Integrated trajectory control and collision avoidance for automated driving

Verhaegh, Jan; Ploeg, Jeroen; Nunen, Ellen van; Teerhuis, Arjan

doi:10.1109/mtits.2017.8005650

Cited by 5 publications

(8 citation statements)

References 8 publications

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“…Particular focus was given to the design of the outer loop in the presence of actuation and friction limits. We have shown how unconstrained control laws, based on input-output linearization, can be augmented with corrective actions that fulfill 3 obtained by solving (22) without the stability constraint (22a) control constraints and minimize loss of performance. The key idea was to treat the correction actions as perturbations in a Lyapunov sense, which then allowed us to relate corrective actions with Lyapunov-based performance loss.…”

Section: Discussionmentioning

confidence: 99%

“…where v * i is the velocity reference signal transmitted to the inner loop i = {x, ψ}, and α i = 1/τ i the inverse of the dominant time constant. The disturbances d x , d ψ capture model mismatches between the real vehicle response and the controloriented model (3). They are assumed to lie in the set…”

Section: A Vehicle Modelmentioning

confidence: 99%

“…In the literature, several solutions can be found for this design problem. They range from nonlinear control laws, reliant on Lyapunov methods [2], input-output linearization [3], linear control based on disturbance observers (DOB) [4], sliding mode control [5] and model-predictive control (MPC) [6]. The majority of approaches usually assume that the vehicle operates with negligible wheel slips and/or distant from friction limits.…”

Section: Introductionmentioning

confidence: 99%

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Cascaded Trajectory Tracking Control for Automated Vehicles

Castro

Brembeck

2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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This work deals with the design of a trajectory tracking controller (TTC) for highly automated vehicles. The proposed design follows a modular architecture, composed of an inner velocity loop and an outer trajectory tracking loop. The outer loop, which represents the main focus on this work, is further divided into two parts. The first uses input-output linearization methods to tackle model nonlinearities. The second part exploits Lyapunov methods and convex optimization to enforce physical constraints -e.g. friction circle -and minimize TTC's loss of performance. Simulation results demonstrate that, in comparison with unconstrained TTC methods, the proposed approach is able to reduce tracking errors in up to 50% when the vehicle operates at the limit of friction.

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Section: Discussionmentioning

confidence: 99%

Section: A Vehicle Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cascaded Trajectory Tracking Control for Automated Vehicles

Castro

Brembeck

2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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“…Rhomboids are traffic flows similar to Figure 14. In the former case, 12 which is a merging style where only one AGV enters a merging point, thus improving the AGV flow. This study uses this merging style as shown in Figure 9.…”

Section: Condition Decisionmentioning

confidence: 99%

“…To control AGV throughput at merging points, the timing of AGVs entering the merging points should be controlled, which is the second challenge in this study. Solutions to this problem can be divided into three approaches: (1) the optimal scheduling approach, 11 (2) the control theory-based approach, 12 and (3) the simulation-based and/or statistical approach. [13][14][15] In a study, AGVs were modeled around a merging point as an integer linear programming problem, providing AGV schedules, which describe AGV movement.…”

Section: Introductionmentioning

confidence: 99%

Maximizing traffic flow of automated guided vehicles based on Jamology and applications

Kumagai

Sawada

Shin

2018

Advances in Mechanical Engineering

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This study aims to propose and investigate a theoretical maximum traffic flow method. Furthermore, we propose a merging timing control based on the results obtained from this method, which are verified using actual automated guided vehicles. The results (micro-level optimization) enable automated guided vehicles to achieve a fabrication level optimization (macro-level optimization). Engineers working in the field of automated guided vehicles have found that there is an optimal headway distance that achieves a maximum traffic throughput when automated guided vehicles operate with merging points. ''Zipper merging'' is known to be a suitable merging method resulting from ''Jamology.'' In this study, first, we will verify the results using the ''Zipper merging'' simulation model. Second, we develop a mathematical model and verify its simulations and experiments using actual automated guided vehicles. Finally, we develop a merging timing control to augment transfer flow and verify the effects using a small full fabrication simulation. We predict that this control will improve factory model throughput when used in practical applications.

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Integrated deep learning for self-driving robotic cars

Gonsalves

Upadhyay

2021

Artificial Intelligence for Future Generation Robotics

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Integrated trajectory control and collision avoidance for automated driving

Cited by 5 publications

References 8 publications

Cascaded Trajectory Tracking Control for Automated Vehicles

Cascaded Trajectory Tracking Control for Automated Vehicles

Maximizing traffic flow of automated guided vehicles based on Jamology and applications

Integrated deep learning for self-driving robotic cars

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