2000 character limit reached
Federated Multi-Agent Mapping for Planetary Exploration
Published 2 Apr 2024 in cs.RO, cs.LG, and cs.MA | (2404.02289v3)
Abstract: Multi-agent robotic exploration stands to play an important role in space exploration as the next generation of robotic systems ventures to far-flung environments. A key challenge in this new paradigm will be to effectively share and utilize the vast amount of data generated onboard while operating in bandwidth-constrained regimes typical of space missions. Federated learning (FL) is a promising tool for bridging this gap. Drawing inspiration from the upcoming CADRE Lunar rover mission, we propose a federated multi-agent mapping approach that jointly trains a global map model across agents without transmitting raw data. Our method leverages implicit neural mapping to generate parsimonious, adaptable representations, reducing data transmission by up to 93.8% compared to raw maps. Furthermore, we enhance this approach with meta-initialization on Earth-based traversability datasets to significantly accelerate map convergence; reducing iterations required to reach target performance by 80% compared to random initialization. We demonstrate the efficacy of our approach on Martian terrains and glacier datasets, achieving downstream path planning F1 scores as high as 0.95 while outperforming on map reconstruction losses.
- M. J. Schuster, M. G. Müller, S. G. Brunner, H. Lehner, and others, “The ARCHES space-analogue demonstration mission: Towards heterogeneous teams of autonomous robots for collaborative scientific sampling in planetary exploration,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5315–5322, 2020.
- F. Rossi, M. Saboia, and J. Krishnamoorthy, Vander Hook, “Proximal exploration of Venus volcanism with teams of autonomous buoyancy-controlled balloons,” Acta Astronautica, vol. 208, no. 2, pp. 389–406, 2023.
- J.-P. de la Croix, F. Rossi, R. Brockers, D. Aguilar, K. Albee, E. Boroson, A. Cauligi, J. Delaune, and others, “Multi-agent autonomy for space exploration on the CADRE Lunar technology demonstration mission,” in IEEE Aerospace Conference, 2024.
- Y. Park, D.-J. Han, D.-Y. Kim, J. Seo, and J. Moon, “Few-round learning for federated learning,” in Conf. on Neural Information Processing Systems, 2021.
- Y. Zhou, G. Pu, X. Ma, X. Li, and D. Wu, “Distilled one-shot federated learning,” 2021. Available at https://arxiv.org/pdf/2009.07999.pdf.
- A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” Int. Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.
- J. Geyer, Y. Kassahun, M. Mahmudi, X. Ricou, R. Durgesh, A. S. Chung, L. Hauswald, V. H. Pham, M. Mühlegg, S. Dorn, et al., “A2d2: Audi autonomous driving dataset,” 2020. Available at https://arxiv.org/pdf/2004.06320.pdf.
- P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al., “Scalability in perception for autonomous driving: Waymo Open Dataset,” in IEEE Conf. on Computer Vision and Pattern Recognition, 2020.
- G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu, “AID: A benchmark data set for performance evaluation of aerial scene classification,” IEEE Transactions on Geosciences and Remote Sensing, vol. 55, no. 7, pp. 3965–3981, 2011.
- Y. Tian, Y. Chang, L. Quang, A. Schang, C. Nieto-Granda, J. P. How, and L. Carlone, “Resilient and distributed multi-robot visual slam: Datasets, experiments, and lessons learned,” 2023.
- R. Thakker, M. Paton, B. Jones, G. Daddi, R. Royce, M. R. Swan, M. Swan, and others, “To boldly go where no robots have gone before - part 4: NEO autonomy for robustly exploring unknown, extreme environments with versatile robots,” in AIAA Scitech Forum, 2024.
- T. Wilhelm, M. Geis, J. Püttschneider, T. Sievernich, T. Weber, K. Wohlfarth, and C. Wöhler, “DoMars16k: A diverse dataset for weakly supervised geomorphologic analysis on Mars,” Remote Sensing, vol. 12, no. 23, p. 3981, 2020.
- V. Verma, M. W. Maimone, D. M. Gaines, R. Francis, T. A. Estlin, S. R. Kuhn, G. R. Rabideau, S. A. Chien, M. McHenry, E. J. Graser, A. L. Rankin, and E. R. Thiel, “Autonomous robotics is driving Perseverance rover’s progress on Mars,” Science Robotics, vol. 8, no. 80, pp. 1–12, 2023.
- A. R. Khairuddin, M. S. Talib, and H. Haron, “Review on simultaneous localization and mapping (SLAM),” in Int. Conf. on Control System, Computing and Engineering, 2015.
- MIT Press, 2005.
- T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter, “Elevation mapping for locomotion and navigation using GPU,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2022.
- J. Yu, J. A. Vincent, and M. Schwager, “DiNNO: Distributed neural network optimization for multi-robot collaborative learning,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1896–1903, 2022.
- S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1–122, 2011.
- B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “NeRF: representing scenes as neural radiance fields for view synthesis,” Communications of the ACM, vol. 65, no. 1, pp. 99–106, 2021.
- V. Sitzmann, J. N. P. Martel, A. W. Bergman, D. B. Lindell, and G. Wetzstein, “Implicit neural representations with periodic activation functions,” in Conf. on Neural Information Processing Systems, 2020.
- N. Rahaman, A. Baratin, D. Arpit, F. Draxler, M. Lin, F. Hamprecht, Y. Bengio, and A. Courville, “On the spectral bias of neural networks,” in Int. Conf. on Machine Learning, 2019.
- M. Tancik, P. P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. Barron, and R. Ng, “Fourier features let networks learn high frequency functions in low dimensional domains,” in Conf. on Neural Information Processing Systems, 2020.
- O. Kwon, J. Park, and S. Oh, “Renderable neural radiance map for visual navigation,” in IEEE Conf. on Computer Vision and Pattern Recognition, 2023.
- H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Aguera y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in AI & Statistics, 2017.
- A. Shamsian, A. Navon, E. Fetaya, and G. Chechik, “Personalized federated learning using hypernetworks,” in Int. Conf. on Machine Learning, 2021.
- V. Chauhan, S. Molaei, D. Clifton, P. Lu, and J. Zhou, “A brief review of hypernetworks in deep learning,” 2023.
- L. Holden, F. Dayoub, D. Harvey, and T.-J. Chin, “Federated Neural Radiance Fields,” 2023. Available at https://arxiv.org/pdf/2305.01163.pdf.
- T. Suzuki, “Federated learning for large-scale scene modeling with Neural Radiance Fields,” 2023. Available at https://arxiv.org/pdf/2309.06030.pdf.
- A. Nichol and J. Schulman, “Reptile: a scalable metalearning algorithm,” 2018. Available at https://arxiv.org/pdf/1803.02999.pdf.
- M. Tancik, B. Mildenhall, T. Wang, D. Schmidt, P. P. Srinivasan, J. T. Barron, and R. Ng, “Learned initializations for optimizing coordinate-based neural representations,” in IEEE Conf. on Computer Vision and Pattern Recognition, 2021.
- A. B. Agarap, “Deep learning using rectified linear units (ReLU),” 2018. Available at https://arxiv.org/pdf/1803.08375.pdf.
- D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in Int. Conf. on Learning Representations, 2015.
- S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečnỳ, S. Kumar, and H. B. McMahan, “Adaptive federated optimization,” 2021. Available at https://arxiv.org/pdf/2003.00295.pdf.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.