The Navigation and Terrain Cameras on the Tianwen-1 Mars Rover

Xiao, Liang; Chen, Wangli; Cao, Zhongxiang; Wu, Fenzhi; Lyu, Weizheng; Song, Yuzhi; Li, Danfeng; Yu, Chengwu; Zhang, Lin; Wang, Li

doi:10.1007/s11214-021-00813-y

Cited by 42 publications

(25 citation statements)

References 7 publications

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“…In this part, we consider the model studied in [52] for rover navigation, where the rover refers to the automatic robot for space exploration (e.g., on Lunar and Mars) [53]. Given the map information, the objective function (also called the reward function) of the model can be defined as:…”

Section: H Case Study On Multiple Robot Navigation Problemmentioning

confidence: 99%

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

105

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Abstract-Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge such as the high-quality solution from one task to other tasks to enhance the optimization ability, which however may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this paper proposes a meta-knowledge transfer-based differential evolution (MKTDE) algorithm by using a more general meta-knowledge transfer (MKT) method to solve MTOPs more efficiently. The meta-knowledge defined in this paper refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of "knowledge of knowledge", which denotes the knowledge of "how to solve problem via evolution" and "the feature of evolving high-quality solution". The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multi-source data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is a multiple populations for multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely-used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms.

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Section: H Case Study On Multiple Robot Navigation Problemmentioning

confidence: 99%

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

105

View full text Add to dashboard Cite

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“…The processing procedures of each instrument are given in Table 3. The more detailed information about calibration is published in the overview of each instrument (Tang et al 2020;Yu et al 2020;Kong et al 2020;Liang et al 2021;Meng et al 2021;Fan et al 2021;He et al 2021;Du et al 2020;Peng et al 2020;Zhou et al 2020).…”

Section: Data Calibration (Level 2a Processing)mentioning

confidence: 99%

Design and Validation of the Scientific Data Products for China’s Tianwen-1 Mission

Zhang

et al. 2021

Space Sci Rev

Self Cite

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The Tianwen-1 mission is China’s first Mars exploration mission. To complete the scientific objectives of “orbiting, landing, and patrolling” in one mission, thirteen instruments for acquiring multi-disciplinary data are configured. The data products with rich ancillary data and flexible structures will facilitate the data application. To integrate the requirements of experts from disparate disciplines and leverage new information technologies, a complete redesign of data products is promoted in the Tianwen-1 mission. We discuss the whole process of data product generation from the data pipeline design to data validation. We design the various data products according to the application requirements of each data type, including radar echo data, spectrum data, image data, energy spectrum data, magnetic field data, meteorological data, and acoustic data. Since various error sources will exist in the generation chain of the data products, data validation is essential before release to the public; the validation activities are discussed at the end. We deliver these scientific products to the community in a timely manner, with ancillary information and quality information. This paper can provide practical reference for Tianwen-1 data application.

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“…During the EDL of Tianwen-1, the descent cameras installed at bottom of the lander platform were to capture sequential descent images of the landing area. The NaTeCam [20] of the Zhurong rover also captured a 360° panorama of the landing site scene with a Timely and precise lander localization is critical for the mission operations and scientific exploration [11][12][13][14][15]. It provides essential geo-location information for task planning, such as radio communication and scientific object exploration.…”

Section: Stage Of Parachute Decelerationmentioning

confidence: 99%

“…During the EDL of Tianwen-1, the descent cameras installed at bottom of the lander platform were to capture sequential descent images of the landing area. The NaTeCam [20] of the Zhurong rover also captured a 360 • panorama of the landing site scene with a pitch angle of −30 • on top of the lander after touching down on the Martian surface. However, due to the limitation of the radio transmission time windows and bandwidth, not all image data was downlinked immediately.…”

Section: Stage Of Parachute Decelerationmentioning

confidence: 99%

Visual Localization of the Tianwen-1 Lander Using Orbital, Descent and Rover Images

Wan

et al. 2021

Remote Sensing

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Tianwen-1, China’s first Mars exploration mission, was successfully landed in the southern part of Utopia Planitia on 15 May 2021 (UTC+8). Timely and accurately determining the landing location is critical for the subsequent mission operations. For timely localization, the remote landmarks, selected from the panorama generated by the earliest received Navigation and Terrain Cameras (NaTeCam) images, were matched with the Digital Orthophoto Map (DOM) generated by high resolution imaging camera (HiRIC) images to obtain the initial result based on the triangulation method. Then, the initial localization result was refined by the descent images received later and the NaTeCam DOM. Finally, the lander location was determined to be (25.066°N, 109.925°E). Verified by the new orbital image with the lander and Zhurong rover visible, the localization accuracy was within a pixel of the HiRIC DOM.

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The Navigation and Terrain Cameras on the Tianwen-1 Mars Rover

Cited by 42 publications

References 7 publications

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Design and Validation of the Scientific Data Products for China’s Tianwen-1 Mission

Visual Localization of the Tianwen-1 Lander Using Orbital, Descent and Rover Images

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