REFERENCES
1. Zhang K, Luo J, Xiao W, et al. A subvision system for enhancing the environmental adaptability of the powered transfemoral prosthesis. IEEE Trans Cybern 2020;51:3285-97.
2. Hood S, Gabert L, Lenzi T. Powered knee and ankle prosthesis with adaptive control enables climbing stairs with different stair heights, cadences, and gait patterns. IEEE Trans Robot 2022;38:1430-41.
3. Dong C, Yu Z, Chen X, Chen H, Huang Y, Huang Q. Adaptability control towards complex ground based on fuzzy logic for humanoid robots. IEEE Trans Fuzzy Syst 2022;30:1574-84.
4. Yan Q, Huang J, Tao C, Chen X, Xu W. Intelligent mobile walking-aids: perception, control and safety. Adv Robot 2020;34:2-18.
5. Wang C, Peng L, Hou ZG, Li J, Zhang T, Zhao J. Quantitative assessment of upper-limb motor function for post-stroke rehabilitation based on motor synergy analysis and multi-modality fusion. IEEE Trans Neural Syst Rehab Eng 2020;28:943-52.
6. Ma Z, Zhao J, Yu L, et al. A review of energy supply for biomachine hybrid robots. Cyborg Bionic Syst 2023;4:0053.
7. Zhou M, Yu Q, Huang K, et al. Towards robotic-assisted subretinal injection: a hybrid parallel–serial robot system design and preliminary evaluation. IEEE Trans Ind Electron 2020;67:6617-28.
8. Zhang K, Liu H, Fan Z, et al. Foot placement prediction for assistive walking by fusing sequential 3D gaze and environmental context. IEEE Robot Autom Lett 2021;6:2509-16.
9. Yang B, Huang J, Chen X, Xiong C, Hasegawa Y. Supernumerary robotic limbs: a review and future outlook. IEEE Trans Med Robot Bionics 2021;3:623-39.
10. Chen X, Zhang K, Liu H, Leng Y, Fu C. A probability distribution model-based approach for foot placement prediction in the early swing phase with a wearable IMU sensor. IEEE Trans Neural Syst Rehab Eng 2021;29:2595-604.
11. Chen X, Liu Z, Zhu J, Zhang K, Leng Y, Fu C. Comparison of machine learning regression algorithms for foot placement prediction. In: 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP); 2021 Nov 26-28; Shanghai, China. IEEE; 2021. pp. 169–74.
12. Leng Y, Huang G, Ma L, et al. A lightweight, integrated and portable force-controlled ankle exoskeleton for daily walking assistance. In: 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP); 2021 Nov 26-28; Shanghai, China. IEEE; 2021. pp. 42–7.
13. Leng Y, Lin X, Yang L, Zhang K, Chen X, Fu C. A model for estimating the leg mechanical work required to walk with an elastically suspended backpack. IEEE Trans Human Mach Syst 2022;52:1303-12.
14. Chen C, Zhang K, Leng Y, Chen X, Fu C. Unsupervised sim-to-real adaptation for environmental recognition in assistive walking. IEEE Trans Neural Syst Rehab Eng 2022;30:1350-60.
15. Ma T, Wang Y, Chen X, et al. A piecewise monotonic smooth phase variable for speed-adaptation control of powered knee-ankle prostheses. IEEE Robot Autom Lett 2022;7:8526-33.
16. Chen X, Chen C, Wang Y, et al. A piecewise monotonic gait phase estimation model for controlling a powered transfemoral prosthesis in various locomotion modes. IEEE Robot Autom Lett 2022;7:9549-56.
17. Chen C, Cao Y, Chen X, Wu D, Xiong C, Huang J. A fused deep fuzzy neural network controller and its application to pneumatic flexible joint. IEEE/ASME Trans Mech 2023;28:3214-25.
18. Chen N, Chen X, Chen C, Leng Y, Fu C. Research on the human-following method, fall gesture recognition, and protection method for the walking-aid cane robot. In: 2022 IEEE International Conference on Cyborg and Bionic Systems (CBS); 2023 Mar 24-26; Wuhan, China. IEEE; 2023. pp. 286–91.
19. Wakita K, Huang J, Di P, Sekiyama K, Fukuda T. Human-walking-intention-based motion control of an omnidirectional-type cane robot. IEEE/ASME Trans Mech 2013;18:285-96.
20. Di P, Hasegawa Y, Nakagawa S, et al. Fall detection and prevention control using walking-aid cane robot. IEEE/ASME Trans Mech 2016;21:625-37.
21. Wang E, Chen X, Li Y, Fu Z, Huang J. Lower-limb motion intent recognition based on sensor fusion and fuzzy multi-task learning. IEEE Trans Fuzzy Syst 2024;32:2903-14.
22. Cong Y, Li X, Liu J, Tang Y. A stairway detection algorithm based on vision for UGV stair climbing. In: 2008 IEEE International Conference on Networking, Sensing and Control; 2008 Apr 06-08; Sanya, China. IEEE; 2008. pp. 1806–11.
23. Murakami S, Shimakawa M, Kivota K, Kato T. Study on stairs detection using RGB-depth images. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS); 2014 Dec 03-06; Kitakyushu, Japan. IEEE; 2014. pp. 1186–91.
24. Sriganesh P, Bagree N, Vundurthy B, Travers M. Fast staircase detection and estimation using 3D point clouds with multi-detection merging for heterogeneous robots. In: 2023 IEEE International Conference on Robotics and Automation (ICRA); 2023 May 29 - Jun 02; London, United Kingdom. IEEE; 2023. pp. 9253–59.
25. Wang C, Pei Z, Qiu S, Tang Z. Deep leaning-based ultra-fast stair detection. Sci Rep 2022;12:16124.
26. Zhou QY, Park J, Koltun V. Open3D: a modern library for 3D data processing. arXiv. [Preprint] Jan 30, 2018. [accessed on 2024 May 8]. Available from: http://dx.doi.org/https://doi.org/10.48550/arXiv.1801.09847.
27. Matsumura H, Premachandra C. Deep-learning-based stair detection using 3D point cloud data for preventing walking accidents of the visually impaired. IEEE Access 2022;10:56249-55.
28. Ramanathan M, Luo L, Er JK, et al. Visual environment perception for obstacle detection and crossing of lower-limb exoskeletons. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2022 Oct 23-27; Kyoto, Japan. IEEE; 2022. pp. 12267–74.
29. Liu DX, Xu J, Chen C, Long X, Tao D, Wu X. Vision-assisted autonomous lower-limb exoskeleton robot. IEEE Trans Syst Man Cybern Syst 2021;51:3759-70.
30. Zhang K, Xiong C, Zhang W, et al. Environmental features recognition for lower limb prostheses toward predictive walking. IEEE Trans Neural Syst Rehab Eng 2019;27:465-76.
31. Chen C, Chen X, Yin S, et al. Enhancing prosthetic safety and environmental adaptability: a visual-inertial prosthesis motion estimation approach on uneven terrains. arXiv. [Preprint] Apr 29, 2024. [accessed on 2024 May 8]. Available from: https://doi.org/10.48550/arXiv.2404.18612.
32. Oßwald S, Gutmann JS, Hornung A, Bennewitz M. From 3D point clouds to climbing stairs: a comparison of plane segmentation approaches for humanoids. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots; 2011 Oct 26-28; Bled, Slovenia. IEEE; 2011. pp. 93–8.
33. Pomerleau F, Liu M, Colas F, Siegwart R. Challenging data sets for point cloud registration algorithms. Int J Robot Res 2012;31:1705-11.
34. Maken FA, Ramos F, Ott L. Bayesian iterative closest point for mobile robot localization. Int J Robot Res 2022;41:851-74.
35. Hong Z, Bian S, Xiong P, Li Z. Vision-locomotion coordination control for a powered lower-limb prosthesis using fuzzy-based dynamic movement primitives. IEEE Trans Autom Sci Eng 2024;21:1188-200.
36. Qian Y, Wang Y, Chen C, et al. Predictive locomotion mode recognition and accurate gait phase estimation for hip exoskeleton on various terrains. IEEE Robot Autom Lett 2022;7:6439-46.
37. Patel P, Hare R, Tang Y, Patel N. 3D multi-angle point cloud stitching using iterative closest-point stitching and K-nearest-neighbors. In: 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI); 2022 Nov 18-21; Nanjing, China. IEEE; 2022. pp. 625–30.
38. Chen X, Yu Z, Zhang W, Zheng Y, Huang Q, Ming A. Bioinspired control of walking with toe-off, heel-strike, and disturbance rejection for a biped robot. IEEE Trans Ind Electron 2017;64:7962-71.
39. Zhao J, Lv Y, Zeng Q, Wan L. Online policy learning-based output-feedback optimal control of continuous-time systems. IEEE Trans Circuits Syst Ⅱ Express Briefs 2024;71:652-6.
