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IEEE OPEN JOURNAL OF control SYSTEMS 2024年 3卷 143-157页

作者： Rousseas, Panagiotis Bechlioulis, Charalampos Kyriakopoulos, Kostas Natl Tech Univ Athens Sch Mech Engn Control Syst Lab Athens 11144 Greece Univ Patras Sch Elect & Comp Engn Div Syst & Control Patras 26504 Greece New York Univ Ctr AI & Robot CAIR Abu Dhabi 129188 U Arab Emirates

An innovative solution to the optimal motion planning problem is presented in this work. A novel parametrized actor structure is proposed, which guarantees safe and convergent navigation by construction. Concurrently, an efficient scheme for optimizing a mixed state and energy cost function is formulated. The proposed method inherits the positive traits of continuous methods, while at the same time providing sub-optimal -but close to optimal- results significantly faster and in more complex workspaces than previous ones. The scheme is demonstrated to outperform established relevant methods, while at the same time being competitive w.r.t. execution time. Extensive simulations to validate the effectiveness of the method are presented, along with relevant technical proofs for safety and convergence.

关键词： optimal motion planning optimization and optimal control optimal motion planning optimization and optimal control

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Maximising Tolerance to Disturbances via Combined control-Actuation Optimisation for Robust Humanoid Robot Walking

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4348-4355页

作者： Sathuluri, Akhil Sartore, Carlotta Dafarra, Stefano Traversaro, Silvio Zimmermann, Markus Pucci, Daniele Tech Univ Munich TUM Sch Engn & Design Lab Prod Dev & Lightweight Design Robot Syst Grp D-80333 Munich Germany Ctr Robot Technol Inst Italiano Technol Artificial & Mech Intelligence I-16163 Genoa Italy

Combined optimisation of various robot subsystems as a co-design problem has been shown to identify performant robots. However, classical optimisation methods result in point-optimum solutions that may not ensure robust performance and physical feasibility, i.e., the existence of components with specifications matching the computed optimum value. To address this problem, we present a set-based robust co-design optimisation strategy to maximise disturbance tolerance. Instead of identifying a single point-optimum solution, a so-called solution space evaluates the combination of the largest design space that delivers the necessary performance while being robust to the largest set of disturbances. The utility of the proposed approach is demonstrated via a computational design study of the ergoCub robot. This study focuses on the robots' walking performance, illustrating (1) improvement in task success considering at least 3 times larger magnitudes of disturbances, (2) identifying a set instead of a point-solution in the design-disturbances space, and (3) improving standardisation of the joint actuation design.

关键词： Robots optimization Legged locomotion Voltage control Motors Aerospace electronics Torque Gears Collision avoidance Humanoid robots Humanoid robot systems optimization and optimal control methods and tools for robot system design

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Bring the Heat: Rapid Trajectory optimization With Pseudospectral Techniques and the Affine Geometric Heat Flow Equation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 4148-4155页

作者： Adu, Challen Enninful Chuquiure, Cesar E. Ramos Zhang, Bohao Vasudevan, Ram Univ Michigan Robot Dept Ann Arbor MI 48109 USA

Generating optimal trajectories for high-dimensional robotic systems in a time-efficient manner while adhering to constraints is a challenging task. This letter introduces PHLAME, which applies pseudospectral collocation and spatial vector algebra to efficiently solve the Affine Geometric Heat Flow (AGHF) Partial Differential Equation (PDE) for trajectory optimization. Computing a solution to the AGHF PDE scales efficiently because its solution is defined over a two-dimensional domain. To solve the AGHF one usually applies the Method of Lines (MOL), which works by discretizing one variable of the AGHF PDE, effectively converting the PDE into a system of ordinary differential equations (ODEs) that can be solved using standard time-integration methods. Though powerful, this method requires a fine discretization to generate accurate solutions and still requires evaluating the AGHF PDE which can be computationally expensive for high-dimensional systems. PHLAME overcomes this deficiency by using a pseudospectral method, which reduces the number of function evaluations required to obtain a high-accuracy solution. To further increase computational speed, this letter presents analytical expressions for the AGHF which can be computed efficiently using rigid body dynamics algorithms. The proposed method PHLAME is tested across various dynamical systems, with and without obstacles and compared to a number of state-of-the-art techniques. PHLAME is able to generate trajectories for a 44-dimensional state-space system in $\sim 5$ seconds.

关键词： Heuristic algorithms Heating systems Robots Trajectory optimization Vectors Partial differential equations Dynamic programming Planning optimal control Faces optimization and optimal control motion and path planning integrated planning and control

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Learning-Based Nonlinear Model Predictive control of Articulated Soft Robots Using Recurrent Neural Networks

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第12期9卷 11609-11616页

作者： Schaefke, Hendrik Habich, Tim-Lukas Muhmann, Christian Ehlers, Simon F. G. Seel, Thomas Schappler, Moritz Leibniz Univ Hannover Inst Mechatron Syst D-30823 Hannover Germany

Soft robots pose difficulties in terms of control, requiring novel strategies to effectively manipulate their compliant structures. Model-based approaches face challenges due to the high dimensionality and nonlinearities such as hysteresis effects. In contrast, learning-based approaches provide nonlinear models of different soft robots based only on measured data. In this letter, recurrent neural networks (RNNs) predict the behavior of an articulated soft robot (ASR) with five degrees of freedom (DoF). RNNs based on gated recurrent units (GRUs) are compared to the more commonly used long short-term memory (LSTM) networks and show better accuracy. The recurrence enables the capture of hysteresis effects that are inherent in soft robots due to viscoelasticity or friction but cannot be captured by simple feedforward networks. The data-driven model is used within a nonlinear model predictive control (NMPC), whereby the correct handling of the RNN's hidden states is focused. A training approach is presented that allows measured values to be utilized in each control cycle. This enables accurate predictions of short horizons based on sensor data, which is crucial for closed-loop NMPC. The proposed learning-based NMPC enables trajectory tracking with an average error of 1.2 degrees in experiments with the pneumatic five-DoF ASR.

关键词： Soft robotics Robots Long short term memory Training Recurrent neural networks Hysteresis Fuzzy control Actuators Logic gates Accuracy Modeling control and learning for soft robots machine learning for robot control optimization and optimal control

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Spectral Trade-Off for Measurement Sparsification of Pose-Graph SLAM

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第1期9卷 723-730页

作者： Nam, Jiyeon Hyeon, Soojeong Joo, Youngjun Noh, Dongki Shim, Hyungbo Seoul Natl Univ Dept Elect & Comp Engn ASRI Seoul 08826 South Korea Sookmyung Womens Univ Dept Elect Engn Seoul 04310 South Korea Sookmyung Womens Univ Inst Adv Mat & Syst Seoul 04310 South Korea LG Elect Inc Adv Robot Lab CTO Div Seoul 07336 South Korea

In this letter, we propose a trade-off optimization algorithm to compute an appropriate number of edges for measurement (edge) sparsification in pose-graph simultaneous localization and mapping (SLAM). The greater the amount of measurement data, the larger is the computational burden. To reduce computational burden, one can remove a portion of measurements. However, reliable data, such as odometric measurements, can be lost if measurements are removed without any principle. To remove measurements which is redundant, we propose a trade-off optimization algorithm between maximization of the Fiedler value and minimization of the largest eigenvalue of adjacency matrix for measurement graph. This problem formulation gives virtues twofold. First, it is scalable. For any dataset, when a weight for trade-off is given, this algorithm determines the appropriate number of edges since this is a trade-off optimization problem. Second, the edges of the measurement graph can be distributed evenly. The algorithm considers the minimization of the largest eigenvalue of the adjacency matrix, so it suppresses the upper bound of the maximum degree of the measurement graph. It removes the redundant information concentrated on a few nodes, and improves the estimation accuracy of the sparsified graph. To validate the performance of the proposed trade-off optimization algorithm, we apply our approach to CSAIL, Intel, and Manhattan datasets.

关键词： Mapping optimization and optimal control simultaneous localization and mapping (SLAM)

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Distributed Coverage Hole Prevention for Visual Environmental Monitoring With Quadcopters Via Nonsmooth control Barrier Functions

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 1546-1565页

作者： Funada, Riku Santos, Maria Maniwa, Ryuichi Yamauchi, Junya Fujita, Masayuki Sampei, Mitsuji Egerstedt, Magnus Tokyo Inst Technol Dept Syst & Control Engn Tokyo 1528550 Japan Princeton Univ Dept Mech & Aerosp Engn Princeton NJ 08544 USA Univ Tokyo Dept Informat Phys & Comp Tokyo 1138656 Japan Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA

This article proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs). We derive a necessary and sufficient condition for eliminating any unsurveilled area that may arise in between the FOVs among a trio of quadcopters by utilizing a power diagram, i.e., a weighted Voronoi diagram defined by radii of FOVs. Because this condition can be described as logically combined constraints, we leverage nonsmooth control barrier functions (NCBFs) to prevent the appearance of unmonitored areas among a team's FOV. We then investigate the symmetric properties of the proposed NCBFs to develop a distributed algorithm. The proposed algorithm can support the switching of the NCBFs caused by changes of the quadcopters composing trios. The existence of the control input satisfying NCBF conditions is analyzed by employing the characteristics of the power diagram. The proposed framework is synthesized with a coverage control law that maximizes the monitoring quality while reducing overlaps of FOVs. The proposed method is demonstrated in simulation and experiment.

关键词： control barrier functions (CBFs) multirobot systems optimization and optimal control sensor networks

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Engineering Compliance in Legged Robots Via Robust Co-Design

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第6期29卷 4711-4722页

作者： Bravo-Palacios, Gabriel Li, He Wensing, Patrick M. Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA

This article presents a design framework for the scalable co-design of hardware and control as applied to improving the energy efficiency of legged robots with parallel compliance. The proposed framework uses the Alternating Direction Method of Multipliers for design synthesis by solving large-scale trajectory optimization problems. Specifically, we use Stochastic Programming constructs to model design uncertainty associated with terrain properties, and enforce robustness by co-optimizing the robot morphology, a nominal trajectory, and a feedback control policy. Our framework is applied to tune the design of parallel elastic actuation (PEA) via considering how the PEA can be used to actively tailor compliance to different locomotion scenarios. The design optimization framework is validated with the MIT Mini Cheetah quadruped, where added compliance reduces its cost of transport by 58.3% in simulation of optimized planar bounding gaits, and up to 17.4% and 8.3% in experiments when executing trotting and bounding gaits, respectively.

关键词： Legged robots mechanism design optimization and optimal control

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A Hessian for Gaussian Mixture Likelihoods in Nonlinear Least Squares

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第9期9卷 7891-7898页

作者： Korotkine, Vassili Cohen, Mitchell Forbes, James Richard McGill Univ Dept Mech Engn Montreal PQ H3A 0C3 Canada

This letter proposes a novel Hessian approximation for Maximum a Posteriori estimation problems in robotics involving Gaussian mixture likelihoods. Previous approaches manipulate the Gaussian mixture likelihood into a form that allows the problem to be represented as a nonlinear least squares (NLS) problem. The resulting Hessian approximation used within NLS solvers from these approaches neglects certain nonlinearities. The proposed Hessian approximation is derived by setting the Hessians of the Gaussian mixture component errors to zero, which is the same starting point as for the Gauss-Newton Hessian approximation for NLS, and using the chain rule to account for additional nonlinearities. The proposed Hessian approximation results in improved convergence speed and uncertainty characterization for simulated experiments, and similar performance to the state of the art on real-world experiments. A method to maintain compatibility with existing solvers, such as ceres, is also presented.

关键词： optimization Simultaneous localization and mapping optimization methods State estimation Standards Newton method Jacobian matrices Localization optimization and optimal control probabilistic inference sensor fusion SLAM

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Heterogeneous Risk Management Using a Multi-Agent Framework for Supply Chain Disruption Response

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 5126-5133页

作者： Bi, Mingjie Estrada-Garcia, Juan-Alberto Tilbury, Dawn M. Shen, Siqian Barton, Kira Univ Michigan Dept Robot Ann Arbor MI 48109 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48105 USA Dept Robot Ann Arbor MI 48109 USA Dept Mech Engn Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

In the highly complex and stochastic global, supply chain environments, local enterprise agents seek distributed and dynamic strategies for agile responses to disruptions. Existing literature explores both centralized and distributed approaches, while most work neglects temporal dynamics and the heterogeneity of the risk management of individual agents. To address this gap, this letter presents a heterogeneous risk management mechanism to incorporate uncertainties and risk attitudes into agent communication and decision-making strategy. Hence, this approach empowers enterprises to handle disruptions in stochastic environments in a distributed way, and in particular in the context of multi-agent control and management. Through a simulated case study, we showcase the feasibility and effectiveness of the proposed approach under stochastic settings and how the decision of disruption responses changes when agents hold various risk attitudes.

关键词： Agent-based systems manufacturing maintenance and supply chains optimization and optimal control

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optimal Initialization Strategies for Range-Only Trajectory Estimation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第3期9卷 2160-2167页

作者： Goudar, Abhishek Dumbgen, Frederike Barfoot, Timothy D. Schoellig, Angela P. Univ Toronto Inst Aerosp Studies Toronto ON M3H5T6 Canada Univ Toronto Robot Inst Toronto ON M3H5T6 Canada Vector Inst Artificial Intelligence Toronto ON M3H5T6 Canada Tech Univ Munich D-80333 Munich Germany Munich Inst Robot & Machine Intelligence D-80333 Munich Germany

Range-only (RO) pose estimation involves determining a robot's pose over time by measuring the distance between multiple devices on the robot, known as tags, and devices installed in the environment, known as anchors. The non-convex nature of the range measurement model results in a cost function with possible local minima. In the absence of a good initial guess, commonly used iterative solvers can get stuck in these local minima resulting in poor trajectory estimation accuracy. In this letter, we propose convex relaxations to the original non-convex problem based on semidefinite programs (SDPs). Specifically, we formulate computationally tractable SDP relaxations to obtain accurate initial pose and trajectory estimates for RO trajectory estimation under static and dynamic (i.e., constant-velocity motion) conditions. Through simulation and hardware experiments, we demonstrate that our proposed approaches estimate the initial pose and initial trajectories accurately compared to iterative local solvers. Additionally, the proposed relaxations recover global minima under moderate range measurement noise levels.

关键词： Trajectory Robots Estimation Robot sensing systems Sensors Pose estimation Location awareness Localization optimization and optimal control range-only localization semidefinite relaxation

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