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Model Predictive Trajectory optimization With Dynamically Changing Waypoints for Serial Manipulators

IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第7期9卷 6488-6495页

作者： Beck, Florian Vu, Minh Nhat Hartl-Nesic, Christian Kugi, Andreas Tech Univ Wien TUW Automat & Control Inst A-1040 Vienna Austria AIT Austrian Inst Technol GmbH A-1210 Vienna Austria

Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predictive trajectory optimization with short planning horizons. This letter presents a novel waypoint model predictive control (wMPC) concept for online replanning tasks. The main idea is to split the planning horizon at the waypoint when it becomes reachable within the current planning horizon and reduce the horizon length towards the waypoints and goal points. This approach keeps the computational load low and provides flexibility in adapting to changing conditions in real-time. The presented approach achieves competitive path lengths and trajectory durations compared to (global) offline RRT-type planners, VP-STO, and tracking MPC in a multi-waypoint scenario. Moreover, the ability of wMPC to dynamically replan tasks online is experimentally demonstrated on a KUKA LBR iiwa 14 R820 robot in a dynamic pick-and-place scenario.

关键词： Constrained motion planning optimization and optimal control industrial robots model predictive trajectory optimization waypoints

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Learning-Based High-Precision Tracking control: Development, Synthesis, and Verification on Spiral Scanning With a Flexure-Based Nanopositioner

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第5期29卷 3867-3876页

作者： Li, Xiaocong Zhu, Haiyue Ma, Jun Wang, Wenxin Teo, Tat Joo Teo, Chek Sing Lee, Tong Heng ASTAR Adapt Robot & Mechatron Grp Singapore Inst Mfg Technol Singapore 138634 Singapore Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Div Emerging Interdisciplinary Areas Hong Kong Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res Shenzhen Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Home Team Sci & Technol Agcy Robot Automat & Unmanned Syst Ctr Expertise Singapore 329560 Singapore

The traditional methodology utilized in dynamic tracking control synthesis is usually model-based, and therefore, the performance is highly dependent on a precise mathematical model. However, with the growth in system complexity, extremely precise dynamic models for modern robotic and automation systems are very hard to obtain. This challenge has sparked the interest of researchers in moving toward data-driven learning-based concepts, specifically aiming to fully exploit the abundant data available to learn better controls. Along this research direction, continued efforts have been spent on learning a single feedback controller. However, just attempting learning procedures on the feedback controller alone could suffer from severe performance limitations due to its reactive nature, i.e., an error must occur first before any corrective action is taken. In line with this consideration, we propose an integrated two-degree-of-freedom (2-DOF) learning-based tracking control synthesis for high-precision systems, consisting of both feedforward and feedback controllers. Unlike the traditional control design where full knowledge of the dynamics is assumed, we explore the use of actual motion data to iteratively determine the optimal controller parameters using gradient-based optimization. The key step here is to estimate the gradient and Hessian of the cost function without a priori knowledge about the dynamics. Experiments are further conducted based on a prototype flexure-based nanopositioner for spiral scanning applications, demonstrating high-precision nanometer-scale tracking performance that is close to the sensor resolution.

关键词： optimization Robots Feedforward systems Automation Adaptive control Cost function Trajectory tracking Data-driven optimization learning-based control model learning for control nanopositioning optimization and optimal control precision motion control

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Sim-to-Real of Soft Robots With Learned Residual Physics

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第10期9卷 8523-8530页

作者： Gao, Junpeng Michelis, Mike Y. Spielberg, Andrew Katzschmann, Robert K. Swiss Fed Inst Technol Dept Mech & Proc Engn Soft Robot Lab CH-8092 Zurich Switzerland Swiss Fed Inst Technol Soft Robot Lab CH-8092 Zurich Switzerland Swiss Fed Inst Technol ETH AI Ctr CH-8092 Zurich Switzerland Harvard Univ Cambridge MA 02138 USA

Accurately modeling soft robots in simulation is computationally expensive and commonly falls short of representing the real world. This well-known discrepancy, known as the sim-to-real gap, can have several causes, such as coarsely approximated geometry and material models, manufacturing defects, viscoelasticity and plasticity, and hysteresis effects. Residual physics networks learn from real-world data to augment a discrepant model and bring it closer to reality. Here, we present a residual physics method for modeling soft robots with large degrees of freedom. We train neural networks to learn a residual term - the modeling error between simulated and physical systems. Concretely, the residual term is a force applied on the whole simulated mesh, while real position data is collected with only sparse motion markers. The physical prior of the analytical simulation provides a starting point for the residual network, and the combined model is more informed than if physics were learned tabula rasa. We demonstrate our method on 1) a silicone elastomeric beam and 2) a soft pneumatic arm with hard-to-model, anisotropic fiber reinforcements. Our method outperforms traditional system identification up to 60%. We show that residual physics need not be limited to low degrees of freedom but can effectively bridge the sim-to-real gap for high dimensional systems.

关键词： Soft robotics Physics Force Robots Accuracy Pipelines Computational modeling Deep learning methods modeling control and learning for soft robots dynamics optimization and optimal control simulation and animation

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optimization-Based Flocking control and MPC-Based Gait Synchronization control for Multiple Quadruped Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第2期9卷 1929-1936页

作者： Liu, Kaige Dong, Lijing Tan, Xin Zhang, Wentao Zhu, Lijun Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Key Lab Vehicle Adv Mfg Measuring & Control Techno Minist Educ Beijing 100089 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

In this letter, we focus on the flocking control and gait synchronization control of multiple quadruped robots to achieve the movement during patrol tasks. To achieve these goals, we propose an optimization-based distributed flocking controller and a model predictive control (MPC)-based gait synchronization controller. A constrained omnidirectional motion kinematic model is employed to simplify the model of a single quadruped robot. In the flocking controller, position and orientation consistency among the quadruped robots are considered. The constraint is designed to minimize the lateral movement velocity of the quadruped robots. As each robot approaches the destination, it maintains alignment of its orientation with the destination. Additionally, a control barrier function (CBF) is designed to maintain a safe distance between the quadruped robots. Moreover, the proposed MPC-based gait synchronization controller effectively achieves gait synchronization among multiple quadruped robots. Simulation and real world experiments demonstrate the effectiveness of the proposed algorithm.

关键词： Robots Quadrupedal robots Robot kinematics Synchronization Collision avoidance Task analysis Legged locomotion Multi-robot systems optimization and optimal control legged robots gait synchronization control

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Constrained Stein Variational Trajectory optimization

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 3602-3619页

作者： Power, Thomas Berenson, Dmitry Univ Michigan Robot Dept Ann Arbor MI 48109 USA

In this article, we present constrained Stein variational trajectory optimization (CSVTO), an algorithm for performing trajectory optimization with constraints on a set of trajectories in parallel. We frame constrained trajectory optimization as a novel form of constrained functional minimization over trajectory distributions, which avoids treating the constraints as a penalty in the objective and allows us to generate diverse sets of constraint-satisfying trajectories. Our method uses Stein variational gradient descent to find a set of particles that approximates a distribution over low-cost trajectories while obeying constraints. CSVTO is applicable to problems with differentiable equality and inequality constraints and includes a novel particle resampling step to escape local minima. By explicitly generating diverse sets of trajectories, CSVTO is better able to avoid poor local minima and is more robust to initialization. We demonstrate that CSVTO outperforms baselines in challenging highly constrained tasks, such as a 7-DoF wrench manipulation task, where CSVTO outperforms all baselines both in success and constraint satisfaction.

关键词： Trajectory optimization Robots optimization Task analysis Planning Inference algorithms Manifolds Motion and path planning optimization and optimal control probability and statistical methods trajectory optimization

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SOMTP: A Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 9247-9254页

作者： Liu, Yifan Wang, You Li, Guang Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

Model Predictive control (MPC)-based trajectory planning has been widely used in robotics, and incorporating control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfortunately, traditional optimizers are resource-consuming and slow to solve such non-convex constrained optimization problems (COPs) while learning-based methods struggle to satisfy the non-convex constraints. In this letter, we propose SOMTP algorithm, a self-supervised learning-based optimizer for CBF-MPC trajectory planning. Specifically, first, SOMTP employs problem transcription to satisfy most of the constraints. Then the differentiable SLPG correction is proposed to move the solution closer to the safe set and is then converted as the guide policy in the following training process. After that, inspired by the Augmented Lagrangian Method (ALM), our training algorithm integrated with guide policy constraints is proposed to enable the optimizer network to converge to a feasible solution. Finally, experiments show that the proposed algorithm has better feasibility than other learning-based methods and can provide solutions much faster than traditional optimizers with similar optimality.

关键词： Training Robots Trajectory planning Learning systems Trajectory optimization methods Robot kinematics Collision avoidance machine learning for robot control motion and path planning optimization and optimal control

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Fast and Accurate Relative Motion Tracking for Dual Industrial Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 10153-10160页

作者： He, Honglu Lu, Chen-Lung Saunders, Glenn Wason, John Yang, Pinghai Schoonover, Jeffrey Ajdelsztajn, Leo Paternain, Santiago Julius, Agung Wen, John T. Rensselaer Polytech Inst Troy NY 12180 USA Wason Technol Tuxedo Pk NY USA GE Aerosp Niskayuna NY 12309 USA

Industrial robotic applications such as spraying, welding, and additive manufacturing frequently require fast, accurate, and uniform motion along a 3D spatial curve. To increase process throughput, some manufacturers propose a dual-arm setup to overcome the speed limitation of a single robot. Industrial robot motion is programmed through waypoints connected by motion primitives (Cartesian linear and circular paths and linear joint paths at constant Cartesian speed). The actual robot motion is affected by the blending between these motion primitives and the pose of the robot (an outstretched/near-singularity pose tends to have larger path-tracking errors). Choosing the waypoints and the speed along each motion segment to achieve the performance requirement is challenging. At present, there is no automated solution, and laborious manual tuning by robot experts is needed to approach the desired performance. In this letter, we present a systematic three-step approach to designing and programming a dual-arm system to optimize system performance. The first step is to select the relative placement between the two robots based on the specified relative motion path. The second step is to select the relative waypoints and the motion primitives. The final step is to update the waypoints iteratively based on the actual measured relative motion. Waypoint iteration is first executed in simulation and then completed using the actual robots. For performance assessment, we use the mean path speed subject to the relative position and orientation constraints and the path speed uniformity constraint. We have demonstrated the effectiveness of this method on two systems, a physical testbed of two ABB robots and a simulation testbed of two FANUC robots, for two challenging test curves.

关键词： Robots Robot kinematics Service robots Tracking optimization Industrial robots Spraying Motion segmentation Kinematics Welding Motion and path planning optimization and optimal control industrial robots multi-robot systems control architectures and programming

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Emergence of Human Oculomotor Behavior in a Cable-Driven Biomimetic Robotic Eye Using optimal control

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024年第4期16卷 1546-1560页

作者： Alitappeh, Reza Javanmard John, Akhil Dias, Bernardo van Opstal, A. John Bernardino, Alexandre Univ Sci & Technol Mazandaran Behshahr *** Iran Inst Syst & Robot ISR Inst Super Tecn P-1049001 Lisbon Portugal Radboud Univ Nijmegen Donders Ctr Neurosci Sect Neurophys NL-6525 AJ Nijmegen Netherlands

This article explores the application of model-based optimal control principles in understanding stereotyped human oculomotor behaviors. Using a realistic model of the human eye with a six-muscle cable-driven actuation system, we tackle the novel challenges of addressing a system with six degrees of freedom. We apply nonlinear optimal control techniques to optimize accuracy, energy, and duration of eye-movement trajectories. Employing a recurrent neural network to emulate system dynamics, we focus on generating rapid, unconstrained saccadic eye-movements. Remarkably, our model replicates realistic 3-D rotational kinematics and dynamics observed in human saccades, with the six cables organizing themselves into appropriate antagonistic muscle pairs, resembling the primate oculomotor system.

关键词： Robots Muscles Biological system modeling Robot kinematics Three-dimensional displays Kinematics optimal control Biologically inspired robots cable-driven robot cable pretension Listing's law oculomotor system optimization and optimal control recurrent neural network saccadic eye movements

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Distributed Sampling-Based Model Predictive control via Belief Propagation for Multi-Robot Formation Navigation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第4期9卷 3467-3474页

作者： Jiang, Chao Univ Wyoming Dept Elect Engn & Comp Sci Laramie WY 82071 USA

Sampling-based stochastic optimal control has become an appealing robotic control framework due to its ability to handle complex and general forms of dynamics models and task specifications. Although sampling-based methods have been shown successful in a variety of single-robot control tasks, studies on their extension to multi-robot problems are limited. In this letter, we propose a distributed framework for sampling-based optimal control. The framework formulates multi-robot optimal control as probabilistic inference over graphical models and leverages belief propagation to achieve inference via distributed computation. We developed a distributed sampling-based model predictive control (MPC) algorithm based on the proposed framework, which obtains optimal controls via variational inference. The algorithm was validated in a multi-robot formation navigation problem. The simulation results show the efficacy of our proposed method with improved control performance over a gradient-based distributed MPC algorithm.

关键词： Robots Robot kinematics optimal control Trajectory Navigation Collision avoidance Belief propagation Distributed robot systems model predictive control variational inference optimization and optimal control

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Robust Dynamic Walking for Humanoid Robots via Computationally Efficient Footstep Planner and Whole-Body control

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2025年第2期111卷 1-21页

作者： Wang, Xiangji Guo, Wei Zhang, Teng Zheng, Weicong He, Zhicheng Zha, Fusheng Wang, Pengfei Sun, Lining Harbin Inst Technol HIT State Key Lab Robot & Syst Harbin 150001 Heilongjiang Peoples R China Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Optimizat Control & Int Harbin 150080 Heilongjiang Peoples R China Harbin Inst Technol HIT Fac Comp Harbin 150001 Heilongjiang Peoples R China

The robust dynamic walking control of humanoid robots is the foundation for their application in complex scenarios. Model Predictive control (MPC) can predict the robot's motion state over a future time horizon to obtain optimal control inputs and has achieved significant success in walking control of legged robots. However, the high computational cost makes it challenging to ensure real-time performance when dealing with complex dynamic systems. A novel dynamic walking control framework for humanoid robots is introduced in this paper by integrating an efficient footstep planner and Task Space Inverse Dynamics based Whole Body controller (TSID-WBC). This framework aims to enhance the dynamic performance and robustness of the humanoid robot's walking control. Firstly, design a bionic step time generator based on relevant research in biology. Then, the MPC controller, combined with the differential equation solution of the Linear Inverted Pendulum (LIP) dynamics model, is used to predict the optimal footsteps for the humanoid robot. Finally, TSID-WBC is employed to optimize joint torques, joint accelerations, and generalized contact forces based on predefined task priorities. The proposed framework avoids using MPC for predictive control of complex dynamic models. Instead, it innovatively integrates the bionic step time generator proposed in this paper with an MPC based on the differential solution of the LIP dynamics model, resulting in an efficient footstep planner. The simulation and experimental results of the humanoid robot Kuavo demonstrate that the proposed control framework achieves better tracking performance for desired walking speeds and responds quickly to external disturbances.

关键词： Humanoid robots Walking control Step time generator optimization and optimal control

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