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Reactive Support Polygon Adaptation for the Hybrid Legged-Wheeled CENTAURO Robot

IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 1734-1741页

作者： Kamedula, Malgorzata Tsagarakis, Nikos G. Ist Italiano Technol Humanoids & Human Ctr Mechatron Lab I-16163 Genoa Italy

The need for robots operating in the real-world has sparked interest in hybrid locomotion that combines the versatile legged mobility with a simpler wheeled motion. The use of legged-wheeled robots in complex real-world scenarios requires controllers that will capitalise on this flexibility. In this work, a reactive control scheme that exploits wheels steering and robot articulated legs is proposed to continuously adjust the robot support polygon (SP) in response to unknown disturbances. The designed cartesian-space control is expressed in the joint-space to account for the hardware limits. To tackle the non-holonomy in the joint-space model, the linear velocity/acceleration-based model is developed for the general legged-wheeled platform and applied to resolve the SP adaptation of a platform with steerable wheels. The proposed control is experimentally verified on the CENTAURO robot, demonstrating the SP adjustment when external disturbances are applied.

关键词： Legged robots wheeled robots optimization and optimal control motion control kinematics

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Efficient Constrained Dynamics Algorithms Based on an Equivalent LQR Formulation Using Gauss' Principle of Least Constraint

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 729-749页

作者： Sathya, Ajay Suresha Bruyninckx, Herman Decre, Wilm Pipeleers, Goele Katholieke Univ Leuven Div Robot Automat & Mechatron Dept Mech Engn B-3001 Leuven Belgium Flanders Make DMMS M Lab B-3001 Leuven Belgium TU Eindhoven NL-5600 MB Eindhoven Netherlands Materialise NV B-3001 Leuven Belgium

We derive a family of efficient constrained dynamics algorithms by formulating an equivalent linear quadratic regulator (LQR) problem using Gauss' principle of least constraint and solving it using dynamic programming. Our approach builds upon the pioneering (but largely unknown) O(n + m(2) d + m(3)) solver by Popov and Vereshchagin (PV), where n, m, and d are the number of joints, number of constraints, and the kinematic tree depth, respectively. We provide an expository derivation for the original PV solver and extend it to floating-base kinematic trees with constraints allowed on any link. We make new connections between the LQR's dual Hessian and the inverse operational space inertia matrix (OSIM), permitting efficient OSIM computation, which we further accelerate using matrix inversion lemma. By generalizing the elimination ordering and accounting for MUJOCO-type soft constraints, we derive two original O(n + m) complexity solvers. Our numerical results indicate that significant simulation speed-up can be achieved for high dimensional robots like quadrupeds and humanoids using our algorithms as they scale better than the widely used O(nd(2) + m(2) d + d(2) m) LTL algorithm of Featherstone. The derivation through the LQR-constrained dynamics connection can make our algorithm accessible to a wider audience and enable cross fertilization of software and research results between the fields.

关键词： Direct/Inverse dynamics formulation dynamics optimization and optimal control redundant robots

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ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots

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IEEE TRANSACTIONS ON ROBOTICS 2023年第2期39卷 885-904页

作者： Fahmi, Shamel Barasuol, Victor Esteban, Domingo Villarreal, Octavio Semini, Claudio Semini, Claudio Ist Italiano Tecnol Dynam Legged Syst Lab I-16163 Genoa Italy MIT Biomimet Robot Lab Cambridge MA 02139 USA

This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a Vision-Based Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative to given footholds, but the body pose that maximizes the chances of the legs in reaching safe footholds. ViTAL plans footholds and poses based on skills that characterize the robot's capabilities and its terrain-awareness. We use the 90 kg HyQ and 140 kg HyQReal quadruped robots to validate ViTAL, and show that they are able to climb various obstacles including stairs, gaps, and rough terrains at different speeds and gaits. We compare ViTAL with a baseline strategy that selects the robot pose based on given selected footholds, and show that ViTAL outperforms the baseline.

关键词： Legged robots optimization and optimal control visual learning whole-body motion planning and control

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Visibility-Aware Navigation With Batch Projection Augmented Cross-Entropy Method Over a Learned Occlusion Cost

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9366-9373页

作者： Masnavi, Houman Shrestha, Jatan Mishra, Manav Sujit, P. B. Kruusamae, Karl Singh, Arun Kumar Univ Tartu Inst Technol EE-50411 Tartu Estonia IISER Bhopal Elect Engn & Comp Sci Dept Bhopal 462066 India

We present two real-time trajectory optimizers based on the Cross-Entropy Method for visibility-aware navigation. The two approaches differ in handling inequality constraints stemming from bounds on motion derivatives, collision avoidance, tracking error, etc. Our first optimizer augments the inequalities into the cost function, while the second one relies on a novel GPU accelerated batch projection algorithm. We adopt a learning-based approach to ensure a fast query of the occlusion cost arising from the environment. Specifically, we train a neural network to compute the occlusion directly from the point obstacles generated from LiDAR or RGB-D sensors. Our learned occlusion model can be queried up to 3x faster than the approaches based on distance computation from occupancy or voxel maps. We improve the state-of-the-art in the following aspects. First, our optimizers do not require any explicit map building and can thus adapt on the fly to the changes in the environment. Second, we outperform existing approaches in target tracking applications in maintaining target visibility and success rate while being competitive in acceleration effort and computation time.

关键词： Aerial systems applications machine learning for robot control optimization and optimal control

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Distributed PDOP Coverage control: Providing Large-Scale Positioning Service Using a Multi-Robot System

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 2217-2224页

作者： Zhang, Liang Zhang, Zexu Siegwart, Roland Chung, Jen Jen Harbin Inst Technol Deep Space Explorat & Res Ctr Sch Astronaut Harbin 150001 Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Integrated & Intelligent Nav Xian Shaanxi Peoples R China Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland

This manuscript addresses the active positioning service using a multi-robot system (MRS) for providing large-scale coverage and scalability in terms of MRS size. Inspired by the coverage control problems from Wireless Sensor Network (WSN) literature, we propose a gradient-based control method where a position dilation of precision (PDOP) field is applied to indicate the performance of the positioning service. We derive the gradient of the PDOP field through rigorous derivation, which can be distributively computed by each robot using only the local information from itself and its neighbors. Extensive simulation results show that our proposed method is robust to initial positions, scalable to MRS size, and can extend the coverage area of the positioning service by leveraging the mobility and coordination within the MRS.

关键词： Multi-robot systems optimization and optimal control distributed robot systems PDOP field coverage active positioning service

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Modeling and MPC-Based Pose Tracking for Wheeled Bipedal Robot

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第12期8卷 7881-7888页

作者： Yu, Jianqiao Zhu, Zhangzhen Lu, Junyuan Yin, Sicheng Zhang, Yu Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou 310027 Peoples R China Key Lab Collaborat Sensing & Autonomous Unmanned S Hangzhou 310027 Peoples R China

In this letter, we propose a model predictive control (MPC)-based robot pose controller for our newly designed wheeled bipedal robot (WBR). The proposed controller uses the virtual model control concept, allowing for wider applicability by ignoring the leg dynamics. By directly incorporating the non-holonomic constraint of the wheels into the dynamic equation, a wheeled rigid dynamic model is proposed to maximize the motion flexibility and minimize the model order. A hierarchical MPC control structure is employed to track the desired pose while considering the non-minimal phase property of WBRs in real time. To enhance the autonomy of the robot, we propose a state estimator that utilizes kinematics and inertial sensor data to provide a high-speed and accurate estimation of the robot's state. Both simulation and real-world experiments demonstrate that the proposed method can track a pose trajectory with lower error than traditional feedback control methods. The effectiveness of the estimator is validated through comparison with motion capture cameras and vision-based odometry.

关键词： Robots Legged locomotion Wheels Torso Mathematical models Dynamics Robot kinematics optimization and optimal control dynamics legged robots wheeled robots

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An Efficient Global optimality Certificate for Landmark-Based SLAM

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第3期8卷 1539-1546页

作者： Holmes, Connor Barfoot, Timothy D. Univ Toronto Robot Inst Toronto ON M3H 5T6 Canada

Modern state estimation is often formulated as an optimization problem and solved using efficient local search methods. These methods at best guarantee convergence to local minima, but, in some cases, global optimality can also be certified. Although such global optimality certificates have been well established for 3D pose-graph optimization, the details have yet to be worked out for the 3D landmark-based SLAM problem, in which estimated states include both robot poses and map landmarks. In this letter, we address this gap by using a graph-theoretic approach to cast the subproblems of landmark-based SLAM into a form that yields a sufficient condition for global optimality. Efficient methods of computing the optimality certificates for these subproblems exist, but first require the construction of a large data matrix. We show that this matrix can be constructed with complexity that remains linear in the number of landmarks and does not exceed the state-of-the-art computational complexity of one local solver iteration. We demonstrate the efficacy of the certificate on simulated and real-world landmark-based SLAM problems. We also integrate our method into the state-of-the-art SE-Sync pipeline to efficiently solve landmark-based SLAM problems to global optimality. Finally, we study the robustness of the global optimality certificate to measurement noise, taking into consideration the effect of the underlying measurement graph.

关键词： Simultaneous localization and mapping optimization Noise measurement Robots Three-dimensional displays Costs Cost function SLAM optimization and optimal control certifiable perception global optimality

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Magnetic Actuation With Stationary Electromagnets Considering Power and Temperature Constraints

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第4期5卷 6964-6971页

作者： Pourkand, Ashkan Abbott, Jake J. Univ Utah Sch Comp Salt Lake City UT 84112 USA Univ Utah Robot Ctr Salt Lake City UT 84112 USA Univ Utah Dept Mech Engn Salt Lake City UT 84112 USA

In systems comprising stationary electromagnets designed for magnetic manipulation and remote magnetic actuation, the standard method for achieving some desired system output (e.g., magnetic force and torque) is to form a matrix that maps the electromagnets' electrical currents to the resulting output, and then use the pseudoinverse of that matrix to solve for the currents. However, this method does not account for the nonlinear nature of the saturation limits of the amplifiers and power supply, nor does it account for the temperature in the electromagnets, both of which limit the system's overall performance in practice. In this letter, we propose improved current-solving methods that account for these real-world system limitations. We demonstrate how the performance of systems can be improved by increasing both the achievable output magnitudes and the total operating time to achieve those outputs, and how the risk of overheating can be eliminated.

关键词： Medical robots and systems micro/nano robots optimization and optimal control redundant robots

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Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第3期9卷 2766-2773页

作者： Velasco-Sanchez, Edison Recalde, Luis F. Guevara, Bryan S. Varela-Aldas, Jose Candelas, Francisco A. Puente, Santiago T. Gandolfo, Daniel C. Univ Alicante Grp Automat Robot & Comp Vis AUROVA Alicante 03690 Spain Univ Nacl San Juan Inst Automat INAUT CONICET J-5400 San Juan Argentina Univ Tecnol Indoamer Ctr Invest Ciencias Humanas & Educ CICHE Ambato 180101 Ecuador

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on the capture and detailed analysis of aerial images (photogrammetry). However, the photogrammetry approach presents limitations, such as an increased amount of useless data and potential issues related to image resolution that negatively impact the detection process during high-altitude flights. In this work, we develop a visual servoing control system with dynamic compensation using nonlinear model predictive control (NMPC) applied to a UAV. This system is capable of accurately tracking the middle of the underlying PV array at various frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on extracting features using RGB-D images and employing a Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture.

关键词： Inspection Autonomous aerial vehicles Visual servoing Feature extraction Cameras Visualization Photovoltaic systems Aerial systems: Perception and autonomy optimization and optimal control visual servoing

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Adaptive-Gains Enforcing Constraints in Closed-Loop QP control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第4期5卷 6504-6511页

作者： Djeha, Mohamed Tanguy, Arnaud Kheddar, Abderrahmane Univ Montpellier CNRS LIRMM Interact Digital Humans Grp F-34095 Montpellier France IRL CNRS AIST Joint Robot Lab Tsukuba Ibaraki 3058560 Japan

In this letter, we revisit an open problem of constraints formulation in the context of task-space control frameworks formulated as quadratic programs. In most inverse dynamics implementations, the decision variables are: robot joints acceleration, interaction forces (mostly physical contacts), and robot torques. Nevertheless, many constraints, like distance and velocity bounds, are not written originally in terms of one of these decision variables. Previous work proposed solutions to formulate and enforce joint limits constraints. Yet, none of them worked properly in closed-loop, specifically when bounds are reached or when they are time-varying. First, we show that constraints like collision avoidance, bounds of center of mass, constraints on field-of-view, Cartesian and velocity bounds on a given link... are written in a generic class. Then, we formulate such a class of constraints with gain-parameterized ordinary differential inequality. An adaptive-gain method enforces systematically such class of constraints, and results on a stable behavior when their bounds (even when they vary with time) are reached in closed-loop. Experimental results performed on a humanoid robot validate our solution on a large panel of constraints.

关键词： Task analysis Collision avoidance Acceleration Humanoid robots Jacobian matrices Aerospace electronics Humanoids robots systems motion control optimization and optimal control

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