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Impedance optimization for Uncertain Contact Interactions Through Risk Sensitive optimal control

IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 4766-4773页

作者： Hammoud, Bilal Khadiv, Majid Righetti, Ludovic NYU Tandon Sch Engn Brooklyn NY 11201 USA Max Planck Inst Intelligent Syst D-72076 Tubingen Germany

This letter addresses the problem of computing optimal impedance schedules for legged locomotion tasks involving complex contact interactions. We formulate the problem of impedance regulation as a trade-off between disturbance rejection and measurement uncertainty. We extend a stochastic optimal control algorithm known as Risk Sensitive control to take into account measurement uncertainty and propose a formal way to include such uncertainty for unknown contact locations. The approach can efficiently generate optimal state and control trajectories along with local feedback control gains, i.e. impedance schedules. Extensive simulations demonstrate the capabilities of the approach in generating meaningful stiffness and damping modulation patterns before and after contact interaction. For example, contact forces are reduced during early contacts, damping increases to anticipate a high impact event and tracking is automatically traded-off for increased stability. In particular, we show a significant improvement in performance during jumping and trotting tasks with a simulated quadruped robot.

关键词： optimal control Impedance Robots Legged locomotion Trajectory Task analysis Computational modeling Humanoid and bipedal locomotion legged robots motion control optimization and optimal control

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CurviTrack: Curvilinear Trajectory Tracking for High-Speed Chase of a USV

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 3932-3939页

作者： Gupta, Parakh M. Prochazka, Ondrej Nascimento, Tiago Saska, Martin Czech Tech Univ Dept Cybernet Prague 14900 Czech Republic Univ Fed Paraiba Dept Comp Syst BR-58051900 Joao Pessoa Brazil

Heterogeneous robot teams used in marine environments incur time-and-energy penalties when the marine vehicle has to halt the mission to allow the autonomous aerial vehicle to land for recharging. In this paper, we present a solution for this problem using a novel drag-aware model formulation which is coupled with Model Predictive control (MPC), and therefore, enables tracking and landing during high-speed curvilinear trajectories of an Uncrewed Surface Vehicle (USV) without any communication. Compared to the state-of-the-art, our approach yields 40% decrease in prediction errors, and provides a 3-fold increase in certainty of predictions. Consequently, this leads to a 30% improvement in tracking performance and 40% higher success in landing on a moving USV even during aggressive turns that are unfeasible for conventional marine missions. We test our approach in two different real-world scenarios with marine vessels of two different sizes and further solidify our results through statistical analysis in simulation to demonstrate the robustness of our method.

关键词： Autonomous aerial vehicles Underactuated surface vessels Predictive models Trajectory Visualization Turning Kalman filters Attitude control Trajectory tracking Training Aerial systems: Mechanics and control dynamics MPC multi-robot systems optimization and optimal control UAV

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Adaptive Non-Linear Centroidal MPC With Stability Guarantees for Robust Locomotion of Legged Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2806-2813页

作者： Elobaid, Mohamed Turrisi, Giulio Rapetti, Lorenzo Romualdi, Giulio Dafarra, Stefano Kawakami, Tomohiro Chaki, Tomohiro Yoshiike, Takahide Semini, Claudio Pucci, Daniele Ist Italiano Tecnol IIT Artificial & Mech Intelligence AMI I-16163 Genoa Italy Ist Italiano Tecnol IIT Dynam Legged Syst DLS I-16163 Genoa Italy Honda R&D Frontier Robot Innovat Res Excellence Saitama 3510188 Japan Univ Manchester Machine Learning & Optimisat Manchester M13 9PL England

Nonlinear model predictive locomotion controllers based on the reduced centroidal dynamics are nowadays ubiquitous in legged robots. These schemes, even if they assume an inherent simplification of the robot's dynamics, were shown to endow robots with a step-adjustment capability in reaction to small pushes, and in the case of uncertain parameters - as unknown payloads - they were shown to provide some "practical", albeit limited, robustness. In this work, we provide rigorous certificates of their closed-loop stability via reformulating the online centroidal MPC controller. This is achieved thanks to a systematic procedure inspired by the machinery of adaptive control, together with ideas coming from control Lyapunov Functions. Our reformulation, in addition, provides robustness for a class of unmeasured constant disturbances. To demonstrate the generality of our approach, we validated our formulation on a new generation of humanoid robots - the $\text{56.7 kg}$ ergoCub, as well as on the commercially available $\text{21 kg}$ quadruped robot Aliengo.

关键词： Robots Robustness Trajectory Payloads Legged locomotion Humanoid robots Vectors Robot kinematics Quadrupedal robots Predictive models Legged robots robust/adaptive control optimization and optimal control

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Local Reactive control for Mobile Manipulators With Whole-Body Safety in Complex Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4556-4563页

作者： Zheng, Chunxin Li, Yulin Song, Zhiyuan Bi, Zhihai Zhou, Jinni Zhou, Boyu Ma, Jun Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou 511453 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Coll Future Technol Guangzhou 511453 Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen 518055 Peoples R China

Mobile manipulators typically encounter significant challenges in navigating narrow, cluttered environments due to their high-dimensional state spaces and complex kinematics. While reactive methods excel in dynamic settings, they struggle to efficiently incorporate complex, coupled constraints across the entire state space. In this work, we present a novel local reactive controller that reformulates the time-domain single-step problem into a multi-step optimization problem in the spatial domain, leveraging the propagation of a serial kinematic chain. This transformation facilitates the formulation of customized, decoupled link-specific constraints, which is further solved efficiently with augmented Lagrangian differential dynamic programming (AL-DDP). Our approach naturally absorbs spatial kinematic propagation in the forward pass and processes all link-specific constraints simultaneously during the backward pass, enhancing both constraint management and computational efficiency. Notably, in this framework, we formulate collision avoidance constraints for each link using accurate geometric models with extracted free regions, and this improves the maneuverability of the mobile manipulator in narrow, cluttered spaces. Experimental results showcase significant improvements in safety, efficiency, and task completion rates. These findings underscore the robustness of the proposed method, particularly in narrow, cluttered environments where conventional approaches could falter.

关键词： Collision avoidance Kinematics Manipulators Aerospace electronics Safety Trajectory optimization Shape Robot kinematics Time-domain analysis Planning mobile manipulation optimization and optimal control

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Scenario-based motion planning with bounded probability of collision

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2025年

作者： de Groot, Oscar Ferranti, Laura Gavrila, Dariu M. Alonso-Mora, Javier Delft Univ Technol Dept Cognit Robot Mekelweg 2 NL-2628 CD Delft Netherlands

Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. optimization-based planners typically avoid humans through collision avoidance chance constraints. This allows the planner to optimize performance while guaranteeing probabilistic safety. However, existing real-time methods do not consider the actual probability of collision for the planned trajectory but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Out of sampled realizations of human motion, we identify which cases affect the optimization. This allows us to certify the planned trajectory in real-time. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans.

关键词： Motion and path planning optimization and optimal control collision avoidance probability and statistical methods

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Trajectory Planning and control for Robotic Manipulation of Magnetic Capsules

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4666-4673页

作者： Isitman, Ogulcan Alcan, Gokhan Kyrki, Ville Aalto Univ Sch Elect Engn Dept Elect Engn & Automat Aalto 00076 Finland Tampere Univ Fac Engn & Nat Sci Automat Technol & Mech Engn Tampere 33720 Finland

Robotic magnetic manipulation offers a minimally invasive approach to gastrointestinal examinations through capsule endoscopy. However, controlling such systems using external permanent magnets (EPM) is challenging due to nonlinear magnetic interactions, especially when there are complex navigation requirements such as avoidance of sensitive tissues. In this work, we present a novel trajectory planning and control method incorporating dynamics and navigation requirements, using a single EPM fixed to a robotic arm to manipulate an internal permanent magnet (IPM). Our approach employs a constrained iterative linear quadratic regulator that considers the dynamics of the IPM to generate optimal trajectories for both the EPM and IPM. Extensive simulations and real-world experiments, motivated by capsule endoscopy operations, demonstrate the robustness of the method, showcasing resilience to external disturbances and precise control under varying conditions. The experimental results show that the IPM reaches the goal position with a maximum mean error of 0.18 cm and a standard deviation of 0.21 cm. This work introduces a unified framework for constrained trajectory optimization in magnetic manipulation, directly incorporating both the IPM's dynamics and the EPM's manipulability.

关键词： Medical robots and systems manipulation planning optimization and optimal control Medical robots and systems manipulation planning optimization and optimal control

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SICNav: Safe and Interactive Crowd Navigation Using Model Predictive control and Bilevel optimization

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 801-818页

作者： Samavi, Sepehr Han, James R. Shkurti, Florian Schoellig, Angela P. Univ Toronto Robot Inst Toronto ON M5G 0C6 Canada Vector Inst Artificial Intelligence Toronto ON M5G 0C6 Canada Tech Univ Munich D-80333 Munich Germany Univ Toronto Toronto ON M5S 1A1 Canada Munich Inst Robot & Machine Intelligence MIRMI D-80992 Munich Germany

Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. In this article, we propose safe and interactive crowd navigation (SICNav), a model predictive control (MPC) method that jointly solves for robot motion and predicted crowd motion in closed loop. We model each human in the crowd to be following an optimal reciprocal collision avoidance (ORCA) scheme and embed that model as a constraint in the robot's local planner, resulting in a bilevel nonlinear MPC optimization problem. We use a Karush-Kuhn-Tucker (KKT)-reformulation to cast the bilevel problem as a single level and use a nonlinear solver to optimize. Our MPC method can influence pedestrian motion while explicitly satisfying safety constraints in a single-robot multihuman environment. We analyze the performance of SICNav in two simulation environments and indoor experiments with a real robot to demonstrate safe robot motion that can influence the surrounding humans. We also validate the trajectory forecasting performance of ORCA on a human trajectory dataset.

关键词： Collision avoidance Navigation Robot kinematics optimization Trajectory Pedestrians Safety Vectors Planning Robot motion Autonomous vehicle navigation collision avoidance optimization and optimal control social navigation

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Empirically Compensated Setpoint Tracking for Spherical Robots With Pressurized Soft-Shells

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2136-2143页

作者： Pravecek, Derek J. Oevermann, Micah J. Thomas, Gray C. Ambrose, Robert O. Texas A&M Univ COLLEGE STN TX 77843 USA

Replacing spherical robots' hard shells with soft, pressurized tires has the potential to improve their off-road practicality immensely. This change leverages spherical robots as a simple and rugged solution to problems currently addressed using wheeled or tracked vehicles. Though numerous prototypes have been launched over the last three decades, there has not been a spherical robot that poses a serious contender to tracked and wheeled systems. Most prototypes are built with a hard outer shell for ease of construction and control. Hard outer shells fail to absorb the impacts from uneven terrain. We addressed this issue by constructing a one-of-a-kind spherical robot with a durable pneumatic, soft outer shell. Although a soft-shell is more desirable for locomotion, it introduces complicated, nonlinear shell dynamics that cause a more challenging control problem. This article presents an empirical model of the steady-state torque induced by soft-shell dynamics, developed using system identification and a model based on tire dynamics. We show how this model, which fingerprints the robot's contact dynamics, is incorporated into RoboBall's steering control algorithm to compensate for soft-shell effects, enhancing setpoint tracking and improving control performance.

关键词： Vehicle dynamics Mathematical models Mobile robots Solid modeling Torque Steady-state Pneumatic systems Tires Nonlinear dynamical systems Kinematics Field robots optimization and optimal control contact modeling

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Hybrid Iterative Linear Quadratic Estimation: optimal Estimation for Hybrid Systems

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 3070-3077页

作者： Payne, J. Joe Zhu, James Kong, Nathan J. Johnson, Aaron M. Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15289 USA

In this letter we present Hybrid iterative Linear Quadratic Estimation (HiLQE), an optimization based offline state estimation algorithm for hybrid dynamical systems. We utilize the saltation matrix, a first order approximation of the variational update through an event driven hybrid transition, to calculate gradient information through hybrid events in the backward pass of an iterative linear quadratic optimization over state estimates. This enables accurate computation of the value function approximation at each timestep. Additionally, the forward pass in the iterative algorithm is augmented with hybrid dynamics in the rollout. A reference extension method is used to account for varying impact times when comparing states for the feedback gain in noise calculation. The proposed method is demonstrated on an ASLIP hopper system with position measurements. In comparison to the Salted Kalman Filter (SKF), the algorithm presented here achieves a maximum of 63.55% reduction in estimation error magnitude over all state dimensions near impact events.

关键词： Jacobian matrices Estimation State estimation Robots Kalman filters optimization Accuracy Trajectory Legged locomotion Heuristic algorithms Sensor fusion optimization and optimal control dynamics

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Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2574-2581页

作者： Wei, Peng Peng, Chen Lu, Wenwu Zhu, Yuankai Vougioukas, Stavros Fei, Zhenghao Ge, Zhikang Zhejiang Univ ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 310027 Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310027 Peoples R China Univ Calif Davis Dept Biol & Agr Engn Davis CA 95616 USA Univ Calif Davis Dept Mech & Aerosp Engn Davis CA 95616 USA

Autonomous agricultural vehicles (AAVs), including field robots and autonomous tractors, are becoming essential in modern farming by improving efficiency and reducing labor costs. A critical task in AAV operations is headland turning between crop rows. This task is challenging in orchards with limited headland space, irregular boundaries, operational constraints, and static obstacles. While traditional trajectory planning methods work well in arable farming, they often fail in cluttered orchard environments. This letter presents a novel trajectory planner that enhances the safety and efficiency of AAV headland maneuvers, leveraging advancements in autonomous driving. Our approach includes an efficient front-end algorithm and a high-performance back-end optimization. Applied to vehicles with various implements, it outperforms state-of-the-art methods in both standard and challenging orchard fields. This work bridges agricultural and autonomous driving technologies, facilitating a broader adoption of AAVs in complex orchards.

关键词： Trajectory Turning Trajectory planning Collision avoidance Planning optimization Geometry Crops Autonomous vehicles Vehicle dynamics Agricultural automation motion and path planning collision avoidance optimization and optimal control agricultural autonomous vehicle

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