Data Driven Analysis of Locomotion for a Class of Articulated Mobile Robots

2021 ◽  
pp. 1-15
Author(s):  
Luca Carbonari ◽  
Andrea Botta ◽  
Paride Cavallone ◽  
Luigi Tagliavini ◽  
Giuseppe Quaglia
Keyword(s):  
Mobile Robots ◽  
Dynamic Model ◽  
Precision Agriculture ◽  
Autonomous Vehicles ◽  
Industrial Applications ◽  
Data Driven ◽  
Analysis Tool ◽  
Recent Past ◽  
Personal Assistance ◽  
Articulated Structures

Abstract In the recent past, the use of autonomous vehicles is becoming of relevant interest in several fields of application. Personal assistance, precision agriculture, and rescue are just few examples alongside the more common industrial applications. In many cases, the use of articulated structures is preferred to single chassis robots for their peculiar modularity. Moreover, they can be easily provided with locomotion units particularly suitable to overpass obstacles and to move on uneven grounds. Such vehicles are often built as an active front module and a rear one that is pulled passively or that can contribute to the vehicle traction when required. Understanding whether this contribution is convenient or not, it is the main matter of this paper. Two different mobile robots of different scale and purpose are taken into consideration. A dynamic model is presented and experimentally validated to be used as an analysis tool. At last, a simple yet effective actuation law is tested to evaluate the whether the contribution of the back module is beneficial or not to the whole machine manoeuvrability.

scholarly journals High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian
Keyword(s):  
High Resolution ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Data Driven ◽  
Traffic Information ◽  
Flow Density ◽  
Massive Data ◽  
Market Penetration ◽  
High Resolution Data ◽  
Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

Fault Diagnosis Based on Data-driven Dynamic Model

2021 ◽  
Author(s):  
Xuan Wang ◽  
Yanxue Wang ◽  
Hanfang Dai
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Model ◽  
Data Driven

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

scholarly journals xMWAS: a data-driven integration and differential network analysis tool

2017 ◽  
Vol 34 (4) ◽  
pp. 701-702 ◽  
Author(s):  
Karan Uppal ◽  
Chunyu Ma ◽  
Young-Mi Go ◽  
Dean P Jones
Keyword(s):  
Network Analysis ◽  
Data Driven ◽  
Analysis Tool ◽  
Differential Network Analysis ◽  
Differential Network

Stabilization of a Four-Wheel Mobile Robot From Kinematic Model to Dynamic Model by Feedback Passivation of Cascades Using Chained Form

2009 ◽  
Author(s):  
Hossein Mohammadi ◽  
Arash Haghpanah ◽  
Mohammad Eghtesad
Keyword(s):  
Mobile Robot ◽  
System Dynamics ◽  
Mobile Robots ◽  
State Space ◽  
Dynamic Model ◽  
Kinematic Model ◽  
State Space Model ◽  
Space Model ◽  
Novel Approach

In this paper, a novel approach for dynamics based stabilization of a four-wheel mobile robot is presented. One of the well-known and well-established approaches for stabilization of mobile robots is converting the kinematic model of the robot to a chained form. In order to extend this method to dynamic based stabilization, kinematic and dynamic subsystems of the mobile robot state-space model can be considered as two subsystems of a cascade and then feedback passivation of cascades can be utilized for stabilization of the whole system dynamics.

Differential evolution tuned fuzzy supervisor adapted extended Kalman filtering for SLAM problems in mobile robots

Robotica ◽  
2009 ◽  
Vol 27 (3) ◽  
pp. 411-423 ◽  
Author(s):  
Amitava Chatterjee
Keyword(s):  
Differential Evolution ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Extended Kalman Filtering ◽  
Extended Kalman Filters ◽  
De Algorithm ◽  
Localization And Mapping ◽  
Recent Method ◽  
Fuzzy Supervisor

SUMMARYThe present paper proposes a successful application of differential evolution (DE) optimized fuzzy logic supervisors (FLS) to improve the quality of solutions that extended Kalman filters (EKFs) can offer to solve simultaneous localization and mapping (SLAM) problems for mobile robots and autonomous vehicles. The utility of the proposed system can be readily appreciated in those situations where an incorrect knowledge of Q and R matrices of EKF can significantly degrade the SLAM performance. A fuzzy supervisor has been implemented to adapt the R matrix of the EKF online, in order to improve its performance. The free parameters of the fuzzy supervisor are suitably optimized by employing the DE algorithm, a comparatively recent method, popularly employed now-a-days for high-dimensional parallel direct search problems. The utility of the proposed system is aptly demonstrated by solving the SLAM problem for a mobile robot with several landmarks and with wrong knowledge of sensor statistics. The system could successfully demonstrate enhanced performance in comparison with usual EKF-based solutions for identical environment situations.

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