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Trajectory optimization Using Learned Robot-Terrain Interaction Model in Exploration of Large Subterranean Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 3365-3371页

作者： Agishev, Ruslan Petricek, Tomas Zimmermann, Karel Czech Tech Univ Fac Elect Engn Dept Cybernet Prague 16636 Czech Republic

We consider the task of active exploration of large subterranean environments with a ground mobile robot. Our goal is to autonomously explore a large unknown area and to obtain an accurate coverage and localization of objects of interest (artifacts). The exploration is constrained by the restricted operation time in rescue scenarios, as well as a hard rough terrain. To this end, we introduce a novel optimization strategy that respects these constraints by maximizing the environment coverage by onboard sensors while producing feasible trajectories with the help of a learned robot-terrain interaction model. The approach is evaluated in diverse subterranean simulated environments, showing the viability of traversability-aware exploration in challenging scenarios. In addition, we demonstrate that the local trajectory optimization improves global coverage of an environment as well as the overall object detection results.

关键词： Search and rescue robots motion and path planning optimization and optimal control

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COCoMoPL: A Novel Approach for Humanoid Walking Generation Combining optimal control, Movement Primitives and Learning and its Transfer to the Real Robot HRP-2

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IEEE ROBOTICS AND AUTOMATION LETTERS 2017年第2期2卷 977-984页

作者： Clever, Debora Harant, Monika Motnbaur, Kaija Naveau, Maxitnilien Stasse, Olivier Endres, Doininik Heidelberg Univ Interdisciplinary Ctr Sci Comp Optimizat Robot & D-69117 Heidelberg Germany CNRS LAAS F-31077 Toulouse France Philipps Univ Marburg Theoret Neurosci Grp Dept Psychol D-35032 Marburg Germany

COCoMoPL is a recently developed approach combining optimal control, movement primitives and learning for the generation of humanoid walking motions (Clever et al. Robot. Auton. Syst., vol. 83, pp. 287.298, 2016). It solves optimal control problems based on detailed dynamic models of the robot for a variety of walking parameters and uses the solutions as training data to create movement primitives that are very close to feasibility and optimality. These can be employed to synthesize complex walking sequences for humanoid robots online in a very efficient way. We demonstrate, for the first time, that COCoMoPL works on a real humanoid robot, here HRP-2 with 36 DOF and 30 position controlled actuators. To this end, it was necessary to significantly extend the existing approach by including transition steps into the training data, modify the movement primitives (MP) to admit these transitions, improve the representation of the zero moment point MPs and tighten the transition conditions at the beginning and end of steps. We present a thorough validation of the method in simulation and on the real robot for a challenging sequence of movements. We also compare the characteristics of movements after each step of the methodology.

关键词： Humanoid and bipedal locomotion learning and adaptive systems optimization and optimal control

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Scenario-Based Trajectory optimization in Uncertain Dynamic Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 5389-5396页

作者： de Degroot, Oscar Brito, Bruno Ferranti, Laura Gavrila, Dariu Alonso-Mora, Javier Delft Univ Technol Dept Cognit Robot NL-2628 CD Delft Netherlands

We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable for online optimization outright for arbitrary probability distributions. Hence, we sample from these chance constraints using an uncertainty model, to generate "scenarios," which translate the probabilistic constraints into deterministic ones. In practice, each scenario represents the collision constraint for a dynamic obstacle at the location of the sample. The number of theoretically required scenarios can be very large. Nevertheless, by exploiting the geometry of the workspace, we show how to prune most scenarios before optimization and we demonstrate how the reduced scenarios can still provide probabilistic guarantees on the safety of the motion plan. Since our approach is scenario based, we are able to handle arbitrary uncertainty distributions. We apply our method in a Model Predictive Contouring control framework and demonstrate its benefits in simulations and experiments with a moving robot platform navigating among pedestrians, running in real-time.

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

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Integrating Features Acceleration in Visual Predictive control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第4期5卷 5197-5204页

作者： Fusco, Franco Kermorgant, Olivier Martinet, Philippe Cent Nantes Lab Sci Numer Nantes LS2N F-44321 Nantes France Univ Cote Azur INRIA F-06902 Sophia Antipolis France

This letter proposes new prediction models for Visual Predictive control that can lead to both better motions in the feature space and shorter sensor trajectories in 3D. Contrarily to existing first-order models based only on the interaction matrix, it is proposed to integrate acceleration information provided by second-order models. This allows to better estimate the evolution of the image features, and consequently to evaluate control inputs that can properly steer the system to a desired configuration. By means of simulations, the performances of these new predictors are shown and compared to those of a classical model. Included experiments using both image point features and polar coordinates confirm the validity and generality of the approach, showing that the increased complexity of the predictors does not prevent real-time implementations.

关键词： Visual Servoing optimization and optimal control

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Range, Endurance, and optimal Speed Estimates for Multicopters

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2953-2960页

作者： Bauersfeld, Leonard Scaramuzza, Davide Univ Zurich Dept Informat Robot & Percept Grp CH-8050 Zurich Switzerland

Multicopters are among the most versatile mobile robots. Their applications range from inspection and mapping tasks to providing vital reconnaissance in disaster zones and to package delivery. The range, endurance, and speed a multirotor vehicle can achieve while performing its task is a decisive factor not only for vehicle design and mission planning, but also for policy makers deciding on the rules and regulations for aerial robots. To the best of the authors' knowledge, this work proposes the first approach to estimate the range, endurance, and optimal flight speed for a wide variety of multicopters. This advance is made possible by combining a state-of-the-art first-principles aerodynamic multicopter model based on blade-element-momentum theory with an electric-motor model and a graybox battery model. This model predicts the cell voltage with only 1.3% relative error (43.1 mV), even if the battery is subjected to non-constant discharge rates. Our approach is validated with real-world experiments on a test bench as well as with flights at speeds up to 65 km/h in one of the world's largest motion-capture systems. We also present an accurate pen-and-paper algorithm to estimate the range, endurance and optimal speed of multicopters to help future researchers build drones with maximal range and endurance, ensuring that future multirotor vehicles are even more versatile.

关键词： Aerial systems: mechanics and control motion and path planning optimization and optimal control

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Complete Closed-Form and Accurate Solution to Pose Estimation From 3D Correspondences

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第3期8卷 1786-1793页

作者： Malis, Ezio Univ Cote dAzur ACENTAURI team Ctr Inria F-06902 Sophia Antipolis France

Computing the pose from 3D data acquired in two different frames is of high importance for several robotic tasks like odometry, SLAM and place recognition. The pose is generally obtained by solving a least-squares problem given points-to-points, points-to-planes or points to lines correspondences. The non-linear least-squares problem can be solved by iterative optimization or, more efficiently, in closed-form by using solvers of polynomial systems. In this letter, a complete and accurate closed-form solution for a weighted least-squares problem is proposed. Adding weights for each correspondence allow to increase robustness to outliers. Contrary to existing methods, the proposed approach is complete since it is able to solve the problem in any non-degenerate case and it is accurate since it is guaranteed to find the global optimal estimate of the weighted least-squares problem. Simulations and experiments on real data demonstrate the superior accuracy and robustness of the proposed algorithm compared to previous approaches.

关键词： Quaternions Three-dimensional displays Symmetric matrices Simultaneous localization and mapping Iterative methods Cost function Closed-form solutions Localization optimization and optimal control SLAM Autonomous Vehicle Navigation

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Feasibility-Driven Step Timing Adaptation for Robust MPC-Based Gait Generation in Humanoids

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 1582-1589页

作者： Smaldone, Filippo M. Scianca, Nicola Lanari, Leonardo Oriolo, Giuseppe Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest Via Ariosto 25 I-00185 Rome Italy

The feasibility region of a Model Predictive control (MPC) algorithm is the subset of the state space in which the constrained optimization problem to be solved is feasible. In our recent Intrinsically Stable MPC (IS-MPC) method for humanoid gait generation, feasibility means being able to satisfy the dynamic balance condition, the kinematic constraints on footsteps as well as an explicit stability condition. Here, we exploit the feasibility concept to build a step timing adapter that, at each control cycle, modifies the duration of the current step whenever a feasibility loss is imminent due, e.g., to an external perturbation. The proposed approach allows the IS-MPC algorithm to maintain its linearity and adds a negligible computational burden to the overall scheme. Simulations and experimental results where the robot is pushed while walking showcase the performance of the proposed approach.

关键词： Humanoids and bipedal locomotion optimization and optimal control robust/adaptive control

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Temporal Logic Motion Planning With Convex optimization via Graphs of Convex Sets

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IEEE TRANSACTIONS ON ROBOTICS 2023年第5期39卷 3791-3804页

作者： Kurtz, Vince Lin, Hai Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA

Temporal logic is a concise way of specifying complex tasks. However, motion planning to achieve temporal logic specifications is difficult, and existing methods struggle to scale to complex specifications and high-dimensional system dynamics. In this article, we cast linear temporal logic motion planning as a shortest path problem in a graph of convex sets and solve it with convex optimization. This approach brings together the best of modern optimization-based temporal logic planners and older automata-theoretic methods, addressing the limitations of each: we avoid clipping and pass-through by representing paths with continuous Bezier curves;computational complexity is polynomial (not exponential) in the number of sample points;global optimality can be certified (though it is not guaranteed);soundness and probabilistic completeness are guaranteed under mild assumptions;and, most importantly, the method scales to complex specifications and high-dimensional systems, including a 30-degree-of-freedom humanoid.

关键词： Index Terms-Formal methods in robotics and automation linear temporal logic (LTL) motion and path planning optimization and optimal control

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Singularity-Aware Design optimization for Multi-Degree-of-Freedom Spatial Linkages

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第4期6卷 6585-6592页

作者： Maloisel, Guirec Knoop, Espen Thomaszewski, Bernhard Bacher, Moritz Coros, Stelian Disney Res CH-8006 Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci CH-8006 Zurich Switzerland

We introduce a singularity-aware design optimization method for spatial multi-degree-of-freedom mechanical linkages. At the core of our approach is an adversarial sampling strategy, which actively detects singular configurations within the targeted operation range. The detection of singularities in both forward and inverse kinematics allows for two-way bijective mappings between input and output trajectories on our optimized designs, thus enabling robust control. We demonstrate our approach on a set of simulation examples and provide additional validation on physical prototypes.

关键词： Mechanism design kinematics optimization and optimal control

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A Planning-and-control Framework for Aerial Manipulation of Articulated Objects

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 10689-10696页

作者： Brunner, Maximilian Rizzi, Giuseppe Studiger, Matthias Siegwart, Roland Tognon, Marco Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland

While the variety of applications for Aerial Manipulators (AMs) has increased over the last years, they are mostly limited to push-and-slide tasks. More complex manipulations of dynamic environments are poorly addressed and still require handcrafted designs of hardware, control, and trajectory planning. In this letter we focus on the active manipulation of articulated objects with AMs. We present a novel planning and control approach that allows the AM to execute complex interaction maneuvers with as little as possible priors given by the operator. Our framework combines sampling-based predictive control to generate pose trajectories with an impedance controller for compliant behaviours, applied to a fully-actuated flying platform. The framework leverages a physics engine to simulate the dynamics of the platform and the environment in order to find optimal motions to execute manipulation tasks. Experiments on two selected examples of pulling open a door and of turning a valve show the feasibility of the proposed approach.

关键词： Aerial systems Mechanics and control manipulation planning optimization and optimal control

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