Adaptable Robot Formation Control: Adaptive and Predictive Formation Control of Autonomous Vehicles

Guillet, Audrey; Lenain, Roland; Thuilot, Benoı̂t; Martinet, Philippe

doi:10.1109/mra.2013.2295946

Cited by 62 publications

(46 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Guillet et al presented a general framework dedicated to adaptable robot formation control. It counts with various dynamics types encountered in a formation of several mobile robots (potentially heterogeneous), through an adaptive and predictive algorithm [11]. The control architecture proposed in such paper constitutes a generic strategy for the formation control of mobile robots in different contexts for various applications.…”

Section: Related Workmentioning

confidence: 99%

Simulation platform for cooperative vehicle systems

Gómez¹,

Santos²,

Filho³

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Road accidents is one of the major cause of deaths worldwide. Traffic is also one of the main problems in large cities. The development of intelligent transportation systems may contribute to the solution of both problems. Autonomous vehicles have been addressed by the academic community for decades and recently have been obtaining a considerable attention from the industry. One of the next steps for such technology is the development of communication systems that allow cooperative driving, which could improve the traffic efficiency. This paper proposes a simulation framework for the development of cooperative driving systems. Our framework is capable of accurate simulation of vehicles, sensors, complex environments, and wireless communication between vehicles. It is also compatible to well known robotic control middlewares, allowing portability of the develop code between real and simulated test vehicles. Results of cooperative adaptive cruise control with multiple vehicles are presented to validate the framework proposed.

show abstract

Section: Related Workmentioning

confidence: 99%

Simulation platform for cooperative vehicle systems

Gómez¹,

Santos²,

Filho³

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…The desired acceleration or speed are then tracked with adaptive throttle and brake controllers in the lower level. With this scheme, model predict controllers are developed for the formation control of off-road autonomous vehicles [6], [7]. Linear optimal upper-level controllers are proposed for the full speed range cruise control in [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based throttle and brake control for autonomous vehicle following

Zhu

Huang

Sun

et al. 2017

2017 Chinese Automation Congress (CAC)

View full text Add to dashboard Cite

Abstract-In this paper, we focus on the basic form of autonomous follow driving problem with one leader and one follower. A reinforcement learning based throttle and brake control approach is developed for the follower vehicle. Near optimal control law is directly learned by "trial and error" with the neural dynamic programming algorithm. According to the timely updated following state, the learned control policy can deliver appropriate throttle and brake control commands for the follower. Simulation tests to illustrate the effectiveness of the presented method are carried out with the highly recognized vehicle dynamic simulator CarSim.Index Terms-autonomous driving, autonomous following, throttle and brake control, neural dynamic programming, reinforcement learning.

show abstract

“…These tasks are already accomplished by vision-based robots, which are nowadays built with inexpensive equipment and have an easy maintenance. Several techniques to endow vehicles with vision-based control can be found in (Güzel, 2013;Bertozzi & Broggi, 1997;Tisdale, et al, 2009;Carloni, et al, 2013;Yunji, et al, 2013;Ivancsits & Lee, 2013;Lim, et al, 2012;Mahony, et al, 2012;Guillet, et al, 2014;Mcgill & Taylor, 2011). They enable object detection, path orientation and distance control.…”

Section: Introductionmentioning

confidence: 99%

“…For example, aircraft fuselage (Tisdale, et al, 2009;Kendoul, et al, 2009), helicopters (Yunji, et al, 2013;Ivancsits & Lee, 2013), quadmotors (Carloni, et al, 2013;Ivancsits & Lee, 2013;Lim, et al, 2012;Mahony, et al, 2012;Frew, et al, 2004), road robots (Bertozzi & Broggi, 1997;Guillet, et al, 2014;) and flying simulator games (Neto & Campos, 2009;Neto, et al, 2011) use Dubin's Path Waypoints (Dubins, 1957). All these vehicles use different approaches of location and orientation methods with techniques that either focus on the odometer or on the accelerometer, or even on the GPS itself.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generating Road Network Graph with Vision-Based Unmanned Vehicle

Yano

Zampirolli

2017

IJURCA

View full text Add to dashboard Cite

This work has undergone a double-blind review by a minimum of two faculty members from institutions of higher learning from around the world. The faculty reviewers have expertise in disciplines closely related to those represented by this work. If possible, the work was also reviewed by undergraduates in collaboration with the faculty reviewers. AbstractWith the advancement of technology and its cheapness, robotic vehicles have gained a large number of applications. The spread of their use is growing also because they are getting smaller, lighter and easier to build. In this paper we present a simple and effective way to map a road network with the help of a driverless vehicle. Our approach consists of only three parts: vision-segmentation, angle variation and travelled distance. A video camera attached to a Lego® NXT Mindstorm vehicle guides it by image segmentation using Matlab® Image processing toolbox, along a road network, in which is represented by black tape over a white floor. The algorithm makes the vehicle travel all over the road memorizing main coordinates to identify all crossroads by keeping track of the travelled distance and the current angle. The crossroads and road's end are the nodes of the graph. After several simulations have been performed, the modelling proved to be successful in that small scale approach. Consequently, there are good chances that driverless cars and UAVs also make use of the strategies to map route networks accordingly. The algorithm presented in this paper is useful when there is no localization signal such as GPS, for example, navigation on water, tunnels, inside buildings, among others.

show abstract

Adaptable Robot Formation Control: Adaptive and Predictive Formation Control of Autonomous Vehicles

Cited by 62 publications

References 18 publications

Simulation platform for cooperative vehicle systems

Simulation platform for cooperative vehicle systems

Reinforcement learning based throttle and brake control for autonomous vehicle following

Generating Road Network Graph with Vision-Based Unmanned Vehicle

Contact Info

Product

Resources

About