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From Unmanned systems to autonomous Intelligent systems

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Engineering 2022年第5期8卷 16-19页

作者： Jie Chen Jian Sun Gang Wang Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 200092China Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China

*** Artificial intelligence(AI)is a rapidly growing field of technol-ogy,which“will enliven inert objects,much as electricity did more than a century *** that we formerly electrified will now cognitize”[1].AI advances are constantly pushing the frontier of what machines can *** attention is being placed on AI research,as well as its development and deployment by commer-cial investors,defense strategists,and policy makers[2].

关键词： frontier inert policy

Multimodal Maximum Entropy Dynamic Games

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arXiv 2022年

作者： So, Oswin Stachowicz, Kyle Theodorou, Evangelos A. Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA United States

Environments with multi-agent interactions often result a rich set of modalities of behavior between agents due to the inherent suboptimality of decision making processes when agents settle for satisfactory decisions. However, existing algorithms for solving these dynamic games are strictly unimodal and fail to capture the intricate multimodal behaviors of the agents. In this paper, we propose MMELQGames (Multimodal Maximum-Entropy Linear Quadratic Games), a novel constrained multimodal maximum entropy formulation of the Differential Dynamic Programming algorithm for solving generalized Nash equilibria. By formulating the problem as a certain dynamic game with incomplete and asymmetric information where agents are uncertain about the cost and dynamics of the game itself, the proposed method is able to reason about multiple local generalized Nash equilibria, enforce constraints with the Augmented Lagrangian framework and also perform Bayesian inference on the latent mode from past observations. We assess the efficacy of the proposed algorithm on two illustrative examples: multi-agent collision avoidance and autonomous racing. In particular, we show that only MMELQGames is able to effectively block a rear vehicle when given a speed disadvantage and the rear vehicle can overtake from multiple positions. © 2022, CC BY.

关键词： Dynamic programming

Low Frequency Sampling in Model Predictive Path Integral control

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arXiv 2024年

作者： Vlahov, Bogdan Gibson, Jason Fan, David D. Spieler, Patrick Agha-Mohammadi, Ali-Akbar Theodorou, Evangelos A. Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA30313 United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

Sampling-based model-predictive controllers have become a powerful optimization tool for planning and control problems in various challenging environments. In this paper, we show how the default choice of uncorrelated Gaussian distributions can be improved upon with the use of a colored noise distribution. Our choice of distribution allows for the emphasis on low frequency control signals, which can result in smoother and more exploratory samples. We use this frequency-based sampling distribution with Model Predictive Path Integral (MPPI) in both hardware and simulation experiments to show better or equal performance on systems with various speeds of input response. © 2024, CC BY.

关键词： Predictive control systems

A Multi-step Dynamics Modeling Framework For autonomous Driving In Multiple Environments

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A Multi-step Dynamics Modeling Framework For Autonomous Driv...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jason Gibson Bogdan Vlahov David Fan Patrick Spieler Daniel Pastor Ali-akbar Agha-mohammadi Evangelos A. Theodorou Autonomous Control and Decision Systems Lab Georgia Institute of Technology Atlanta GA USA NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

Modeling dynamics is often the first step to making a vehicle autonomous. While on-road autonomous vehicles have been extensively studied, off-road vehicles pose many challenging modeling problems. An off-road vehicle encounters highly complex and difficult-to-model terrain/vehicle interactions, as well as having complex vehicle dynamics of its own. These complexities can create challenges for effective high-speed control and planning. In this paper, we introduce a framework for multistep dynamics prediction that explicitly handles the accumulation of modeling error and remains scalable for sampling-based controllers. Our method uses a specially-initialized Long Short-Term Memory (LSTM) over a limited time horizon as the learned component in a hybrid model to predict the dynamics of a 4-person seating all-terrain vehicle (Polaris S4 1000 RZR) in two distinct environments. By only having the LSTM predict over a fixed time horizon, we negate the need for long term stability that is often a challenge when training recurrent neural networks. Our framework is flexible as it only requires odometry information for labels. Through extensive experimentation, we show that our method is able to predict millions of possible trajectories in real-time, with a time horizon of five seconds in challenging off road driving scenarios.

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Sampling-Based Optimization for Multi-Agent Model Predictive control

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arXiv 2022年

作者： Wang, Ziyi Saravanos, Augustinos D. Almubarak, Hassan So, Oswin Theodorou, Evangelos A. Autonomous Control and Decision Systems Lab School of Aerospace Engineering Georgia Institute of Technology GA30318 United States

We systematically review the Variational Optimization, Variational Inference and Stochastic Search perspectives on sampling-based dynamic optimization and discuss their connections to state-of-the-art optimizers and Stochastic Optimal control (SOC) theory. A general convergence and sample complexity analysis on the three perspectives is provided through the unifying Stochastic Search perspective. We then extend these frameworks to their distributed versions for multi-agent control by combining them with consensus Alternating Direction Method of Multipliers (ADMM) to decouple the full problem into local neighborhood-level ones that can be solved in parallel. Model Predictive control (MPC) algorithms are then developed based on these frameworks, leading to fully decentralized sampling-based dynamic optimizers. The capabilities of the proposed algorithms framework are demonstrated on multiple complex multi-agent tasks for vehicle and quadcopter systems in simulation. The results compare different distributed sampling-based optimizers and their centralized counterparts using unimodal Gaussian, mixture of Gaussians, and stein variational policies. The scalability of the proposed distributed algorithms is demonstrated on a 196-vehicle scenario where a direct application of centralized sampling-based methods is shown to be prohibitive. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

A Multi-step Dynamics Modeling Framework For autonomous Driving In Multiple Environments

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arXiv 2023年

作者： Gibson, Jason Vlahov, Bogdan Fan, David Spieler, Patrick Pastor, Daniel Agha-Mohammadi, Ali-Akbar Theodorou, Evangelos A. Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA30313 United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

关键词： Off road vehicles

Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

Variational inference MPC using tsallis divergence

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arXiv 2021年

作者： Wang, Ziyi So, Oswin Gibson, Jason Vlahov, Bogdan Gandhi, Manan S. Liu, Guan-Horng Theodorou, Evangelos A. Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA United States

In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal control by using the non-extensive Tsallis divergence. By incorporating the deformed exponential function into the optimality likelihood function, a novel Tsallis Variational Inference-Model Predictive control algorithm is derived, which includes prior works such as Variational Inference-Model Predictive control, Model Predictive Path Integral control, Cross Entropy Method, and Stein Variational Inference Model Predictive control as special cases. The proposed algorithm allows for effective control of the cost/reward transform and is characterized by superior performance in terms of mean and variance reduction of the associated cost. The aforementioned features are supported by a theoretical and numerical analysis on the level of risk sensitivity of the proposed algorithm as well as simulation experiments on 5 different robotic systems with 3 different policy parameterizations. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

Dynamics Modeling using Visual Terrain Features for High-Speed autonomous Off-Road Driving

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arXiv 2024年

作者： Gibson, Jason Alavilli, Anoushka Tevere, Erica Theodorou, Evangelos A. Spieler, Patrick Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA30332 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States

Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, or planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, high-speed and varying terrain can affect parameters such as traction, tire slip, and rolling resistance. To achieve effective planning in such environments, it is crucial to have a dynamics model that can accurately anticipate these conditions. In this work, we present a hybrid model that predicts the changing dynamics induced by the terrain as a function of visual inputs. We leverage a pre-trained visual foundation model (VFM) DINOv2, which provides rich features that encode fine-grained semantic information. To use this dynamics model for planning, we propose an end-to-end training architecture for a projection distance independent feature encoder that compresses the information from the VFM, enabling the creation of a lightweight map of the environment at runtime. We validate our architecture on an extensive dataset (hundreds of kilometers of aggressive off-road driving) collected across multiple locations as part of the DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER) program. https://***/watch?v=dycTXxEosMk © 2024, CC BY.

关键词： Off road vehicles