Groups of unmanned vehicles: differential flatness, trajectory planning, and control

Pledgie, Stephen; Hao, Yaoyao; Ferreira, A.; Agrawal, Sunil K.; Murphey, Robert

doi:10.1109/robot.2002.1014246

Cited by 17 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used fuzzy systems to control each robot speed, and fuzzy-neuro systems to implement the obstacle avoidance behaviour. Differential flat systems are utilized in Pledgie et al (2002) to model the robots behaviour. Kim et al (2001) use Petri-nets to model three control subtasks: formation maintenance, identification and formation generation.…”

Section: Related Workmentioning

confidence: 99%

Attractor dynamics approach to formation control: theory and application

Monteiro

Bicho

2010

Auton Robot

View full text Add to dashboard Cite

In this paper we show how non-linear attractor dynamics can be used as a framework to control teams of autonomous mobile robots that should navigate according to a predefined geometric formation. The environment does not need to be known a priori and may change over time. Implicit to the control architecture are some important features such as establishing and moving the formation, split and join of formations (when necessary to avoid obstacles). Formations are defined by a formation matrix. By manipulating this formation matrix it is also possible to switch formations at run time. Examples of simulation results and implementations with real robots (teams of Khepera robots and medium size mobile robots), demonstrate formation switch, static and dynamic obstacle avoidance and split and join formations without the need for any explicit coordination scheme. Robustness against environmental perturbations is intrinsically achieved because the behaviour of each robot is generated as a time series of asymptotically stable states, which contribute to the asymptotic stability of the overall control system.

show abstract

Section: Related Workmentioning

confidence: 99%

Attractor dynamics approach to formation control: theory and application

Monteiro

Bicho

2010

Auton Robot

View full text Add to dashboard Cite

show abstract

“…Optimal LQT control is one of the most successful control algorithms and is widely used in handling multivariables and constraints [13][14][15][16][17]. The designed control discipline is based on a mathematic model of a controlled object with the prescribed limit to acquire the optimal performance index [18][19][20]. According to previous research on PID control, the conventional control algorithm gives good results at infinite steady state; the only difficulty occurs when the reference trajectory is fluctuating [21,22].…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Dynamical Maximum Power Tracking Control for a Vertical Axis Water Turbine with Retractable Blades

Mao

Liu²,

Cui³

2016

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

This paper addresses the power generation control system of a new drag-type Vertical Axis Turbine with several retractable blades. The returning blades can be entirely hidden in the drum, and negative torques can then be considerably reduced as the drum shields the blades. Thus, the power efficiency increases. Regarding the control, a Linear Quadratic Tracking (LQT) optimal control algorithm for Maximum Power Point Tracking (MPPT) is proposed to ensure that the wave energy conversion system can operate highly effectively under fluctuating conditions and that the tracking process accelerates over time. Two-dimensional Computational Fluid Dynamics (CFD) simulations are performed to obtain the maximum power points of the turbine's output. To plot the tip speed ratio curve, the least squares method is employed. The efficacy of the steady and dynamic performance of the control strategy was verified using Matlab/Simulink software. These validation results show that the proposed system can compensate for power fluctuations and is effective in terms of power regulation. Hindawi Publishing Corporation

show abstract

“…In the literature, formation control for mobile robots is studied by way of reactive methods (Balch and Arkin, 1998;Fredslund and Mataric, 2001) or by centralized control (Hao et al, 2003;Guo and Parker, 2002). Algorithms for their coordination and control must account for the dynamic nature of the environment in which they operate (Hao et al, 2003;Pledgie et al, 2002;Giulietti et al, 2000;Desai * To whom correspondence should be addressed. Egerstedt and Hu, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic optimization based on first-order and higher-order representations of a system have been compared showing substantial savings in computation (Veeraklaew and Agrawal, 2001). Preliminary results of flatness based planning of groups of autonomous vehicles were reported (Hao et al, 2003;Pledgie et al, 2002;Ferreira et al, 2000). Assuming that each member of the group has flat dynamics, by imposing certain forms of intra-group dynamics, it is possible to find a flat representation for the group, while simultaneously satisfying inequalities motivated from relative position constraints.…”

Section: Introductionmentioning

confidence: 99%

Formation Planning and Control of UGVs with Trailers

Hao

Agrawal

2005

Auton Robot

View full text Add to dashboard Cite

This paper provides a framework for planning and control of formations of multiple unmanned ground vehicles with trailers to traverse between goal points in an idealized, disturbance-free environment. This framework allows on-line planning of the formations using the A* search algorithm based on current sensor data. The formation is allowed to dynamically change in order to avoid obstacles in the environment while minimizing a cost function aimed at obtaining collision-free and deadlock-free paths. Based on a feasible path for a leader of the group and the differential flatness property of a truck-tractor-trailer system, the trajectory planner satisfies the kinematic constraints of the individual vehicles while accounting for inter-vehicle collisions and path constraints. Also, optimization techniques are used to on-line change the path of the truck-tractor-trailer system. Illustrative simulations with simplified models of John Deere vehicles with trailers in formations are presented. Laboratory experiments are also performed on a 2-wheel differential drive mobile vehicle attached with a trailer cart on a flat, smooth floor using overhead cameras for precise references. The concluding section of the paper discusses some of the additional work needed to make the results applicable in a real-world environment.

show abstract

Groups of unmanned vehicles: differential flatness, trajectory planning, and control

Cited by 17 publications

References 2 publications

Attractor dynamics approach to formation control: theory and application

Attractor dynamics approach to formation control: theory and application

Discrete-Time Dynamical Maximum Power Tracking Control for a Vertical Axis Water Turbine with Retractable Blades

Formation Planning and Control of UGVs with Trailers

Contact Info

Product

Resources

About