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Inherently Robust Suboptimal MPC for Autonomous Racing With Anytime Feasible SQP

IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第7期9卷 6616-6623页

作者： Numerow, Logan Zanelli, Andrea Carron, Andrea Zeilinger, Melanie N. ETH Inst Dynam Syst & Control CH-8092 Zurich Switzerland

In this paper, we propose an efficient inexact model predictive control (MPC) strategy for autonomous miniature racing with inherent robustness properties. We rely on a feasible sequential quadratic programming (SQP) algorithm capable of generating feasible intermediate iterates such that the solver can be stopped after any number of iterations, without jeopardizing recursive feasibility. Furthermore, we present the design of suitable terminal ingredients and prove how their combination with the solver ensures constraint satisfaction for any sufficiently small disturbance affecting the system's dynamics. We validate the effectiveness of the proposed strategy in simulation and by deploying it in a physical experiment with autonomous miniature race cars.

关键词： Autonomous agents dynamics optimization and optimal control

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Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 3112-3119页

作者： Rahman, Shatil Waslander, Steven L. Univ Toronto Inst Aerosp Studies Toronto ON M5S Canada Univ Toronto Robot Inst Toronto ON M5S Canada

Most mobile robots follow a modular sense-plan-act system architecture that can lead to poor performance or even catastrophic failure for visual inertial navigation systems due to trajectories devoid of feature matches. Planning in belief space provides a unified approach to tightly couple the perception, planning and control modules, leading to trajectories that are robust to noisy measurements and disturbances. However, existing methods handle uncertainties as costs that require manual tuning for varying environments and hardware. We therefore propose a novel trajectory optimization formulation that incorporates inequality constraints on uncertainty and a novel Augmented Lagrangian based stochastic differential dynamic programming method in belief space. Furthermore, we develop a probabilistic visibility model that accounts for discontinuities due to feature visibility limits. Our simulation tests demonstrate that our method can handle inequality constraints in different environments, for holonomic and nonholonomic motion models with no manual tuning of uncertainty costs involved. We also show the improved optimization performance in belief space due to our visibility model.

关键词： Uncertainty Trajectory Robots Cameras Planning Aerospace electronics Motion measurement Motion and path planning optimization and optimal control vision-based navigation

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Fast Path Planning Through Large Collections of Safe Boxes

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 3795-3811页

作者： Marcucci, Tobia Nobel, Parth Tedrake, Russ Boyd, Stephen MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

We present a fast algorithm for the design of smooth paths (or trajectories) that are constrained to lie in a collection of axis-aligned boxes. We consider the case where the number of these safe boxes is large, and basic preprocessing of them (such as finding their intersections) can be done offline. At runtime, we quickly generate a smooth path between given initial and terminal positions. Our algorithm designs trajectories that are guaranteed to be safe at all times, and detects infeasibility whenever such a trajectory does not exist. Our algorithm is based on two subproblems that we can solve very efficiently: finding a shortest path in a weighted graph, and solving (multiple) convex optimal-control problems. We demonstrate the proposed path planner on large-scale numerical examples, and we provide an efficient open-source software implementation, fastpathplanning.

关键词： Collision avoidance convex optimization motion and path planning optimization and optimal control Collision avoidance convex optimization motion and path planning optimization and optimal control

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Hardware-in-the-Loop Iterative optimal Feedback control Without Model-Based Future Prediction

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IEEE TRANSACTIONS ON ROBOTICS 2019年第6期35卷 1419-1434页

作者： Chen, Yuqing Braun, David J. Singapore Univ Technol & Design Engn Prod Dev Pillar Singapore 487372 Singapore Vanderbilt Univ Dept Mech Engn Nashville TN 37212 USA

optimal control provides a systematic approach to control robots. However, computing optimal controllers for hardware-in-the-loop control is sensitively affected by modeling assumptions, computationally expensive in online implementation, and time-consuming in practical application. This makes the theoretical appeal of optimization challenging to exploit in real-world implementation. In this paper, we present a novel online optimal control formulation that aims to address the above-mentioned limitations. The formulation combines a model with measured state information to efficiently find near-optimal feedback controllers. The idea to combine a model with measurements from the actual motion is similar to what is used in model predictive control formulations, with the difference that here the model is not used for future prediction, the optimization is performed along the measured trajectory of the system, and the online computation is reduced to a minimum;it requires a small-scale, one time step, static optimization, instead of a large-scale, finite time horizon, dynamic optimization. The formulation can be used to solve optimal control problems defined with nonlinear cost, nonlinear dynamics, and box-constrained control inputs. Numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness of the proposed hardware-in-the-loop optimal control approach.

关键词： Predictive models Computational modeling optimization optimal control Robots Trajectory Data models control architectures and programming feedback control hardware-in-the-loop control optimization and optimal control

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Position and Orientation Tunnel-Following NMPC of Robot Manipulators Based on Symbolic Linearization in Sequential Convex Quadratic Programming

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2867-2874页

作者： Astudillo, Alejandro Gillis, Joris Diehl, Moritz Decre, Wilm Pipeleers, Goele Swevers, Jan Katholieke Univ Leuven Dept Mech Engn MECO Res Team B-3001 Leuven Belgium Flanders Make DMMS Lab B-3001 Leuven Belgium Univ Freiburg Dept Microsyst Engn IMTEK D-79110 Baden Baden Germany Univ Freiburg Dept Math D-79110 Baden Baden Germany

The tunnel-following nonlinear model predictive control (NMPC) scheme allows to exploit acceptable deviations around a path reference. This is done by using convex-over-nonlinear functions as objective and constraints in the underlying optimal control problem (OCP). The convex-over-nonlinear structure is exploited by algorithms such as the generalized Gauss-Newton (GGN) method or the sequential convex quadratic programming (SCQP) method to reduce the computational complexity of the OCP solution. However, the modeling effort and engineering time required to implement these methods is high. We address the problem of reducing the modeling effort in the implementation of SCQP, focusing on a standard sequential quadratic programming (SQP) implementation where symbolic linearization is applied to the nonlinear part of the convex-over-nonlinear functions in the objective and constraints. The novelty of this letter is twofold. It introduces a novel operator that applies symbolic linearization in a transparent and easy way to solve nonconvex OCPs with the SCQP method, and introduces a meaningful representation of an orientation-tunnel for robotic applications by means of a convex-over-nonlinear constraint, which preserves the convexity exploitation by the SCQP method. The proposed technique is demonstrated in a tunnel-following task for a 7-degrees-of-freedom manipulator.

关键词： optimization and optimal control motion control tunnel-following sequential convex quadratic programming symbolic linearization

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Simulation Aided Co-Design for Robust Robot optimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 11306-11313页

作者： Fadini, Gabriele Flayols, Thomas Del Prete, Andrea Soueres, Philippe Univ Toulouse LAAS CNRS CNRS Toulouse France Univ Trento Ind Engn Dept I-38123 Trento Italy

This letter outlines a bi-level algorithm to concurrently optimize robot hardware and control parameters in order to minimize energy consumption during the execution of tasks and to ensure robust performance. The outer loop consists in a genetic algorithm that optimizes the co-design variables according to the system average performance when tracking a locally optimal trajectory in perturbed simulations. The tracking controller exploits the locally optimal feedback gains computed in the inner loop with a Differential Dynamic Programming algorithm, which finds the optimal reference trajectories. Our simulations feature a complete actuation model, including friction compensation and bandwidth limits. This strategy can potentially account for arbitrary perturbations, and discards solutions that cannot robustly meet the task requirements. The results show improved performance of the designed platform in realistic application scenarios, autonomously leading to the selection of lightweight and more transparent hardware.

关键词： Mechanism design methods and tools for robot system design optimization and optimal control robust/adaptive control simulation and animation

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Whole-Body MPC for a Dynamically Stable Mobile Manipulator

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第4期4卷 3687-3694页

作者： Minniti, Maria Vittoria Farshidian, Farbod Grandia, Ruben Hutter, Marco Swiss Fed Inst Technol Robot Syst Lab CH-8092 Zurich Switzerland

Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by themanipulator. In this letter, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction, as one optimization problem for an inherently unstable robot. The optimization is performed using a model predictive control (MPC) approach;the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in theMPCplanner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening.

关键词： Mobile manipulation optimization and optimal control

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Thermal Recovery of Multi-Limbed Robots With Electric Actuators

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第2期4卷 1077-1084页

作者： Jorgensen, Steven Jens Holley, James Mathis, Frank Mehling, Joshua S. Sentis, Luis Univ Texas Austin Austin TX 78712 USA NASA Johnson Space Ctr Houston TX 77058 USA

The problem of finding thermally minimizing configurations of a humanoid robot to recover its actuators from unsafe thermal states is addressed. Afirst-order, data-driven, effort-based, thermal model of the robot's actuators is devised, which is used to predict future thermal states. Given this predictive capability, a map between configurations and future temperatures is formulated to find what configurations, subject to valid contact constraints, can be taken now to minimize future thermal states. Effectively, this approach is a realization of a contact-constrained thermal inverse kinematics process. Experimental validation of the proposed approach is performed on the NASA Valkyrie robot hardware.

关键词： Humanoid robots optimization and optimal control failure detection and recovery

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optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 706-713页

作者： Li, Bai Li, Li Acarman, Tankut Shao, Zhijiang Yue, Ming Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Hunan Peoples R China Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China Galatasaray Univ Dept Comp Engn TR-34349 Istanbul Turkey Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Dalian Univ Technol Sch Automot Engn Dalian 116622 Peoples R China

This study is focused on the maneuver planning problem for a tractor-trailer vehicle in a curvy and tiny tunnel. Due to the curse of dimensionality, the prevalent sampling-and- search-based planners used to handle a rigid-body vehicle well become less efficient when the trailer number grows or when the tunnel narrows. This fact also has impacts on an optimization-based planner if it counts on a sampling-and-search-based initial guess to warm-start. We propose an optimization-based maneuver planner that weakly relies on the sampling and search, hoping to get rid of the curse of dimensionality and thus find optima rapidly. The proposed planner comprises three stages: stage I identifies the homotopy class via A(*) search in a 2D grid map;stage 2 recovers the kinematic feasibility with softened intermediate problems iteratively solved;stage 3 finds an optimum that strictly satisfies the nominal collision-avoidance constraints. optimization-based planners are commonly known to run slowly, but this work shows that they have obvious advantages over the prevalent sampling-and-search-based planners when the solution space dimension is high and/or the constraints are harsh.

关键词： optimization and optimal control nonholonomic motion planning tractor-trailer vehicle

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optimal Cooperative Maneuver Planning for Multiple Nonholonomic Robots in a Tiny Environment via Adaptive-Scaling Constrained optimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 1511-1518页

作者： Li, Bai Ouyang, Yakun Zhang, Youmin Acarman, Tankut Kong, Qi Shao, Zhijiang Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada Galatasaray Univ Comp Engn Dept TR-34349 Istanbul Turkey JD Com Amer Technol Corp JDX R&D Ctr Mountain View CA 94043 USA Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

This letter is focused on the time-optimal Multi-Vehicle Trajectory Planning (MVTP) problem for multiple car-like robots when they travel in a tiny indoor scenario occupied by static obstacles. Herein, the complexity of the concerned MVTP task includes i) the non-convexity and narrowness of the environment, ii) the nonholonomy and nonlinearity of the vehicle kinematics, iii) the pursuit for a time-optimal solution, and iv) the absence of predefined homotopic routes for the vehicles. The aforementioned factors, when mixed together, are beyond the capability of the prevalent coupled or decoupled MVTP methods. This work proposes an adaptive-scaling constrained optimization (ASCO) approach, aiming to find the optimum of the nominally intractable MVTP problem in a decoupled way. Concretely, an iterative computation framework is built, wherein each intermediate subproblem contains only risky collision avoidance constraints within a certain range, thus being tractable in the scale. During the iteration, the constraint activation scale can change adaptively, thereby enabling to promote the convergence rate, to recover from an intermediate failure, and to get rid of a poor initial guess. ASCO is compared versus the state-of-the-art MVTP methods and is validated in real experiments conducted by a team of three car-like robots.

关键词： Path planning for multiple mobile robots or agents nonholonomic motion planning optimization and optimal control motion and path planning

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