Time-optimal velocity planning along predefined path for static formations of mobile robots

2016 ◽  
Vol 15 (1) ◽  
pp. 293-302 ◽  
Author(s):  
Toni Petrinić ◽  
Mišel Brezak ◽  
Ivan Petrović
Keyword(s):  
Mobile Robots ◽  
Optimal Velocity ◽  
Velocity Planning ◽  
Time Optimal

scholarly journals Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 943 ◽  
Author(s):  
Il Bae ◽  
Jaeyoung Moon ◽  
Jeongseok Seo
Keyword(s):  
Real Time ◽  
High Performance ◽  
Autonomous Driving ◽  
Planning Method ◽  
Dynamics Simulation ◽  
Driving Experience ◽  
Optimal Velocity ◽  
Planning Approach ◽  
Velocity Planning ◽  
Time Optimal

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.

Time-optimal velocity planning by a bound-tightening technique

2018 ◽  
Vol 70 (1) ◽  
pp. 61-90 ◽  
Author(s):  
Federico Cabassi ◽  
Luca Consolini ◽  
Marco Locatelli
Keyword(s):  
Optimal Velocity ◽  
Velocity Planning ◽  
Bound Tightening ◽  
Time Optimal

scholarly journals Optimal Velocity Planning of Wheeled Mobile Robots on Specific Paths in Static and Dynamic Environments

2003 ◽  
Vol 20 (12) ◽  
pp. 737-754 ◽  
Author(s):  
María Prado ◽  
Antonio Simón ◽  
Enrique Carabias ◽  
Ana Perez ◽  
Francisco Ezquerro
Keyword(s):  
Mobile Robots ◽  
Dynamic Environments ◽  
Wheeled Mobile Robots ◽  
Optimal Velocity ◽  
Velocity Planning

scholarly journals Time-Optimal Velocity Tracking Control for Consensus Formation of Multiple Nonholonomic Mobile Robots

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7997
Author(s):  
Hamidreza Fahham ◽  
Abolfazl Zaraki ◽  
Gareth Tucker ◽  
Mark W. Spong
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Switched System ◽  
Wheeled Mobile Robot ◽  
Consensus Algorithm ◽  
Consensus Formation ◽  
Robot System ◽  
Optimal Velocity ◽  
Time Optimal ◽  
Velocity Tracking

The problem of velocity tracking is considered essential in the consensus of multi-wheeled mobile robot systems to minimise the total operating time and enhance the system’s energy efficiency. This study presents a novel switched-system approach, consisting of bang-bang control and consensus formation algorithms, to address the problem of time-optimal velocity tracking of multiple wheeled mobile robots with nonholonomic constraints. This effort aims to achieve the desired velocity formation in the least time for any initial velocity conditions in a multiple mobile robot system. The main findings of this study are as follows: (i) by deriving the equation of motion along the specified path, the motor’s extremal conditions for a time-optimal trajectory are introduced; (ii) utilising a general consensus formation algorithm, the desired velocity formation is achieved; (iii) applying the Pontryagin Maximum Principle, the new switching formation matrix of weights is obtained. Using this new switching matrix of weights guarantees that at least one of the system’s motors, of either the followers or the leader, reaches its maximum or minimum value by using extremals, which enables the multi-robot system to reach the velocity formation in the least time. The proposed approach is verified in a theoretical analysis along with the numerical simulation process. The simulation results demonstrated that using the proposed switched system, the time-optimal consensus algorithm behaved very well in the networks with different numbers of robots and different topology conditions. The required time for the consensus formation is dramatically reduced, which is very promising. The findings of this work could be extended to and beneficial for any multi-wheeled mobile robot system.

Time-Optimal Gathering Algorithm of Mobile Robots with Local Weak Multiplicity Detection in Rings

2013 ◽  
Vol E96.A (6) ◽  
pp. 1072-1080 ◽  
Author(s):  
Tomoko IZUMI ◽  
Taisuke IZUMI ◽  
Sayaka KAMEI ◽  
Fukuhito OOSHITA
Keyword(s):  
Mobile Robots ◽  
Time Optimal

Optimal Control of a Multirotor Unmanned Aerial Vehicle Based on a Multiphysical Model

2020 ◽  
Author(s):  
Nicolas Michel ◽  
Zhaodan Kong ◽  
Xinfan Lin
Keyword(s):  
Optimal Control ◽  
Unmanned Aerial Vehicle ◽  
Operation Time ◽  
Rigid Body Dynamics ◽  
System Level ◽  
Optimal Velocity ◽  
Rotor Aerodynamics ◽  
Time Optimal ◽  
Aerial Vehicle ◽  
Level Model

Abstract Electric multirotor aircraft with vertical-take-off-and-landing capabilities are emerging as a revolutionary transportation mode. This paper studies optimal control of a multirotor unmanned aerial vehicle based on a system-level multiphysical model. The model considers aerodynamics of the rotor-propeller assembly, electro-mechanical dynamics of the motor and motor controller, and rigid-body dynamics of the vehicle, as control based on a system-level model incorporating all these dynamics and their coupling is missing in literature. A forward flight operation is considered for time-optimal and energy-optimal control, as well as battery voltages of 25 V and 21 V. Energy-optimal control is shown to reduce the energy required for the operation by 38.5% at 25 V, while reducing the battery voltage increases the minimum operation time by 19.8%. The energy-optimal cruise velocity is also examined, demonstrating that the optimal velocity predicted without considering rotor aerodynamics uses 35.2% more energy per meter travelled than is required at the true optimal velocity.

Model-Based Reinforcement Learning for Time-Optimal Velocity Control

2020 ◽  
Vol 5 (4) ◽  
pp. 6185-6192
Author(s):  
Gabriel Hartmann ◽  
Zvi Shiller ◽  
Amos Azaria
Keyword(s):  
Reinforcement Learning ◽  
Velocity Control ◽  
Optimal Velocity ◽  
Model Based ◽  
Time Optimal
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