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第28届中国控制与决策会议

作者： Changqing Wu Rui Xu Shengying Zhu Nan Yang Lina Wang Beijing Institute of Technology Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Ministry of Industry and Information Technology Training center of state grid Shandong electric power company National Key Laboratory of Science and Technology on Aerospace Intelligence Control

ISBN: (纸本)9781467397155

During the deep space exploration missions a lot of attitude maneuvers need to be completed for the change of different ***,in the course ofthe explorer attitude maneuver there are many attitude pointing constraints due to the diversity of space ***,attitude kinematic and dynamic equations based on quaternion are established in this paper,which laid the foundation for design and simulation of the planning ***,to improve system stability and robustness of strong disturbance,an autonomous attitude planning system based sliding mode control has been *** addition,a constraint monitor module ensures that the attitude commands received from the attitude controllers do not violate attitude pointing ***,simulation results demonstrate that:the methods in this paper not only meet complex attitude pointing constraints but also achieve high-precision attitude maneuver in large angle maneuver under high time-varying disturbance torque,and the attitude maneuver path is smoother and feasible for implementation.

关键词： deep space exploration Attitude maneuver Sliding mode control Path planning

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autonomous navigation for small body landing using optical and inter-spacecraft measurements

Autonomous navigation for small body landing using optical a...

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IEEE Aerospace Conference

作者： He Jia Shengying Zhu Pingyuan Cui Institute of Deep Space Exploration Beijing Institute of Technology 5 South Zhongguancun Street Beijing China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Ministry of Industry and Information Technology 5 South Zhongguancun Street Beijing China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education 5 South Zhongguancun Street Beijing China

ISBN: (数字)9781728127347

ISBN: (纸本)9781728127354

In this paper, an optical camera/inter-spacecraft measurement integrated navigation method to autonomously guide a formation of spacecraft for small body landing is proposed and the visual landmark recognition method is presented to improve the navigation performance. By introducing the relative range and line of sight (LOS) angle between spacecraft measured by radiometric and optical sensors, the states of two spacecraft are estimated, and the estimation performance degradation of velocity is reduced, which is caused by using LOS to the feature point on small body surface as the unique navigation information. The camera is used for state estimation of the spacecraft, and the inter-spacecraft measurement is introduced to improve the observability and accuracy of the onboard autonomous navigation system, which provides an effective way to guarantee the sufficient accuracy. The observability of the integrated navigation system has been proven based on the analysis of the observability matrix, which illustrates the improvement of observability of the navigation system. Furthermore, a high-accuracy center extraction method is used for crater landmark extraction to obtain accurate LOS measurement information, and the extraction error is less than 1 pixel. To verify the performance of the proposed navigation method, numerical simulations are set up. Simulation results illustrate that the novel navigation method proposed in this paper improves the observability and estimation accuracy of the navigation system.

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Anti-Windup Trajectory Optimization for High-Mass Mars Entry Vehicles

Anti-Windup Trajectory Optimization for High-Mass Mars Entry...

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Chinese control Conference (CCC)

作者： Hena Gu Shicong Dai Zixuan Liang School of Aerospace Engineering Beijing Institute of Technology Beijing China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration (Beijing Institute of Technology) Ministry of Industry and Information Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China

A trajectory optimization method under complex disturbance is proposed for the future high-mass Mars entry vehicles. First, the dynamic model for the high-mass Mars entry vehicle with a supersonic inflatable aerodynamic decelerator (SIAD) is established. Then, an orthogonal array of three levels with four variables is used to consider the hybrid disturbances of the aerodynamic parameters, and a sensitivity formula is designed to quantify the sensitivity of the bank angle to the hybrid disturbances. With an anti-windup index based on the sensitivity formula and a terminal constraint index together, a comprehensive index is obtained to solve the anti-windup trajectory optimization problem. Finally, the anti-windup trajectory optimization method is simulated and compared with the traditional method. The results show that the proposed method can reduce the sensitivity of aerodynamic disturbance and improve the tracking performance of drag acceleration.

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Flying beacon aided entry navigation for mars orbiter-lander integrated mission

Flying beacon aided entry navigation for mars orbiter-lander...

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AIAA Guidance, navigation, and control Conference, 2016

作者： Qin, T. Zhu, S. Cui, P. School of Aerospace Engineering Beijing Institute of Technology No.5 Zhonguancun South Street Haidian District Beijing100081 China Key laboratory of Autonomous Navigation and Control for Deep Space Exploration Ministry of Industry and Information Technology School of Aerospace Engineering Beijing Institute of Technology No.5 Zhonguancun South Street Haidian District Beijing100081 China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education School of Aerospace Engineering Beijing Institute of Technology No.5 Zhonguancun South Street Haidian District Beijing100081 China

ISBN: (数字)9781624103896

ISBN: (纸本)9781624103896

The Mars orbiter-lander integrated exploration has its attractiveness on better economy and more scientific return, for which the human’s first successful Mars landing exploration, Viking 1, exactly selected this pattern and so will the Chinese planning first Mars mission. This paper proposes an innovative entry navigation concept for the integrated exploration to acquire a precise state estimation which plays a fundamental part in the high-precision soft landing. Radio communication, regarded as the near-future EDL navigation strategy surpassing dead reckoning, is the basic technology of the navigation scheme in which the orbiter works as the navigation reference owing to its precise determined orbit. However, the one orbiter based entry navigation makes no accuracy improvement in some cases blamed on the terrible geometric configuration. Thus, the flying beacon is introduced to ameliorate the geometric configuration instead of providing another radio reference. As for the details of the navigation scheme, the position and velocity of both the lander and the flying beacon are combined into the estimated state space;the relative range and line-of-sight velocity among the three vehicles are measured and inputted into a nonlinear filter to obtain the optimal state estimation. To verify the advancement of the proposed concept, observability degree is exploited to quantify the navigation performance and the Monte Carlo simulation is performed to obtain the statistical estimation accuracy. Analysis indicates that the observability degree is significantly improved and performance sensitivity to the orbiter-lander geometric configuration is greatly reduced. Therefore, all states can be estimated accurately under various orbiter-lander geometric configurations. © 2016 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Monte Carlo methods

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Dual-Level Trajectory Planning for Hypersonic Flight with Multiple No-fly Zones

Dual-Level Trajectory Planning for Hypersonic Flight with Mu...

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Chinese control Conference (CCC)

作者： Yuhuan Ran Ke Yi Zixuan Liang School of Aerospace Engineering Beijing Institute of Technology Beijing China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration (Beijing Institute of Technology) Ministry of Industry and Information Technology Beijing China CSSC System Engineering Research Institute Beijing

A dual-level trajectory planning method is proposed for the trajectory planning of hypersonic vehicles with no-fly zone constraint. To improve the efficiency of the trajectory planning, the method is designed in two levels: the first level is the heading planning, and the second level is the trajectory planning. For the first level, a rapid heading planning method is proposed based on the graphical model of no-fly zones, and the evaluation of the avoidance heading is based on the simplified lateral dynamics equation. The second level introduces the avoidance heading constraint and the fitness with self-seeking, and thus the trajectory planning model can quickly generate a trajectory that satisfies the constraints. Finally, the proposed dual-level planning method is validated by simulations of hypersonic flight with multiple no-fly zones. The results show that the proposed method is capable to generate flight trajectories in high efficiency.

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Hierarchical reinforcement learning based planning method with uncertainty in limited visions for lunar rovers 74

Hierarchical reinforcement learning based planning method wi...

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74th International Astronautical Congress, IAC 2023

作者： Lu, Siyao Xu, Rui Yu, Dengyun Li, Zhaoyu Gao, Ai Wang, Bang Pan, Bo School of Aerospace Engineering Beijing Institute of Technology 5th Zhongguancun South Street Haidian Distinct Beijing100081 China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Beijing Institute of Technology Ministry of Industry and Information Technology Beijing100081 China China Aerospace Science and Technology Corporation 16th Fucheng Road Haidian Distinct Beijing100049 China Beijing Institute of Spacecraft Engineering Beijing Beijing100094 China

China and Russia will establish the International Lunar Research Station together in around 2035 when the resource acquisition from the lunar surface and the construction of lunar bases will be applied at the beginning period. However, the accuracy of the lunar surface digital elevation map (DEM) is not enough, which cannot meet the needs of path planning or acting for lunar rovers. What's more, there are limitations to the vision of each rover compared to the wide moon surface, so rovers are required to foresee the obstacles and adjust movements for obstacle avoidance, power saving, and safety. Another problem is that the acquisition and construction are long-term tasks so pure path planning methods won't work properly. Therefore, we propose a new way of planning both the path and the task by hierarchical reinforcement learning, where hundreds of simulation environments in which the obstacles and places for acquisition, charging, blending, and construction are varied. Rovers can only obtain a vision of several meters and they will only know the approximate locations of targets. So uncertainty occurs during the rovers' way to the targets, on which there are small and large obstacles. Targets will be given by the task level from which the guidance will be applied on the path level. However, data on the task level generated by the hierarchical environment is not enough for training the task policies so pre-generated data will be prepared for the pre-training of the task policies then the policies will be set on the task level with constraints while updating rather than joint training from the beginning. In our experiment, we intend that our way leads each rover to finish the long-term tasks without meeting large obstacles, trains the whole hierarchal policy more quickly than the traditional way, and generates a better result than pure path planning in the uncertainty environment for long-term tasks. Copyright © 2023 by the International Astronautical Federation (IAF).

关键词： hierarchical reinforcement learning lunar rover uncertainty

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A Connectivity-Prediction Algorithm and its Application in Active Cooperative Localization for Multi-Robot Systems

A Connectivity-Prediction Algorithm and its Application in A...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Liang Zhang Zexu Zhang Roland Siegwart Jen Jen Chung Deep Space Exploration and Research Center Harbin Institute of Technology Harbin China Autonomous System Lab ETH Zürich Switzerland Shaanxi Key Laboratory of Integrated and Intelligent Navigation Autonomous Systems Lab ETH Zürich Zürich Switzerland

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

This paper presents a method for predicting the probability of future connectivity between mobile robots with range-limited communication. In particular, we focus on its application to active motion planning for cooperative localization (CL). The probability of connection is modeled by the distribution of quadratic forms in random normal variables and is computed by the infinite power series expansion theorem. A finite-term approximation is made to realize the computational feasibility and three more modifications are designed to handle the adverse impacts introduced by the omission of the higher order series terms. On the basis of this algorithm, an active and CL problem with leader-follower architecture is then reformulated into a Markov Decision Process (MDP) with a one-step planning horizon, and the optimal motion strategy is generated by minimizing the expected cost of the MDP. Extensive simulations and comparisons are presented to show the effectiveness and efficiency of both the proposed prediction algorithm and the MDP model.

关键词： Prediction algorithms Robot sensing systems Planning Uncertainty Computational modeling Gaussian distribution

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autonomous optical navigation for interplanetary exploration based on information of earth-moon

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Journal of Harbin Institute of Technology(New Series) 2003年第3期10卷 343-348页

作者：吴伟仁田玉龙黄翔宇 Institute for Pattern Recognition and Artificial Intelligence State Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Deep Space Exploration Research Center Harbin Institute of Technology Harbin 150001 Chinahe image elements of earth-center and moon-center are obtained by processing the images of earth and moon these image elements in combination with the inertial attitude information and the moon ephemeris are utilized to obtain the probe initial position relative to earth and the Levenberg-Marquardt algorithm is used to determine the accurate probe position relative to earth and the probe orbit relative to earth is estimated by using the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simulation.

The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized to obtain the probe initial position relative to earth, and the Levenberg-Marquardt algorithm is usedto determine the accurate probe position relative to earth, and the probe orbit relative to earth is estimated by u-sing the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simu-lation.

关键词： autonomous optical navigation interplanetary exploration Levenberg-Marquardt algorithm

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Safe reinforcement learning task planning with uncertain duration and resource consumption in limited daytime for lunar rovers

Safe reinforcement learning task planning with uncertain dur...

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2024 IAF space Operations Symposium at the 75th International Astronautical Congress, IAC 2024

作者： Lu, Siyao Xu, Rui Gao, Ai Li, Zhaoyu Liu, Jiamou Zhang, Libo Zhao, Zhijun Zhu, Shengying Li, Yuqiong School of Aerospace Engineering Beijing Institute of Technology 5th Zhongguancun South Street Haidian Distinct Beijing Beijing100081 China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Beijing Institute of Technology Ministry of Industry and Information Technology Beijing100081 China School of Computer Science University of Auckland 34 Princes Street Auckland1010 New Zealand Beijing Institute of Spacecraft Engineering Beijing Beijing100094 China Institute of Mechanics Chinese Academy of Sciences 15th North Fourth Ring West Road Haidian District Beijing100190 China

ISBN: (纸本)9798331312183

The International Lunar Research Station will be established near the south pole through advanced unmanned rovers at the beginning period. The south pole of the moon has short daytime, so the efficiency of remote control is inadequate. However, the duration and power resource usage of the lunar rover moving on the lunar surface remains uncertain because of different loading weight of collection and changes of terrain in moving. What’s more, a lunar rover needs to move back to the base before nighttime without sunlight to provide energy, while the whole time of working on the moon also needs optimization. We select to solve the planning problem with reinforcement learning (RL) due to its capability in tackling uncertainty and optimization. However, traditional reinforcement learning cannot guarantee safety with time uncertainty, resource uncertainty, and constraints due to the soft constraints in optimization. Therefore, we propose a new way through safe reinforcement learning of task planning and resource collection optimization among tasks with uncertain duration and resource collection. We consider a scenario of in-situ material utilization for the lunar base, where there are tasks of moving, charging, collecting, material delivering, and material receiving, all of which have uncertain duration in execution and every task must be done during the daytime except the charging. Resource collection is related to power consumption in moving so it will be decided according to the remaining power. We further propose an architecture on reinforcement learning to let rovers decide the next step instantaneously according to the expected task duration, the remaining time, and the remaining power. Maximizing the amount of material delivered is an optimization target in training while keeping the rovers safe to work only in the daytime without an empty battery. In our experiment, we intend that our way works well in the uncertainties, and it will lead the rover to finish tasks w

关键词： Reinforcement learning

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Probabilistic network topology prediction for active planning: An adaptive algorithm and application

arXiv

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arXiv 2022年

作者： Zhang, Liang Zhang, Zexu Siegwart, Roland Chung, Jen Jen The School of Engineering and Automation Anhui University Hefei230601 China The Deep Space Exploration and Research Center School of Astronautics Harbin Institute of Technology Harbin150001 China Autonomous System Lab ETH Zürich 8092 Switzerland The Shaanxi Key Laboratory of Integrated and Intelligent Navigation China The Autonomous Systems Lab ETH Zürich Zürich8092 Switzerland

This paper tackles the problem of active planning to achieve cooperative localization for multi-robot systems (MRS) under measurement uncertainty in GNSS-limited scenarios. Specifically, we address the issue of accurately predicting the probability of a future connection between two robots equipped with range-based measurement devices. Due to the limited range of the equipped sensors, edges in the network connection topology will be created or destroyed as the robots move with respect to one another. Accurately predicting the future existence of an edge, given imperfect state estimation and noisy actuation, is therefore a challenging task. An adaptive power series expansion (or APSE) algorithm is developed based on current estimates and control candidates. Such an algorithm applies the power series expansion formula of the quadratic positive form in a normal distribution. Finite-term approximation is made to realize the computational tractability. Further analyses are presented to show that the truncation error in the finite-term approximation can be theoretically reduced to a desired threshold by adaptively choosing the summation degree of the power series. Several sufficient conditions are rigorously derived as the selection principles. Finally, extensive simulation results and comparisons, with respect to both single and multi-robot cases, validate that a formally computed and therefore more accurate probability of future topology can help improve the performance of active planning under uncertainty. Copyright © 2022, The Authors. All rights reserved.

关键词： Network topology

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