Model Predictive Control-Based Probabilistic Search Method for Autonomous Ground Robot in a Dynamic Environment

2015 ◽  
Author(s):  
Chang Liu ◽  
Shengbo Eben Li ◽  
J. Karl Hedrick
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Gaussian Function ◽  
Dynamic Environment ◽  
Gaussian Mixture ◽  
Search Method ◽  
Stationary Target ◽  
Probability Map ◽  
Probabilistic Search ◽  
Binary Sensor

Target search using autonomous robots is an important application for both civil and military scenarios. In this paper, a model predictive control (MPC)-based probabilistic search method is presented for a ground robot to localize a stationary target in a dynamic environment. The robot is equipped with a binary sensor for target detection, of which the uncertainties of binary observation are modeled as a Gaussian function. Under the model predictive control framework, the probability map of the target is updated via the recursive Bayesian estimation and the collision avoidance with obstacles is enforced using barrier functions. By approximating the updated probability map using a Gaussian Mixture Model, an analytical form of the objective function in the prediction horizon is derived, which is promising to reduce the computation complexity compared to numerical integration methods. The effectiveness of the proposed method is demonstrated by performing simulations in dynamic scenarios with both static and moving obstacles.

scholarly journals Trajectory Generation for Mobile Robots in a Dynamic Environment using Nonlinear Model Predictive Control

2021 ◽  
Author(s):  
Jonas Berlin ◽  
Georg Hess ◽  
Anton Karlsson ◽  
William Ljungbergh ◽  
Ze Zhang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Long Range ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Trajectory Generation ◽  
Dynamic Environment ◽  
Optimization Method ◽  
Time Frame ◽  
Nonlinear Model Predictive Control ◽  
Robot Dynamics

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long range planning a visibility graph together with A* is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.

A method for avoiding obstacles of a self-driving carin the dynamic environment based on model predictive control

2021 ◽  
Author(s):  
N.P. Demenkov ◽  
Kai Zou
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Urban Environment ◽  
Predictive Control ◽  
Potential Field ◽  
Dynamic Environment ◽  
Field Method ◽  
Artificial Potential Field ◽  
Physical Constraints ◽  
Local Path

The paper discusses the problem of obstacle avoidance of a self-driving car in urban road conditions. The artificial potential field method is used to simulate traffic lanes and cars in a road environment. The characteristics of the urban environment, as well as the features and disadvantages of existing methods based on the structure of planning-tracking, are analyzed. A method of local path planning is developed, based on the idea of an artificial potential field and model predictive control in order to unify the process of path planning and tracking to effectively cope with the dynamic urban environment. The potential field functions are introduced into the path planning task as constraints. Based on model predictive control, a path planning controller is developed, combined with the physical constraints of the vehicle, to avoid obstacles and execute the expected commands from the top level as the target for the task. A joint simulation was performed using MATLAB and CarSim programs to test the feasibility of the proposed path planning method. The results show the effectiveness of the proposed method.

A Mobile Robot Obstacle Avoidance Using Fuzzy Logic and Model Predictive Control

2014 ◽  
Vol 548-549 ◽  
pp. 922-927
Author(s):  
Bayanjargal Baasandorj ◽  
Aamir Reyaz ◽  
Park Joung Ho ◽  
Cha Wang Cheol ◽  
Deok Jin Lee ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Model Predictive Control ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Predictive Control ◽  
Real Life ◽  
Dynamic Environment ◽  
Logic Control ◽  
Microwave Sensor ◽  
Very High

This paper presents a method of solving the problem of mobile robot Obstacle avoidance and path planning in an unknown dynamic environment. A linear model of the two-wheeled nonholonomic robot controlled using Model predictive control controller. For obstacle avoidance Fuzzy logic control is used. The ultrasonic sensors are used for positioning and identifying an obstacle. The proposed method is successfully tested in simulations. Obstacle avoiding technique is very useful in real life, this technique can also use as a vision belt of blind people by changing the IR sensor by a kinetic sensor ,which is on type of microwave sensor whose sensing range is very high and the output of this sensor vary in according to the object position changes.

scholarly journals Robust Optimal Navigation Using Nonlinear Model Predictive Control Method Combined with Recurrent Fuzzy Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Qidan Zhu ◽  
Yu Han ◽  
Chengtao Cai ◽  
Yao Xiao
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Dynamic Environment ◽  
Filter Method ◽  
Fuzzy Neural ◽  
Navigation Method ◽  
Recurrent Fuzzy Neural Network

This paper presents a novel navigation strategy of robot to achieve reaching target and obstacle avoidance in unknown dynamic environment. Considering possible generation of uncertainty, disturbances brought to system are separated into two parts, i.e., bounded part and unbounded part. A dual-layer closed-loop control system is then designed to deal with two kinds of disturbances, respectively. In order to realize global optimization of navigation, recurrent fuzzy neural network is used to predict optimal motion of robot for its ability of processing nonlinearity and learning. Extended Kalman filter method is used to train RFNN online. Moving horizon technique is used for RFNN motion planner to guarantee optimization in dynamic environment. Then, model predictive control is designed against bounded disturbances to drive robot to track predicted trajectories and limit robot’s position in a tube with good robustness. A novel iterative online learning method is also proposed to estimate intrinsic error of system using online data that makes system adaptive. Feasibility and stability of proposed method are analyzed. By examining our navigation method on mobile robot, effectiveness is proved in both simulation and hardware experiments. Robustness and optimization of proposed navigation method can be guaranteed in dynamic environment.

Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control

2020 ◽  
Author(s):  
YIMIN CHEN ◽  
Huilong Yu ◽  
Jinwei Zhang ◽  
Donpu Cao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Potential Field ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Prediction Method ◽  
Gaussian Mixture ◽  
Driving Safety ◽  
Trajectory Prediction ◽  
Vehicle Trajectory

Abstract The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the lane-exchanging scenario. The nearby vehicle trajectory needs to be predicted, from which the autonomous vehicle is controlled to prevent possible collisions. This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control. A trajectory prediction method is developed to anticipate the nearby vehicle trajectory. The Gaussian mixture model (GMM), together with the vehicle kinematic model, are synthesized to predict the nearby vehicle trajectory. A potential-field-based model predictive control (MPC) approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver. The potential field of the nearby vehicle is considered in the controller design for collision avoidance. On-road driving data verification shows the nearby vehicle trajectory can be predicted by the proposed method. CarSim simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy. The autonomous vehicle can thus safely perform the lane-exchanging maneuver and avoid the nearby vehicle.

scholarly journals Trajectory Generation for Mobile Robots in a Dynamic Environment using Nonlinear Model Predictive Control

2021 ◽  
Author(s):  
Jonas Berlin ◽  
Georg Hess ◽  
Anton Karlsson ◽  
William Ljungbergh ◽  
Ze Zhang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Long Range ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Trajectory Generation ◽  
Dynamic Environment ◽  
Optimization Method ◽  
Time Frame ◽  
Nonlinear Model Predictive Control ◽  
Robot Dynamics

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long range planning a visibility graph together with A* is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.

Sign in / Sign up

Export Citation Format

Share Document