{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:57:31Z","timestamp":1790888251026,"version":"4.1.0"},"reference-count":42,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot\u2019s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot\u2019s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog\u2019s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains.<\/jats:p>","DOI":"10.3390\/s24227306","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T06:59:13Z","timestamp":1731653953000},"page":"7306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7683-3535","authenticated-orcid":false,"given":"Tianxiang","family":"Chen","sequence":"first","affiliation":[{"name":"Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1421-9770","authenticated-orcid":false,"given":"Yipeng","family":"Huangfu","sequence":"additional","affiliation":[{"name":"Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0239-183X","authenticated-orcid":false,"given":"Sutthiphong","family":"Srigrarom","sequence":"additional","affiliation":[{"name":"Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4710-4598","authenticated-orcid":false,"given":"Boo Cheong","family":"Khoo","sequence":"additional","affiliation":[{"name":"Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"ref_1","unstructured":"Semini, C., Focchi, M., Barasuol, V., Frigerio, M., Mateos, L., Caldwell, D.G., and Buchli, J. (October, January 28). Design and control of the hydraulically-actuated, torque-controlled quadruped robot HyQ2Max. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hutter, M., Gehring, C., Jud, D., Lauber, A., Bellicoso, D.C., Tsounis, V., Hwangbo, J., Bodie, K., Fankhauser, P., and Bloesch, M. (2016, January 9\u201314). ANYmal: A highly mobile and dynamic quadrupedal robot. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7758092"},{"key":"ref_3","unstructured":"Kolter, J.Z., and Bagnell, J.A. (2012, January 14\u201318). A complete control framework for quadrupedal locomotion over rough terrain. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN, USA."},{"key":"ref_4","first-page":"3680","article-title":"Trajectory optimization for legged robots with unscented dynamic programming","volume":"4","author":"Kuwata","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bledt, G., Powell, M.J., Katz, B., Di Carlo, J., Wensing, P.M., and Kim, S. (2018, January 1\u20135). Cheetah 3: Design and control of a robust, dynamic quadruped robot. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593885"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Di Carlo, J., Katz, B., Bledt, G., and Kim, S. (2018, January 1\u20135). Dynamic locomotion in the MIT Cheetah 3 through convex model-predictive control. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594448"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"eaau5872","DOI":"10.1126\/scirobotics.aau5872","article-title":"Learning agile and dynamic motor skills for legged robots","volume":"4","author":"Hwangbo","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_8","unstructured":"Buchli, J., Stulp, F., Theodorou, E., and Schaal, S. (2009, January 11\u201315). Model predictive control for legged robots. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MI, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1109\/LRA.2018.2794620","article-title":"Dynamic locomotion through online nonlinear motion optimization for quadrupedal robots","volume":"3","author":"Bellicoso","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_10","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. arXiv."},{"key":"ref_11","first-page":"1432","article-title":"Motion planning networks: Bridging the gap between learning-based and classical motion planners","volume":"4","author":"Qureshi","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_12","unstructured":"Thomas, F., Santamaria-Navarro, A., and Andrade-Cetto, J. (2014, January 14\u201318). Toward lifelong adaptive mobile robots. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA."},{"key":"ref_13","first-page":"25","article-title":"Shared autonomy in multi-agent systems for effective task completion in challenging environments","volume":"4","author":"Drew","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1109\/LRA.2018.2798285","article-title":"Gait and trajectory optimization for legged systems through phase-based end-effector parameterization","volume":"3","author":"Winkler","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","first-page":"2261","article-title":"Real-time optimal trajectory generation for quadrupedal locomotion","volume":"3","author":"Yang","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_16","unstructured":"Guo, X., Li, W., Yan, J., and Zeng, Z. (2020, January 25\u201329). Ego-motion estimation and sensor fusion for quadrupedal robot navigation in dynamic environments. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10822","DOI":"10.3182\/20080706-5-KR-1001.01833","article-title":"Bigdog, the rough-terrain quadruped robot","volume":"41","author":"Raibert","year":"2008","journal-title":"IFAC Proc. Vol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1177\/0278364917694244","article-title":"High-speed bounding with the MIT Cheetah 2: Control design and experiments","volume":"36","author":"Park","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_19","first-page":"180","article-title":"Anymal: A highly mobile and autonomous quadrupedal robot","volume":"3","author":"Bellicoso","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_20","first-page":"420","article-title":"Unitree A1: A quadruped robot for industrial and public safety applications","volume":"5","author":"Wang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_21","first-page":"1420","article-title":"DeepRobotics Jueying Lite: A cost-effective quadruped robot for research and consumer applications","volume":"6","author":"Liu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_22","unstructured":"Koenig, S., and Likhachev, M. (August, January 28). D* Lite. Proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, AB, USA."},{"key":"ref_23","first-page":"932","article-title":"D* lite: Incremental path planning in unknown terrain","volume":"172","author":"Koenig","year":"2004","journal-title":"Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/100.580977","article-title":"The dynamic window approach to collision avoidance","volume":"4","author":"Fox","year":"1997","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_25","first-page":"41","article-title":"Trajectory modification considering dynamic constraints of autonomous robots","volume":"74","author":"Rosmann","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TITS.2023.3258186","article-title":"DP-A*: For Path Planning of UGV and Contactless Delivery","volume":"25","author":"Gan","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1109\/LCSYS.2023.3285746","article-title":"Dynamic and Nonlinear Programming for Trajectory Planning","volume":"7","author":"Britzelmeier","year":"2023","journal-title":"IEEE Control Syst. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/LRA.2020.3047728","article-title":"EGO-Planner: An esgocentric optimization-based local planner for quadrotor navigation","volume":"6","author":"Zhou","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11443","DOI":"10.1109\/TIE.2022.3229343","article-title":"Adaptive Switching Control Based on Dynamic Zero-Moment Point for Versatile Hip Exoskeleton Under Hybrid Locomotion","volume":"70","author":"Miao","year":"2023","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_30","unstructured":"Kim, D., Di Carlo, J., Katz, B., Bledt, G., and Kim, S. (2020). Highly dynamic quadruped locomotion via whole-body impulse control and model predictive control. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fink, G., and Semini, C. (2020\u201324, January 24). Proprioceptive Sensor Fusion for Quadruped Robot State Estimation. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341521"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10514-013-9341-4","article-title":"Centroidal dynamics of a humanoid robot","volume":"35","author":"Orin","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1109\/ROBOT.2003.1241826","article-title":"Biped walking pattern generation by using preview control of the zero-moment point","volume":"Volume 2","author":"Kajita","year":"2003","journal-title":"Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA)"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Featherstone, R. (2008). Robot Dynamics Algorithms, Springer.","DOI":"10.1007\/978-1-4899-7560-7"},{"key":"ref_35","unstructured":"Featherstone, R. (2014). Rigid Body Dynamics Algorithms, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/S0005-1098(99)00214-9","article-title":"Constrained model predictive control: Stability and optimality","volume":"36","author":"Mayne","year":"2000","journal-title":"Automatica"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Camacho, E.F., and Bordons, C. (2007). Model Predictive Control, Springer Science & Business Media.","DOI":"10.1007\/978-0-85729-398-5"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/LRA.2019.2947001","article-title":"Full-Body Optimal Control Toward Versatile and Agile Behaviors in a Humanoid Robot","volume":"5","author":"Ishihara","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Adu-Bredu, A., Gibson, G., and Grizzle, J. (2023, January 1\u20135). Exploring Kinodynamic Fabrics for Reactive Whole-Body Control of Underactuated Humanoid Robots. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10342091"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1000711","DOI":"10.1109\/TIM.2022.3229714","article-title":"Automatic extrinsic calibration of dual LiDARs with adaptive surface normal estimation","volume":"72","author":"Nie","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/LRA.2020.3047728","article-title":"EGO-Planner: An ESDF-free gradient-based local planner for quadrotors","volume":"6","author":"Zhou","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"17298814211016609","DOI":"10.1177\/17298814211016609","article-title":"Collision-free and dynamically feasible trajectory planning for omnidirectional mobile robots using a novel B-spline based rapidly exploring random tree","volume":"18","author":"Sun","year":"2021","journal-title":"Int. J. Adv. Robot. 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