig, W., & Ayanian, N. (2021). RLSS: Real-time multi-robot trajectory replanning using linear spatial separations. In ICRA 2021 "Robot Swarms in the Real World: From Design to Deployment" Workshop. [paper] [code] [video]
  2. Ren, J., Sathiyanarayanan, V., Ewing, E., Şenbaşlar, B., & Ayanian, N. (2021). Automatic Optimal Multi-Agent Path Finding Algorithm Selector (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, Number 18, pp. 15877–15878). [paper]
  3. Şenbaşlar, B., Hönig, W., & Ayanian, N. (2019). Robust Trajectory Execution for Multi-robot Teams Using Distributed Real-time Replanning (Extended Abstract). In Southern California Robotics Symposium (SCR). [paper]
  4. Şenbaşlar, B., Hönig, W., & Ayanian, N. (2018). Robust Trajectory Execution for Multi-Robot Teams Using Distributed Real-time Replanning (Extended Abstract). In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Late Breaking). [paper]

Manuals

  1. Cacciola, F., Rouxel-Labbé, M., & Şenbaşlar, B. (2019). The Computational Geometry Algorithms Library (CGAL) Triangulated Surface Mesh Simplification User Manual. [manual]