Autonomous vehicle's technology is expected to be disruptive for automotive industry in next years. This paper proposes a novel real-time trajectory planner based on a Nonlinear Model Predictive Control (NMPC) algorithm. A nonlinear single track vehicle model with Pacejka's lateral tyre formulas has been implemented. The numerical solution of the NMPC problem is obtained by means of the implementation of a novel genetic algorithm strategy. Numerical results are discussed through simulations that shown a reasonable behavior of the proposed strategy in presence of static or moving obstacles as well as in a wide rage of road friction conditions. Moreover a real-time implementation is made possible by the reported computational time analysis.
Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction force optimization integrates a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot's kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot's rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation's generality thro…
In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are separated, limiting the planner to react to predictions that are not informed by the planned trajectory of the AV. This work presents a module that tightly couples these layers via a game-theoretic Model Predictive Controller (MPC) that uses a novel interactive multi-agent neural network policy as part of its predictive model. In our setting, the MPC planner considers all the surrounding agents by informing the multi-agent policy with the planned state sequence. Fundamental to the success of our method is …
Institut de Robòtica i Informàtica Industrial (CSIC-UPC)（机器人与工业信息研究所（CSIC-UPC））；Universitat Politécnica de Catalunya（加泰罗尼亚理工大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立高等物理、数学与信息学院）
In this paper, the Model Predictive Control (MPC) and Moving Horizon Estimator (MHE) strategies using a data-driven approach to learn a Takagi-Sugeno (TS) representation of the vehicle dynamics are proposed to solve autonomous driving control problems in real-time. To address the TS modeling, we use the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to obtain a set of polytopic-based linear representations as well as a set of membership functions relating in a non-linear way the different linear subsystems. The proposed control approach is provided by racing-based references of an exte…
Designing a local planner to control tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem in the research community of autonomous driving systems. Considering a critical situation in the stability of tractor-trailer systems, a practical and novel approach is presented to design a non-linear MPC(NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor velocity and steering angle are considered to be control variables. The proposed NMPC local planner is designed to handle jackknife situations, avoid…
Kungliga Tekniska Högskolan（瑞典皇家理工学院）；Technical University of Munich（慕尼黑工业大学）；Robert Bosch GmbH（罗伯特·博世有限公司）
Differential drive mobile robots often use one or more caster wheels for balance. Caster wheels are appreciated for their ability to turn in any direction almost on the spot, allowing the robot to do the same and thereby greatly simplifying the motion planning and control. However, in aligning the caster wheels to the intended direction of motion they produce a so-called bore torque. As a result, additional motor torque is required to move the robot, which may in some cases exceed the motor capacity or compromise the motion planner's accuracy. Instead of taking a decoupled approach, where the …
Zhejiang University（浙江大学）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）；National Engineering Research Center for Industrial Automation (Ningbo Institute)（工业自动化国家工程研究中心（宁波研究院））
The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectiv…
An ACO-MPC Framework for Energy-Efficient and Collision-Free Path Planning in Autonomous Maritime Navigation
School of Engineering, University of Strathclyde（斯特拉思克莱德大学工程学院）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；School of Power and energy, Northwestern Polytechnical University（西北工业大学动力与能源学院）；School of Engineering, University of Birmingham（伯明翰大学工程学院）
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller. Our method is named as corridor-based model predictive contouring control (CMPCC) since it builds upon on MPCC and utilizes the flight corridor as hard safety constraints. It optimizes the flight aggressiveness and tracking accuracy simultaneously, thus improving our system's robustness by overcoming unmeasured disturbances. Our method features its online flight speed optimization, strict safety and feasibility, and real-time performance, a…
Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles
Jilin University（吉林大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Tongji University（同济大学）；NKIARI；Nankai University（南开大学）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE（教育部知识驱动人机智能工程研究中心）
Real-time motion planning and decision-making for a group of differential drive robots under connectivity constraints using robust MPC and mixed-integer programming
Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Cognitive Robotics Department, TU Delft（代尔夫特理工大学认知机器人部门）；Damen Naval（达门海军）；Centre for Intelligent Transport, Queen Mary University of London（伦敦大学玛丽女王学院智能交通中心）
This paper presents an integrated path planning and tracking control of marine hydrokinetic energy harvesting devices. To address the highly nonlinear and uncertain oceanic environment, the path planner is designed based on a reinforcement learning (RL) approach by fully exploring the historical ocean current profiles. The planner will search for a path to optimize a chosen cost criterion, such as maximizing the total harvested energy for a given time. Model predictive control (MPC) is then utilized to design the tracking control for the optimal path command from the planner subject to problem…
Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University（教育部道路与交通工程重点实验室，同济大学）；UCLA Mobility Lab, University of California, Los Angeles（加州大学洛杉矶分校UCLA移动实验室）
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We find that well-tuned model-free agents are strong baselines even for high DoF control problems but MPC with learned proposals and models (trained on the fly or transferred from related tasks) can s…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（图宾根大学）；International Institute for Information Technology（国际信息技术研究所）
In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex const…
Delft University of Technology（代尔夫特理工大学）；Massachusetts Institute of Technology（麻省理工学院）
Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require global guidance, which is not trivial to obtain in crowded scenarios. This paper proposes to learn, via deep Reinforcement Learning (RL), an interaction-aware policy that provides long-term guidance to the local planner. In particular, in simulations with …
Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
Department of Computer Science（计算机科学系）；Maharishi International University（玛哈里什国际大学）；Electrical Engineering and Computing（电气工程与计算）；University of Zagreb（扎格雷布大学）；Eastern Illinois University（东部伊利诺伊大学）
University of California, Berkeley（加州大学伯克利分校）；University of Minnesota-Twin Cities（明尼苏达大学双城分校）
This paper presents a novel planning and control strategy for competing with multiple vehicles in a car racing scenario. The proposed racing strategy switches between two modes. When there are no surrounding vehicles, a learning-based model predictive control (MPC) trajectory planner is used to guarantee that the ego vehicle achieves better lap timing performance. When the ego vehicle is competing with other surrounding vehicles to overtake, an optimization-based planner generates multiple dynamically-feasible trajectories through parallel computation. Each trajectory is optimized under a MPC …
The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly…
Autonomous ground vehicles (AGVs) are receiving increasing attention, and the motion planning and control problem for these vehicles has become a hot research topic. In real applications such as material handling, an AGV is subject to large uncertainties and its motion planning and control become challenging. In this paper, we investigate this problem by proposing a hierarchical control scheme, which is integrated by a model predictive control (MPC) based path planning and trajectory tracking control at the high level, and a reduced-order extended state observer (RESO) based dynamic control at…
Stanford University（斯坦福大学）；DeepMind（深度思维）；University of California Berkeley（加州大学伯克利分校）
We introduce MuJoCo MPC (MJPC), an open-source, interactive application and software framework for real-time predictive control, based on MuJoCo physics. MJPC allows the user to easily author and solve complex robotics tasks, and currently supports three shooting-based planners: derivative-based iLQG and Gradient Descent, and a simple derivative-free method we call Predictive Sampling. Predictive Sampling was designed as an elementary baseline, mostly for its pedagogical value, but turned out to be surprisingly competitive with the more established algorithms. This work does not present algori…
Autonomous Block Assembly for Boom Cranes with Passive Joint Dynamics: Integrated Vision MPC Control
Automation & Control Institute (ACIN), TU Wien（自动化与控制研究所（ACIN），维也纳技术大学）；Center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH（视觉、自动化与控制中心，奥地利技术研究所有限公司）
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Resolving edge-cases in autonomous driving, head-to-head autonomous racing is getting a lot of attention from the industry and academia. In this study, we propose a game-theoretic model predictive control (MPC) approach for head-to-head autonomous racing and data-driven model identification method. For the practical estimation of nonlinear model parameters, we adopted the hyperband algorithm, which is used for neural model training in machine learning. The proposed controller comprises three modules: 1) game-based opponents' trajectory predictor, 2) high-level race strategy planner, and 3) MPC…
