An MPC Algorithm With Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles

2015 ◽  
Author(s):  
Jiechao Liu ◽  
Paramsothy Jayakumar ◽  
Jeffrey L. Stein ◽  
Tulga Ersal
Keyword(s):  
Obstacle Avoidance ◽  
A Priori ◽  
Simultaneous Optimization ◽  
Ground Vehicles ◽  
Steering Control ◽  
Detection Range ◽  
Autonomous Ground Vehicles ◽  
Lift Off ◽  
Multi Phase ◽  
The Cost

This article presents a model predictive control based obstacle avoidance algorithm for autonomous ground vehicles in unstructured environments. The novelty of the algorithm is the simultaneous optimization of speed and steering without a priori knowledge about the obstacles. Obstacles are detected using a planar light detection and ranging sensor and a multi-phase optimal control problem is formulated to optimize the speed and steering commands within the detection range. Acceleration capability of the vehicle as a function of speed, and stability and handling concerns such as tire lift-off are taken into account as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Thus, a safe and quick navigation is enabled without the need for a preloaded map of the environment. Simulation results show that the proposed algorithm is capable of navigating the vehicle through obstacle fields that cannot be cleared with steering control alone.

Model-predictive active steering and obstacle avoidance for autonomous ground vehicles

2009 ◽  
Vol 17 (7) ◽  
pp. 741-750 ◽  
Author(s):  
Yongsoon Yoon ◽  
Jongho Shin ◽  
H. Jin Kim ◽  
Yongwoon Park ◽  
Shankar Sastry
Keyword(s):  
Obstacle Avoidance ◽  
Ground Vehicles ◽  
Autonomous Ground Vehicles ◽  
Active Steering

scholarly journals Shared Steering Control for Lane Keeping and Obstacle Avoidance Based on Multi-Objective MPC

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4671
Author(s):  
Yang Liang ◽  
Zhishuai Yin ◽  
Linzhen Nie
Keyword(s):  
Cost Function ◽  
Obstacle Avoidance ◽  
Assessment Method ◽  
Reference Trajectory ◽  
Current Steering ◽  
Steering Control ◽  
Multi Objective ◽  
Driver’S Intention ◽  
The Cost ◽  
Lane Keeping

This paper presents a shared steering control framework for lane keeping and obstacle avoidance based on multi-objective model predictive control. One of the control objectives is to track the reference trajectory, which is updated continuously by the trajectory planning module; whereas the other is to track the driver’s current steering command, so as to consider the driver’s intention. By adding the two control objectives to the cost function of an MPC shared controller, a smooth combination of the commands of the driver and the automation can be achieved through the optimization. The authority of the driver and the automation is allocated by adjusting the weights of the objective terms in the cost function, which is determined by the proposed situation assessment method considering the longitudinal and lateral risks simultaneously. The results of the CarSim-Matlab/Simulink joint simulations show that the proposed shared controller can assist the driver to complete the tasks of lane keeping and obstacle avoidance smoothly while maintaining a good level of vehicle stability.

scholarly journals Lidar Based Intelligent Obstacle Avoidance System for Autonomous Ground Vehicles

2020 ◽  
Vol 8 (6) ◽  
pp. 2466-2472
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Ground Vehicles ◽  
Distance Information ◽  
Vast Number ◽  
Autonomous Ground Vehicles ◽  
Level Of Automation ◽  
High Level

Autonomous ground vehicles (AGVs) started occupying our day-to-day life. AGVs can be programmed to be smart with the current technological advancements. In doing so, we can apply them to assist humans in many aspects like reducing road accidents, enabling us to use cars without driving knowledge, autonomous patrolling in dangerous zones, and autonomous farming. For AGVs to operate at this level of automation, it must be equipped with sensory perception devices to be aware of its surroundings, and also, a way to perceives this data is crucial. As a first step towards this, researchers have developed a vast number of camera vision-based efficient neural network algorithms for detecting and avoiding obstacles. Unfortunately, an AGV cannot survive only with computer vision as it suffers from several effects like night driving and erroneous estimation of distance information. Camera vision and lidar vision together is suitable for AGVs to operate in all conditions like day, night, and fog. We propose a novel neural network model, which transforms the lidar sensor data into obstacle avoidance decisions, which is integrated into the hybrid vision of any AGV. Existing lidar sensor-based obstacle detection and avoidance systems like 2D collision cone approaches are not suitable for real-time applications, as they lag in providing accurate and quick responses, which leads to collisions. The proposed intelligent Field of View (FOV) mechanism replaces classical mathematical approaches, which accurately mimics the behavior of human drivers. The model quickly takes decisions with a high level of accuracy to command the AGV upon being obstructed with obstacles in the trajectory. This makes the AGV drive in obstacle rich environments without manual maneuvering autonomously.

Sign in / Sign up

Export Citation Format

Share Document