Contact detection with multi-information fusion for quadruped robot locomotion under unstructured terrain

Abstract

Reliable foot-to-ground contact state detection is crucial for the locomotion control of quadruped robots in unstructured environments. To improve the reliability and accuracy of contact detection for quadruped robots, a detection approach based on the probabilistic contact model with multi-information fusion is presented to detect the actual contact states of robotic feet with the ground. Moreover, a relevant control strategy to address unexpected early and delayed contacts is planned. The approach combines the internal state information of the robot with the measurements from external sensors mounted on the legs and feet of the prototype. The overall contact states are obtained by the classification of the model-based predicted probabilities. The control strategy for unexpected foot-to-ground contacts can correct the control actions of each leg of the robot to traverse cluttered environments by changing the contact state. The probabilistic model parameters are determined by testing on the single-leg experimental platform. The experiments are conducted on the experimental prototype, and results validate the contact detection and control strategy for unexpected contacts in unstructured terrains during walking and trotting. Compared with the body orientation under the time-based control method regardless of terrain, the root mean square errors of roll, pitch, and yaw respectively decreased by 60.07%, 54.73%, and 64.50% during walking and 73.40%, 61.49%, and 61.48% during trotting.

Graphical abstract

Keywords

multi-information fusion / contact detection / quadruped robot / probabilistic contact model / unstructured terrain

References

[1] He J , Gao F . Mechanism, actuation, perception, and control of highly dynamic multilegged robots: a review. Chinese Journal of Mechanical Engineering, 2020, 33(1): 79

[2] Li X , Zhang S Y , Zhou H T , Feng H B , Fu Y L . Locomotion adaption for hydraulic humanoid wheel-legged robots over rough terrains. International Journal of Humanoid Robotics, 2021, 18(1): 2150001

[3] Chai H , Rong X W , Tang X P , Li Y B . Gait-based quadruped robot planar hopping control with energy planning. International Journal of Advanced Robotic Systems, 2016, 13(1): 20

[4] Zhao Y , Gao F , Sun Q , Yin Y P . Terrain classification and adaptive locomotion for a hexapod robot Qingzhui. Frontiers of Mechanical Engineering, 2021, 16(2): 271–284

[5] Hammoud B , Khadiv M , Righetti L . Impedance optimization for uncertain contact interactions through risk sensitive optimal control. IEEE Robotics and Automation Letters, 2021, 6(3): 4766–4773

[6] Jin Y B , Liu X W , Shao Y C , Wang H T , Yang W . High-speed quadrupedal locomotion by imitation-relaxation reinforcement learning. Nature Machine Intelligence, 2022, 4(12): 1198–1208

[7] AnanthanarayananAFoongSKimS. A compact two DOF magneto-elastomeric force sensor for a running quadruped. In: Proceedings of 2012 IEEE International Conference on Robotics and Automation (ICRA). Saint Paul: IEEE, 2012, 1398–1403

[8] Chuah M Y , Kim S . Enabling force sensing during ground locomotion: a bio-inspired, multi-axis, composite force sensor using discrete pressure mapping. IEEE Sensors Journal, 2014, 14(5): 1693–1703

[9] KäslinRKolvenbachHPaezLLikaKHutterM. Towards a passive adaptive planar foot with ground orientation and contact force sensing for legged robots. In: Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid: IEEE, 2018, 2707–2714

[10] LiX QWangY BWangWYiJ Q. Foot-ground contact force during quadruped locomotion using CPG control. In: Proceedings of 2018 the 37th Chinese Control Conference (CCC). Wuhan: IEEE, 2018, 5233–5238

[11] RuppertFBadri-SpröwitzA. FootTile: a rugged foot sensor for force and center of pressure sensing in soft terrain. In: Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris: IEEE, 2020, 4810–4816

[12] Xu Y T , Wang Z Y , Hao W J , Zhao W Y , Lin W E , Jin B C , Ding N . A flexible multimodal sole sensor for legged robot sensing complex ground information during locomotion. Sensors, 2021, 21(16): 5359

[13] Weerakkodi MudaligeN DNazarovaEBabataevIKopanevPFedoseevACabreraM ATsetserukouD. DogTouch: CNN-based recognition of surface textures by quadruped robot with high density tactile sensors. In: Proceedings of 2022 IEEE the 95th Vehicular Technology Conference (VTC2022-Spring). Helsinki: IEEE, 2022, 1–5

[14] Buchanan R , Bednarek J , Camurri M , Nowicki M R , Walas K , Fallon M . Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification. Autonomous Robots, 2021, 45(6): 843–857

[15] Ba K X , Song Y H , Shi Y P , Wang C Y , Ma G L , Wang Y , Yu B , Yuan L P . A novel one-dimensional force sensor calibration method to improve the contact force solution accuracy for legged robot. Mechanism and Machine Theory, 2022, 169: 104685

[16] Park H W , Ramezani A , Grizzle J W . A finite-state machine for accommodating unexpected large ground-height variations in bipedal robot walking. IEEE Transactions on Robotics, 2013, 29(2): 331–345

[17] HwangboJBellicosoC DFankhauserPHutterM. Probabilistic foot contact estimation by fusing information from dynamics and differential/forward kinematics. In: Proceedings of 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon: IEEE, 2016, 3872–3878

[18] Wensing P M , Wang A , Seok S , Otten D , Lang J , Kim S . Proprioceptive actuator design in the MIT Cheetah: impact mitigation and high-bandwidth physical interaction for dynamic legged robots. IEEE Transactions on Robotics, 2017, 33(3): 509–522

[19] Camurri M , Fallon M , Bazeille S , Radulescu A , Barasuol V , Caldwell D G , Semini C . Probabilistic contact estimation and impact detection for state estimation of quadruped robots. IEEE Robotics and Automation Letters, 2017, 2(2): 1023–1030

[20] BledtGWensingP MIngersollSKimS. Contact model fusion for event-based locomotion in unstructured terrains. In: Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane: IEEE, 2018, 4399–4406

[21] Cong Z , Honglei A , Wu C Y , Lang L , Wei Q , Hongxu M . Contact force estimation method of legged-robot and its application in impedance control. IEEE Access: Practical Innovations, Open Solutions, 2020, 8: 161175–161187

[22] Chatzinikolaidis I , You Y W , Li Z B . Contact-implicit trajectory optimization using an analytically solvable contact model for locomotion on variable ground. IEEE Robotics and Automation Letters, 2020, 5(4): 6357–6364

[23] WolfslagW JMcGreavyCXinG YTiseoCVijayakumarSLiZ B. Optimisation of body-ground contact for augmenting the whole-body loco-manipulation of quadruped robots. In: Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas: IEEE, 2020, 3694–3701

[24] LiX SShenY GLuoCXuQ WChenX LLiC. A probabilistic models fusion based contact detection for quadruped robot. In: Proceedings of 2021 IEEE International Conference on Mechatronics and Automation (ICMA). Takamatsu: IEEE, 2021, 703–708

[25] Liu Q Y , Yuan B , Wang Y . Online learning for foot contact detection of legged robot based on data stream clustering. Frontiers in Bioengineering and Biotechnology, 2022, 9: 771415

[26] Han Y Y , Liu G P , Lu Z Y , Zong H Z , Zhang J H , Zhong F F , Gao L Y . A stability locomotion-control strategy for quadruped robots with center-of-mass dynamic planning. Journal of Zhejiang University−Science A, 2023, 24(6): 516–530

[27] ThrunSBurgardWFoxD. Probabilistic Robotics. Cambridge: MIT Press, 2005, 39–43

[28] Park H W , Wensing P M , Kim S . High-speed bounding with the MIT Cheetah 2: control design and experiments. International Journal of Robotics Research, 2017, 36(2): 167–192

[29] RaibertM H. Legged Robots That Balance. Cambridge: MIT Press, 1986

[30] Di CarloJWensingP MKatzBBledtGKimS. Dynamic locomotion in the MIT Cheetah 3 through convex model-predictive control. In: Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid: IEEE, 2018, 7440–7447

[31] Focchi M , del Prete A , Havoutis I , Featherstone R , Caldwell D G , Semini C . High-slope terrain locomotion for torque-controlled quadruped robots. Autonomous Robots, 2017, 41(1): 259–272

[32] BoaventuraTSeminiCBuchliJFrigerioMFocchiMCaldwellD G. Dynamic torque control of a hydraulic quadruped robot. In: Proceedings of 2012 IEEE International Conference on Robotics and Automation (ICRA). Saint Paul: IEEE, 2012, 1889–1894

[33] CebeOTiseoCXinG YLinH CSmithJMistryM. Online dynamic trajectory optimization and control for a quadruped robot. In: Proceedings of 2021 IEEE International Conference on Robotics and Automation (ICRA). Xi’an: IEEE, 2021, 12773–12779

[34] Lin P C , Komsuoglu H , Koditschek D E . A leg configuration measurement system for full-body pose estimates in a hexapod robot. IEEE Transactions on Robotics, 2005, 21(3): 411–422

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