{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/flappy-hummingbird-an-open-source-dynamic","title":"Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals","arxiv_id":"1902.09628","date":"2019-02-25","proceeding":null,"authors":["Fan Fei","Zhan Tu","Yilun Yang","Jian Zhang","Xinyan Deng"],"abstract":"Insects and hummingbirds exhibit extraordinary flight capabilities and can\nsimultaneously master seemingly conflicting goals: stable hovering and\naggressive maneuvering, unmatched by small scale man-made vehicles. Flapping\nWing Micro Air Vehicles (FWMAVs) hold great promise for closing this\nperformance gap. However, design and control of such systems remain challenging\ndue to various constraints. Here, we present an open source high fidelity\ndynamic simulation for FWMAVs to serve as a testbed for the design,\noptimization and flight control of FWMAVs. For simulation validation, we\nrecreated the hummingbird-scale robot developed in our lab in the simulation.\nSystem identification was performed to obtain the model parameters. The force\ngeneration, open-loop and closed-loop dynamic response between simulated and\nexperimental flights were compared and validated. The unsteady aerodynamics and\nthe highly nonlinear flight dynamics present challenging control problems for\nconventional and learning control algorithms such as Reinforcement Learning.\nThe interface of the simulation is fully compatible with OpenAI Gym\nenvironment. As a benchmark study, we present a linear controller for hovering\nstabilization and a Deep Reinforcement Learning control policy for\ngoal-directed maneuvering. Finally, we demonstrate direct simulation-to-real\ntransfer of both control policies onto the physical robot, further\ndemonstrating the fidelity of the simulation.","url_abs":"http://arxiv.org/abs/1902.09628v1","url_pdf":"http://arxiv.org/pdf/1902.09628v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"flappy-hummingbird-an-open-source-dynamic","repo_url":"https://github.com/purdue-biorobotics/flappy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"openai-gym","task_name":"OpenAI Gym"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}