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Force Push: Robust Single-Point Pushing with Force Feedback

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arXiv 2024年

作者： Heins, Adam Schoellig, Angela P. The Learning Systems and Robotics Lab the Technical University of Munich Germany The University of Toronto Institute for Aerospace Studies Canada the Vector Institute for Artificial Intelligence Germany

We present a controller for quasistatic robotic planar pushing with single-point contact using only force feedback to sense the pushed object. We consider an omnidirectional mobile robot pushing an object (the "slider") along a given path, where the robot is equipped with a force-torque sensor to measure the force at the contact point with the slider. The geometric, inertial, and frictional parameters of the slider are not known to the controller, nor are measurements of the slider’s pose. We assume that the robot can be localized so that the global position of the contact point is always known and that the approximate initial position of the slider is provided. Simulations and real-world experiments show that our controller yields pushes that are robust to a wide range of slider parameters and state perturbations along both straight and curved paths. Furthermore, we use an admittance controller to adjust the pushing velocity based on the measured force when the slider contacts obstacles like walls. Copyright © 2024, The Authors. All rights reserved.

关键词： Controllers

Robust Nonprehensile Object Transportation with Uncertain Inertial Parameters

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arXiv 2024年

作者： Heins, Adam Schoellig, Angela P. The Learning Systems and Robotics Lab The Technical University of Munich Germany The University of Toronto Institute for Aerospace Studies Canada The Vector Institute for Artificial Intelligence Germany

We consider the nonprehensile object transportation task known as the waiter’s problem—in which a robot must move an object balanced on a tray from one location to another—when the balanced object has uncertain inertial parameters. In contrast to existing approaches that completely ignore uncertainty in the inertia matrix or which only consider small parameter errors, we are interested in pushing the limits of the amount of inertial parameter uncertainty that can be handled. We first show how balancing constraints robust to inertial parameter uncertainty can be incorporated into a motion planning framework to balance objects while moving quickly. Next, we develop necessary conditions for the inertial parameters to be realizable on a bounding shape based on moment relaxations, allowing us to verify whether a trajectory will violate the balancing constraints for any realizable inertial parameters. Finally, we demonstrate our approach on a mobile manipulator in simulations and real hardware experiments: our proposed robust constraints consistently balance a 56 cm tall object with substantial inertial parameter uncertainty in the real world, while the baseline approaches drop the object while transporting it. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles

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arXiv 2024年

作者： Duan, Yufei Achermann, Florian Lim, Jaeyoung Siegwart, Roland Robotics Perception and Learning Lab KTH Royal Institude of Technology Stockholm114 28 Sweden Autonomous Systems Lab ETH Zürich Zürich8092 Switzerland

Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond its operational limits, potentially raising safety concerns. Moreover, wind impacts the energy required to follow a path, especially in locations where the wind direction and speed are not favorable. Incorporating wind information into mission planning is essential to ensure both safety and energy efficiency. In this paper, we propose a sampling-based planner using the kinematic Dubins aircraft paths with respect to the ground, to plan energy-efficient paths in non-uniform wind fields. We study the characteristics of the planner with synthetic and real-world wind data and compare its performance against baseline cost and path formulations. We demonstrate that the energy-optimized planner effectively utilizes updrafts to minimize energy consumption, albeit at the expense of increased travel time. The ground-relative path formulation facilitates the generation of safe trajectories onboard sUAVs within reasonable computational timeframes. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

Balanced resonate-and-fire neurons 24

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Balanced resonate-and-fire neurons

Proceedings of the 41st International Conference on Machine learning

作者： Saya Higuchi Sebastian Kairat Sander M. Bohté Sebastian Otte Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany Machine Learning Group Centrum Wiskunde & Informatica (CWI) Amsterdam The Netherlands

The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.

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ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time

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arXiv 2022年

作者： Grothe, Francesco Hartmann, Valentin N. Orthey, Andreas Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Machine Learning & Robotics Lab University of Stuttgart Germany

We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectional motion planning algorithm, which is asymptotically optimal with respect to the shortest arrival time. We experimentally evaluate ST-RRT* in both abstract (2D disk, 8D disk in cluttered spaces, and on a narrow passage problem), and simulated robotic path planning problems (sequential planning of 8DoF mobile robots, and 7DoF robotic arms). The proposed planner outperforms RRT-Connect and RRT* on both initial solution time, and attained final solution cost. The code for ST-RRT* is available in the Open Motion Planning Library (OMPL). Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space

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AMSwarmX: Safe Swarm Coordination in CompleX Environments vi...

IEEE International Conference on robotics and Automation (ICRA)

作者： Vivek K. Adajania Siqi Zhou Arun Kumar Singh Angela P. Schoellig Learning Systems and Robotics Lab University of Toronto Institute for Aerospace Studies Canada Technical University of Munich Germany Vector Institute for Artificial Intelligence University of Tartu Estonia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Quadrotor motion planning in complex environments leverage the concept of safe flight corridor (SFC) to facilitate static obstacle avoidance. Typically, SFCs are constructed through convex decomposition of the environment’s free space into cuboids, convex polyhedra, or spheres. However, such SFCs can be overly conservative when dealing with a quadrotor swarm, substantially limiting the available free space for quadrotors to coordinate. This paper presents an Alternating Minimization-based approach that does not require building a conservative free-space approximation. Instead, both static and dynamic collision constraints are treated in a unified manner. Dynamic collisions are handled based on shared position trajectories of the quadrotors. Static obstacle avoidance is coupled with distance queries from the Octomap, providing an implicit non-convex decomposition of free space. As a result, our approach is scalable to arbitrary complex environments. Through extensive comparisons in simulation, we demonstrate a 60% improvement in success rate, an average 1.8× reduction in mission completion time, and an average 23× reduction in per-agent computation time compared to SFC-based approaches. We also experimentally validated our approach using a Crazyflie quadrotor swarm of up to 12 quadrotors in obstacle-rich environments. The code, supplementary materials, and videos are released for reference.

关键词： Limiting Codes Buildings Trajectory Planning Collision avoidance robotics and automation

Autonomous Forest Inventory with Legged Robots: System Design and Field Deployment

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arXiv 2024年

作者： Mattamala, Matias Chebrolu, Nived Casseau, Benoit Freißmuth, Leonard Frey, Jonas Tuna, Turcan Hutter, Marco Fallon, Maurice Oxford Robotics Institute The University of Oxford United Kingdom Robotic Systems Lab ETH Zurich Switzerland Technical University of Munich Germany Autonomous Learning Group Max Planck Institute for Intelligent Systems Germany

We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balance of endurance and low soil impact for forest applications. In this paper, we present the complete system architecture of our forest inventory solution which includes state estimation, navigation, mission planning, and real-time tree segmentation and trait estimation. We present preliminary results for three campaigns in forests in Finland and the UK and summarize the main outcomes, lessons, and challenges. Our UK experiment at the Forest of Dean with the ANYmal D legged platform, achieved an autonomous survey of a 0.96 ha plot in 20 min, identifying over 100 trees with typical DBH accuracy of 2 cm. © 2024, CC BY.

关键词： Machine design

Optimized Control Invariance Conditions for Uncertain Input-Constrained Nonlinear Control systems

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arXiv 2023年

作者： Brunke, Lukas Zhou, Siqi Che, Mingxuan Schoellig, Angela P. The Learning Systems and Robotics Lab The Technical University of Munich Germany Germany The University of Toronto Institute for Aerospace Studies The University of Toronto Robotics Institute The Vector Institute for Artificial Intelligence Canada

Providing safety guarantees for learning-based controllers is important for real-world applications. One approach to realizing safety for arbitrary control policies is safety filtering. If necessary, the filter modifies control inputs to ensure that the trajectories of a closed-loop system stay within a given state constraint set for all future time, referred to as the set being positive invariant or the system being safe. Under the assumption of fully known dynamics, safety can be certified using control barrier functions (CBFs). However, the dynamics model is often either unknown or only partially known in practice. learning-based methods have been proposed to approximate the CBF condition for unknown or uncertain systems from data;however, these techniques do not account for input constraints and, as a result, may not yield a valid CBF condition to render the safe set invariant. In this work, we study conditions that guarantee control invariance of the system under input constraints and propose an optimization problem to reduce the conservativeness of CBF-based safety filters. Building on these theoretical insights, we further develop a probabilistic learning approach that allows us to build a safety filter that guarantees safety for uncertain, input-constrained systems with high probability. We demonstrate the efficacy of our proposed approach in simulation and real-world experiments on a quadrotor and show that we can achieve safe closed-loop behavior for a learned system while satisfying state and input constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertain systems

A Data-Driven Method for Estimating Formation Flexibility in Beyond-Visual-Range Air Combat

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A Data-Driven Method for Estimating Formation Flexibility in...

International Conference on Unmanned Aircraft systems (ICUAS)

作者： Edvards Scukins Andre N. Costa Petter Ögren Aeronautical Solutions Division SAAB Aeronautics Robotics Perception and Learning Lab. Royal Institute of Technology (KTH) Decision Support Systems Subdivision Institute for Advanced Studies (IEAv)

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having the Situational Awareness (SA) necessary to evaluate the effects of multiple missile threats can often be challenging. This study provides a new method for calculating an aircraft fleet's maneuver flexibility in a Beyond-Visual-Range (BVR) setting. Sustaining a high degree of flexibility is necessary to adapt to unforeseen circumstances in BVR air combat. To do that, we employ Deep Neural Networks (DNN) to capture the result of a high-performance aircraft model in the presence of adversarial BVR missiles. We then modify our approach to calculate the aircraft's maneuverability concerning an opposing fleet, looking at the advantages and disadvantages of several flight formations. Finally, we consider the anticipated threat from an incoming opponent formation and optimize the counter-formation. This methodology offers a more sophisticated comprehension of aircraft maneuver flexibility within a BVR framework and aids in developing flexible and efficient decision-making techniques for air combat.

关键词： Training Measurement Missiles Visualization Atmospheric modeling Decision making Artificial neural networks