scholarly journals Motion Boundaries Formation For Autonomous Vehicles

2021 ◽  
Author(s):  
Joel Bannis
Keyword(s):  
Motion Planning ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Motion Constraints ◽  
Prediction Horizon ◽  
Curvilinear Motion ◽  
Model Dynamics ◽  
Motion Boundaries ◽  
The Impact ◽  
The Ideal

<div>In this paper, the application of Model Predictive Control to perform curvilinear motion planning is explored. More specifically, nonlinear MPC will be focused on because of its proven efficiency in the modeling of uncertainties as well as in nonlinear model dynamics. The main objective of this report is to show that with proper modeling and formulation of motion constraints, curvilinear motion planning can be achieved with nonlinear MPC. The trajectory of the vehicle will be tracked with the least error while satisfying constraints such as speed and steering angles. Simulations are presented which demonstrate the ability of the suggested models to successfully perform curvilinear motion staying safely within the bounds, while simulations of several models validate its performance. A deterministic sensitivity analysis was conducted in order to determine the impact</div><div>of the prediction horizon time. Experimental results show that a critical prediction horizon time approximately 10 to 13 seconds was identified as the ideal range for optimal results of the model.</div>

Motion Planning of Autonomous Vehicles Using Obstacle-Free Q-Learning Path Generator and Model Predictive Control

2021 ◽  
Author(s):  
Roozbeh Bazargani ◽  
Mahboobe Shakeri Nadrabadi ◽  
Elnaz Firouzmand ◽  
Iman Sharifi ◽  
Heidar Ali Talebi
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Q Learning ◽  
Learning Path

Constrained model predictive control for mobile robotic manipulators

Robotica ◽  
2017 ◽  
Vol 36 (1) ◽  
pp. 19-38 ◽  
Author(s):  
Giovanni Buizza Avanzini ◽  
Andrea Maria Zanchettin ◽  
Paolo Rocco
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Online Algorithm ◽  
Mobile Manipulation ◽  
Linear Quadratic ◽  
Piecewise Constant ◽  
Motion Constraints ◽  
Prediction Horizon ◽  
Constrained Model ◽  
Mobile Base

SUMMARYThis paper discusses the application of a constraint-based model predictive control (MPC) to mobile manipulation tracking problems. The problem has been formulated so as to guarantee offset-free tracking of piecewise constant references, with convergence and recursive feasibility guarantees. Since MPC inputs are recomputed at every control iteration, it is possible to deal with dynamic and unknown scenarios. A number of motion constraints can also be easily included: Acceleration, velocity and position constraints have been enforced, together with collision avoidance constraints for the mobile base and the arm and field-of-view constraints. Such constraints have been extended over the prediction horizon maintaining a linear-quadratic formulation of the problem. Navigation performance has been improved by devising an online algorithm that includes an additional goal to the problem, derived from the classical vortex field approach. Experimental validation shows the applicability of the proposed approach.

scholarly journals Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

2021 ◽  
pp. 036119812110357
Author(s):  
Hao Zhou ◽  
Jorge Laval ◽  
Anye Zhou ◽  
Yu Wang ◽  
Wenchao Wu ◽  
...  
Keyword(s):  
Motion Planning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Domain Knowledge ◽  
Imitation Learning ◽  
Longitudinal Motion ◽  
Research Gaps ◽  
Learning Methods ◽  
Congestion Mitigation ◽  
The Impact

Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

The Impact of the Industrial Processing of Oil on the Rainfall Water Quality

2017 ◽  
Vol 68 (1) ◽  
pp. 175-179
Author(s):  
Oana Roxana Chivu ◽  
Augustin Semenescu ◽  
Claudiu Babis ◽  
Catalin Amza ◽  
Gabriel Iacobescu ◽  
...  
Keyword(s):  
Water Quality ◽  
Sulfuric Acid ◽  
Experimental Research ◽  
Air Purification ◽  
High Toxicity ◽  
Industrial Processing ◽  
Mobile Sources ◽  
Specific Activities ◽  
The Impact ◽  
The Ideal

Rainfall is a major component of the environment and the main source of the air purification becouse of many pollutants increases who have the most varied sources: various human activities including industry and agriculture, and some household duties. Air purification by means of precipitation is achieved by numerous highly complex mechanisms. The final products of degradation of the pollutant in the air, which are generally harmless, can be reacted with each other in the presence of water, giving rise to the final compounds with a high toxicity. Thus, exhaust, mobile sources of noxious almost identical to those specific activities in the industrial processing of oil, contain lead which is the ideal catalyst for converting SO2 to sulfuric acid in the presence of rainwater, with all the disadvantages that they create. This paper will present an experimental research oabout how rainfall water quality is influenced by the activity of the industrial processing of oil, in a chemical plant in Constanta County.

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