Presented By: Aerospace Engineering
Chair's Distinguished Lecture: Space Debris Propagation, Prediction, and Removal
Xiaoli Bai, Assistant Professor, Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey
Xiaoli Bai
Assistant Professor
Department of Mechanical and Aerospace Engineering
Rutgers, The State University of New Jersey
Since the launch of the first satellite (Sputnik 1) in 1957, humans have created a lot of objects in orbit around Earth. The number of space objects larger than 10 cm is presently approaching 21,000, the estimated population of objects between 1 and 10cm is about 500, 000, and for objects smaller than 1cm the number exceeds 100 million. Both the number of space objects and the number of conflicts between these objects are increasing exponentially.
This talk overviews the research we have been pursuing on to address the challenges posed by the growth of space debris. We will first introduce the Modified Chebyshev-Picard Iteration (MCPI) Methods, which are a set of parallel-structured methods for solution of initial value problems and boundary value problems. The MCPI methods have been recommended as the “promising and parallelizable method for orbit propagation” by the National Research Council. The talk will then highlight our recent results to develop a methodology to predict RSOs trajectories both higher accuracy and higher reliability than those of the current methods. Inspired by the learning theory through which the models are learnt based on large amounts of data and the prediction can be conducted without explicitly modeling space objects and space environment, we are working on a new orbit prediction framework that integrates physics-based orbit prediction algorithms with a learning process. Last, we will present our research in autonomous, performance-driven, and online trajectory planning and tracking of space robotics for space debris removal with the goal to solve the problem in real time.
About the speaker...
Dr. Xiaoli Bai is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey. She obtained her PhD degree of Aerospace Engineering from Texas A&M University. Prior to joining Rutgers, she was a research scientist at Optimal Synthesis Inc. in Los Altos, California, working with NASA Langley and NASA Ames on advanced research and development projects in the area of air traffic management systems. One consequence of her dissertation is a set of methods which significantly enhances the fundamental processes underlying the maintenance of space debris catalogs. Her current research interests include astrodynamics and Space Situational Awareness; spacecraft guidance, control, and space robotics; and Unmanned Aerial Vehicle navigation and control. Dr. Bai was a recipient of The 2019 NASA Early Career Faculty Award, The 2016 Air Force Office of Scientific Research Young Investigator Research Program Award, Outstanding Young Aerospace Engineer Award from Texas A&M University in 2018, A. Water Tyson Assistant Professor Award from Rutgers in 2018, Amelia Earhart Fellowship, AIAA Foundation John Leland Atwood Graduate Award, and JPL Graduate Fellow. Dr. Bai have published 30 journal articles since she joined Rutgers in July 2014 (for a total of 38 journal papers). Her research has have been funded by NASA, AFOSR, Air Force STTR, and ONR.Bio: Dr. Xiaoli Bai is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey. She obtained her PhD degree of Aerospace Engineering from Texas A&M University. Prior to joining Rutgers, she was a research scientist at Optimal Synthesis Inc. in Los Altos, California, working with NASA Langley and NASA Ames on advanced research and development projects in the area of air traffic management systems. One consequence of her dissertation is a set of methods which significantly enhances the fundamental processes underlying the maintenance of space debris catalogs. Her current research interests include astrodynamics and Space Situational Awareness; spacecraft guidance, control, and space robotics; and Unmanned Aerial Vehicle navigation and control. Dr. Bai was a recipient of The 2019 NASA Early Career Faculty Award, The 2016 Air Force Office of Scientific Research Young Investigator Research Program Award, Outstanding Young Aerospace Engineer Award from Texas A&M University in 2018, A. Water Tyson Assistant Professor Award from Rutgers in 2018, Amelia Earhart Fellowship, AIAA Foundation John Leland Atwood Graduate Award, and JPL Graduate Fellow. Dr. Bai have published 30 journal articles since she joined Rutgers in July 2014 (for a total of 38 journal papers). Her research has have been funded by NASA, AFOSR, Air Force STTR, and ONR.
Assistant Professor
Department of Mechanical and Aerospace Engineering
Rutgers, The State University of New Jersey
Since the launch of the first satellite (Sputnik 1) in 1957, humans have created a lot of objects in orbit around Earth. The number of space objects larger than 10 cm is presently approaching 21,000, the estimated population of objects between 1 and 10cm is about 500, 000, and for objects smaller than 1cm the number exceeds 100 million. Both the number of space objects and the number of conflicts between these objects are increasing exponentially.
This talk overviews the research we have been pursuing on to address the challenges posed by the growth of space debris. We will first introduce the Modified Chebyshev-Picard Iteration (MCPI) Methods, which are a set of parallel-structured methods for solution of initial value problems and boundary value problems. The MCPI methods have been recommended as the “promising and parallelizable method for orbit propagation” by the National Research Council. The talk will then highlight our recent results to develop a methodology to predict RSOs trajectories both higher accuracy and higher reliability than those of the current methods. Inspired by the learning theory through which the models are learnt based on large amounts of data and the prediction can be conducted without explicitly modeling space objects and space environment, we are working on a new orbit prediction framework that integrates physics-based orbit prediction algorithms with a learning process. Last, we will present our research in autonomous, performance-driven, and online trajectory planning and tracking of space robotics for space debris removal with the goal to solve the problem in real time.
About the speaker...
Dr. Xiaoli Bai is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey. She obtained her PhD degree of Aerospace Engineering from Texas A&M University. Prior to joining Rutgers, she was a research scientist at Optimal Synthesis Inc. in Los Altos, California, working with NASA Langley and NASA Ames on advanced research and development projects in the area of air traffic management systems. One consequence of her dissertation is a set of methods which significantly enhances the fundamental processes underlying the maintenance of space debris catalogs. Her current research interests include astrodynamics and Space Situational Awareness; spacecraft guidance, control, and space robotics; and Unmanned Aerial Vehicle navigation and control. Dr. Bai was a recipient of The 2019 NASA Early Career Faculty Award, The 2016 Air Force Office of Scientific Research Young Investigator Research Program Award, Outstanding Young Aerospace Engineer Award from Texas A&M University in 2018, A. Water Tyson Assistant Professor Award from Rutgers in 2018, Amelia Earhart Fellowship, AIAA Foundation John Leland Atwood Graduate Award, and JPL Graduate Fellow. Dr. Bai have published 30 journal articles since she joined Rutgers in July 2014 (for a total of 38 journal papers). Her research has have been funded by NASA, AFOSR, Air Force STTR, and ONR.Bio: Dr. Xiaoli Bai is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey. She obtained her PhD degree of Aerospace Engineering from Texas A&M University. Prior to joining Rutgers, she was a research scientist at Optimal Synthesis Inc. in Los Altos, California, working with NASA Langley and NASA Ames on advanced research and development projects in the area of air traffic management systems. One consequence of her dissertation is a set of methods which significantly enhances the fundamental processes underlying the maintenance of space debris catalogs. Her current research interests include astrodynamics and Space Situational Awareness; spacecraft guidance, control, and space robotics; and Unmanned Aerial Vehicle navigation and control. Dr. Bai was a recipient of The 2019 NASA Early Career Faculty Award, The 2016 Air Force Office of Scientific Research Young Investigator Research Program Award, Outstanding Young Aerospace Engineer Award from Texas A&M University in 2018, A. Water Tyson Assistant Professor Award from Rutgers in 2018, Amelia Earhart Fellowship, AIAA Foundation John Leland Atwood Graduate Award, and JPL Graduate Fellow. Dr. Bai have published 30 journal articles since she joined Rutgers in July 2014 (for a total of 38 journal papers). Her research has have been funded by NASA, AFOSR, Air Force STTR, and ONR.
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