In this paper, we propose a new model predictive control (MPC) formulation for autonomous driving. The novelty of our MPC stems from the following results. Firstly, we adopt an alternating minimization approach wherein linear velocities and angular accelerations are alternately optimized. We show that in contrast to the joint optimization, the alternating minimization exploits the structure of the problem better, which in turn translates to reduction in computation time. Secondly, our MPC explicitly incorporates the time dependent non-linear actuator dynamics that captures the transient response of the vehicle for a given commanded velocity. This added complexity improves the predictive component of MPC resulting in improved margin of inter-vehicle distance during maneuvers like overtaking, lane-change, etc. Although, past works have also incorporated actuator dynamics within MPC, there has been very few attempts towards coupling actuator dynamics to collision avoidance constraints through the non-holonomic motion model of the vehicle and analyzing the resulting behavior. We use a high fidelity simulator to benchmark our actuator dynamics augmented MPC with other related approaches in terms of metrics like inter-vehicle distance, trajectory smoothness, and velocity overshoot.
University of Texas at Austin（得克萨斯大学奥斯汀分校）；NASA Johnson Space Center（美国国家航空航天局约翰逊航天中心）
The problem of finding thermally minimizing configurations of a humanoid robot to recover its actuators from unsafe thermal states is addressed. A first-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-kinematic…
We present an information theoretic approach to stochastic optimal control problems that can be used to derive general sampling based optimization schemes. This new mathematical method is used to develop a sampling based model predictive control algorithm. We apply this information theoretic model predictive control (IT-MPC) scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance to a model predictive control version of the cross-entropy method.
We present a framework for vision-based model predictive control (MPC) for the task of aggressive, high-speed autonomous driving. Our approach uses deep convolutional neural networks to predict cost functions from input video which are directly suitable for online trajectory optimization with MPC. We demonstrate the method in a high speed autonomous driving scenario, where we use a single monocular camera and a deep convolutional neural network to predict a cost map of the track in front of the vehicle. Results are demonstrated on a 1:5 scale autonomous vehicle given the task of high speed, ag…
University of Twente（特文特大学）；LAAS-CNRS（系统分析与架构实验室）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）；University of Padova（帕多瓦大学）；Politecnico of Milano（米兰理工大学）
In this paper, we propose, discuss, and validate an online Nonlinear Model Predictive Control (NMPC) method for multi-rotor aerial systems with arbitrarily positioned and oriented rotors which simultaneously addresses the local reference trajectory planning and tracking problems. This work brings into question some common modeling and control design choices that are typically adopted to guarantee robustness and reliability but which may severely limit the attainable performance. Unlike most of state of the art works, the proposed method takes advantages of a unified nonlinear model which aims …
Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）
IIIT-Hyderabad（印度国际信息技术研究所海得拉巴分校）；Tampere University of Technology（坦佩雷理工大学）；Mathworks Inc.（Mathworks公司）
In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization la…
Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots
Autonomous Systems Lab, Stanford University, USA（斯坦福大学自主系统实验室，美国）；Institute of Mechanical Systems, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院机械系统研究所）；Soft Robotics Lab, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院软机器人实验室）；NVIDIA Research, Santa Clara, USA（美国圣克拉拉NVIDIA研究）
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Massachusetts Institute of Technology（麻省理工学院）
Demonstrating acrobatic behavior of a humanoid robot such as flips and spinning jumps requires systematic approaches across hardware design, motion planning, and control. In this paper, we present a new humanoid robot design, an actuator-aware kino-dynamic motion planner, and a landing controller as part of a practical system design for highly dynamic motion control of the humanoid robot. To achieve the impulsive motions, we develop two new proprioceptive actuators and experimentally evaluate their performance using our custom-designed dynamometer. The actuator's torque, velocity, and power li…
IIIT Hyderabad（海得拉巴国际信息技术研究所）；University of Tartu（塔尔图大学）
Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this paper, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goal-directed parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cos…
Southern University of Science and Technology（南方科技大学）
This article presents the design, implementation, and performance evaluation of SWheg, a novel modular wheel-leg transformable robot family with minimalist actuator realization. SWheg takes advantage of both wheeled and legged locomotion by seamlessly integrating them on a single platform. In contrast to other designs that use multiple actuators, SWheg uses only one actuator to drive the transformation of all the wheel-leg modules in sync. This means an N-legged SWheg robot requires only N+1 actuators, which can significantly reduce the cost and malfunction rate of the platform. The tendon-dri…
Successful soft robot modeling approaches appearing in recent literature have been based on a variety of distinct theories, including traditional robotic theory, continuum mechanics, and machine learning. Though specific modeling techniques have been developed for and validated against already realized systems, their strengths and weaknesses have not been explicitly compared against each other. In this paper, we show how three distinct model structures ---a lumped-parameter model, a continuum mechanical model, and a neural network--- compare in capturing the gross trends and specific features …
We present formulation and open-source tools to achieve in-material model predictive control of sensor/actuator systems using learned forward kinematics and on-device computation. Microcontroller units (MCUs) that compute the prediction and control task while colocated with the sensors and actuators enable in-material untethered behaviors. In this approach, small parameter size neural network models learn forward kinematics offline. Our open-source compiler, nn4mc, generates code to offload these predictions onto MCUs. A Newton-Raphson solver then computes the control input in real time. We fi…
Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications
Mechatronics group at the Department of Electrical Engineering, Chalmers University of Technology（楚克大学电气工程系机电组）；Dept. of Electrical and Information Engineering, Italian National Ph.D. DAUSY, Polytechnic of Bari（意大利国家博士项目DAUSY巴里理工学院电子与信息工程系）
Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
In this paper, we propose a novel framework on force-and-moment-based Model Predictive Control (MPC) for dynamic legged robots. Specifically, we present a formulation of MPC designed for 10 degree-of-freedom (DoF) bipedal robots using simplified rigid body dynamics with input forces and moments. This MPC controller will calculate the optimal inputs applied to the robot, including 3-D forces and 2-D moments at each foot. These desired inputs will then be generated by mapping these forces and moments to motor torques of 5 actuators on each leg. We evaluate our proposed control design on physical…
Michigan State University（密歇根州立大学）；Oakland University（奥克兰大学）
Event-triggered model predictive control (eMPC) is a popular optimal control method with an aim to alleviate the computation and/or communication burden of MPC. However, it generally requires priori knowledge of the closed-loop system behavior along with the communication characteristics for designing the event-trigger policy. This paper attempts to solve this challenge by proposing an efficient eMPC framework and demonstrate successful implementation of this framework on the autonomous vehicle path following. First of all, a model-free reinforcement learning (RL) agent is used to learn the op…
Fluid-based soft actuators are an attractive option for lightweight and human-safe robots. These actuators, combined with fluid pressure force feedback, are in principle a form of series-elastic actuation (SEA), in which nearly all driving-point (e.g. motor/gearbox) friction can be eliminated. Fiber-elastomer soft actuators offer unique low-friction and low-hysteresis mechanical properties which are particularly suited to force-control based on internal pressure force feedback, rather than traditional external force feedback using force/tactile sensing, since discontinuous (Coulomb) endpoint f…
In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about…
Portable, High-Frequency, and High-Voltage Control Circuits for Untethered Miniature Robots Driven by Dielectric Elastomer Actuators
Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Beijing Key Laboratory of Transformative High-end Manufacturing Equipment and Technology, Tsinghua University（机械工程系、先进设备摩擦学国家重点实验室、北京 transformative 高端制造装备与技术重点实验室、清华大学）
Making safe and human-like decisions is an essential capability of autonomous driving systems, and learning-based behavior planning presents a promising pathway toward achieving this objective. Distinguished from existing learning-based methods that directly output decisions, this work introduces a predictive behavior planning framework that learns to predict and evaluate from human driving data. This framework consists of three components: a behavior generation module that produces a diverse set of candidate behaviors in the form of trajectory proposals, a conditional motion prediction networ…
Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology（香港科学与技术大学机器人与自主系统方向）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学电子与计算机工程系）；Department of Civil and Environmental Engineering, University of California, Los Angeles（加州大学洛杉矶分校土木与环境工程系）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
Transportation Research Center Inc.（美国交通研究中心公司）；National Highway Traffic Safety Administration（美国国家公路交通安全管理局）
Vehicles with Automated Driving Systems (ADS) operate in a high-dimensional continuous system with multi-agent interactions. This continuous system features various types of traffic agents (non-homogeneous) governed by continuous-motion ordinary differential equations (differential-drive). Each agent makes decisions independently that may lead to conflicts with the subject vehicle (SV), as well as other participants (non-cooperative). A typical vehicle safety evaluation procedure that uses various safety-critical scenarios and observes resultant collisions (or near collisions), is not sufficie…
Technische Universität Braunschweig（布伦瑞克工业大学）；Institute of Control Engineering（控制工程研究所）
The advent of automated vehicles operating at SAE levels 4 and 5 poses high fault tolerance demands for all functions contributing to the driving task. At the actuator level, fault-tolerant vehicle motion control, which exploits functional redundancies among the actuators, is one means to achieve the required degree of fault tolerance. Therefore, we give a comprehensive overview of the state of the art in actuator fault-tolerant vehicle motion control with a focus on drive, brake, and steering degradations, as well as tire blowouts. This review shows that actuator fault-tolerant vehicle motion…
Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits in performance and generalization are observed on unseen test roads in both simulation and on a real Jackal robot. The novel predictive representation is learned by general value functions (GVFs) to provide out-of-policy, or counter-factual, predictions of future lane centeredness and road angle that form a compact representation of the state of the agent improving learning in both online and offline reinforcement lea…
fortiss GmbH（福蒂斯有限公司）；Technische Universität München（慕尼黑工业大学）
Although many research vehicle platforms for autonomous driving have been built in the past, hardware design, source code and lessons learned have not been made available for the next generation of demonstrators. This raises the efforts for the research community to contribute results based on real-world evaluations as engineering knowledge of building and maintaining a research vehicle is lost. In this paper, we deliver an analysis of our approach to transferring an open source driving stack to a research vehicle. We put the hardware and software setup in context to other demonstrators and ex…
Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural motion planners for autonomous vehicles. Instead, this work proposes to learn which features are task-relevant. Given its immediate relevance to motion planning, our proposed architecture encodes the probabilistic occupancy map as a proxy for obtaining pre-trained state representations. By leveraging a map-aware graph formulation of the environment, our agent-centric encoder generalizes to arbitrary road networks and t…
