Velocity Obstacle Approaches for Multi-Agent Collision Avoidance

Douthwaite, James A.; Zhao, Shiyu; Mihaylova, Lyudmila

doi:10.1142/s2301385019400065

Cited by 54 publications

(23 citation statements)

References 21 publications

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“…Simulated humans and robots are set up in this environment. e simulated human is controlled by ORCA [25], and the sampling parameters obey the Gaussian distribution so as to achieve the diversity of behavioral data. We used circle crossing scenes for people in both training and testing; that is, the positions of all humans are randomly arranged and represented by a circle with a radius of 4 m, and their destinations are on the opposite of the starting position.…”

Section: Virtual Environment Construction and Parameter Settingmentioning

confidence: 99%

“…e linear velocity takes exponential values in (0, v pref ]; this allows the robot to take some of the more fine-grained operations when approaching the target; and the angular velocity takes 16 values uniformly in [0, 2π). [25] is a classic distributed underlying algorithm. It is local navigation, and its navigation target is around the individual, allowing the individual to avoid other individual targets and obstacles that are close to him.…”

Section: Virtual Environment Construction and Parameter Settingmentioning

confidence: 99%

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Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning

Zeng

Huang

et al. 2021

Mathematical Problems in Engineering

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Finding a feasible, collision-free path in line with social activities is an important and challenging task for robots working in dense crowds. In recent years, many studies have used deep reinforcement learning techniques to solve this problem. In particular, it is necessary to find an efficient path in a short time which often requires predicting the interaction with neighboring agents. However, as the crowd grows and the scene becomes more and more complex, researchers usually simplify the problem to a one-way human-robot interaction problem. But, in fact, we have to consider not only the interaction between humans and robots but also the influence of human-human interactions on the movement trajectory of the robot. Therefore, this article proposes a method based on deep reinforcement learning to enable the robot to avoid obstacles in the crowd and navigate smoothly from the starting point to the target point. We use a dual social attention mechanism to jointly model human-robot and human-human interaction. All sorts of experiments demonstrate that our model can make robots navigate in dense crowds more efficiently compared with other algorithms.

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Section: Virtual Environment Construction and Parameter Settingmentioning

confidence: 99%

Section: Virtual Environment Construction and Parameter Settingmentioning

confidence: 99%

Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning

Zeng

Huang

et al. 2021

Mathematical Problems in Engineering

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“…Jang et al [23] introduced a collaborative monocular SLAM using the rendezvous generated as multiple robots execute tasks. Douthwaite et al [24] analyzed several velocity-based multi-agent collision avoidance algorithms. Li and Zhou [25] proposed the slight-weight convolutional neural network as an end-to-end training method that can be applied to the visual scene recognition of a multi-agent system.…”

Section: Introductionmentioning

confidence: 99%

Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System

Moon

2021

Sensors

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As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment.

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“…less collisions). A critical analysis of common VO approaches is presented by Douthwaite et al [30]. In their analysis they find the ORCA model to scale the best out of the tested VO models, and also find ORCA provides the smoothest trajectories.…”

Section: Introductionmentioning

confidence: 99%

Simulating Crowds and Autonomous Vehicles

Charlton

Gonzalez

Maddock

et al. 2020

Lecture Notes in Computer Science

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Understanding how people view and interact with autonomous vehicles is important to guide future directions of research. One such way of aiding understanding is through simulations of virtual environments involving people and autonomous vehicles. We present a simulation model that incorporates people and autonomous vehicles in a shared urban space. The model is able to simulate many thousands of people and vehicles in real-time. This is achieved by use of GPU hardware, and through a novel linear program solver optimized for large numbers of problems on the GPU. The model is up to 30 times faster than the equivalent multi-core CPU model.

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Velocity Obstacle Approaches for Multi-Agent Collision Avoidance

Cited by 54 publications

References 21 publications

Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning

Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning

Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System

Simulating Crowds and Autonomous Vehicles

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