Avoidance Control with Relative Velocity Information for Lagrangian Dynamics

Zhang, Wenxue; Rodriguez-Seda, Erick J.; Deka, Shankar A.; Amrouche, Massinissa; Zhou, Di; Stipanović, Dušan M.; Leitmann, G.

doi:10.1007/s10846-019-01122-x

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…For example, for image data synthesis, a visual structure is applied to produce an estimated geometric representation of an object, whether the image input is static. The second example enables the creation of an image-based human model that may be utilized for optical motion capture 165 .…”

Section: Geometric Model (Gm)mentioning

confidence: 99%

“…Researchers have fantastic solutions for enhanced autonomous decision-making and control for AVs. In the article 165 , authors propose a particular precise deep Q-network-based automatic braking system to avoid vehicle-pedestrian collisions (DQN). Subsequently, in the article 163 authors created a cooperative adaptive cruise control (CACC) automobile controller based on RL.…”

Section: Dynamic Model (Dm)mentioning

confidence: 99%

“…Subsequently, in the article 163 authors created a cooperative adaptive cruise control (CACC) automobile controller based on RL. Recently, the authors, in 165 , proposed a framework for CACC systems based on supervised reinforcement learning (SRL). However, authors in 166 proposed a considerable method to overcome the coordination problem in autonomous driving using multi-agent reinforcement learning (MARL).…”

Section: Dynamic Model (Dm)mentioning

confidence: 99%

“…For a group of robots with a centralized aim to achieve a task collectively, swarm intelligence, formation control, and consensus control have all been widely employed, as has multi-AV control 162 . A centralized controller or planner coordinates the movement of vehicles in cooperative multiple-vehicle systems to achieve a shared goal, like collectively stabilizing traffic flow and smoothing traffic jams, optimizing driving comfort, or improving fuel efficiency 165 .…”

Section: Cooperative Control (Cc)mentioning

confidence: 99%

See 3 more Smart Citations

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

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Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

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Section: Geometric Model (Gm)mentioning

confidence: 99%

Section: Dynamic Model (Dm)mentioning

confidence: 99%

Section: Dynamic Model (Dm)mentioning

confidence: 99%

Section: Cooperative Control (Cc)mentioning

confidence: 99%

See 2 more Smart Citations

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A crucial problem for all motion planning strategies is obstacle avoidance. Methods for obstacle avoidance may either be position-dependent [4,31] or velocity-dependent [5,44], with the latter guaranteeing smoother trajectories, especially in the presence of moving agents and obstacles. In this paper, we focus on the obstacle avoidance problem within the Dynamic Movement Primitives (DMPs) framework [10,14,23,36].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

Ginesi

Meli

Roberti

et al. 2021

J Intell Robot Syst

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Obstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.

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Vision-Locomotion Coordination Control for a Powered Lower-Limb Prosthesis Using Fuzzy-Based Dynamic Movement Primitives

Hong

Bian

Xiong

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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Avoidance Control with Relative Velocity Information for Lagrangian Dynamics

Cited by 10 publications

References 23 publications

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

Vision-Locomotion Coordination Control for a Powered Lower-Limb Prosthesis Using Fuzzy-Based Dynamic Movement Primitives

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