Biologically inspired ant colony simulation

Self Cite

Insect swarms are common phenomena in nature and therefore have been actively pursued in computer animation. Realistic insect swarm simulation is difficult due to two challenges: high-fidelity behaviors and large scales, which make the simulation practice subject to laborious manual work and excessive trial-and-error processes. To address both challenges, we present a novel data-driven framework, FASTSWARM, to model complex behaviors of flying insects based on real-world data and simulate plausible animations of flying insect swarms. FASTSWARM has a linear time complexity and achieves real-time performance for large swarms. The high-fidelity behavior model of FASTSWARM explicitly takes into consideration the most common behaviors of flying insects, including the interactions among insects such as repulsion and attraction, self-propelled behaviors such as target following and obstacle avoidance, and other characteristics such as random movements. To achieve scalability, an energy minimization problem is formed with different behaviors modeled as energy terms, where the minimizer is the desired behavior. The minimizer is computed from the real-world data, which ensures the plausibility of the simulation results. Extensive simulation results and evaluations show that FASTSWARM is versatile in simulating various swarm behaviors, high fidelity measured by various metrics, easily controllable in inducing user controls and highly scalable.

Section: Related Workmentioning

confidence: 99%

FASTSWARM: A data‐driven framework for real‐time flying insect swarm simulation

Wei

Yao

Wang

et al. 2020

Self Cite

“…Transportation techniques have been proposed for objects of different shapes, such as arbitrary two‐dimensional convex shape, long rod shape, box shape, and disc shape . In computer animation, collaboration and cooperative behaviors are simulated, such as agents pushing boxes in crowd using collision probability fields, collective transport of ants, and ant colony . To successfully transport a passive object, agents should apply appropriate forces or move in proper speed to maneuver the passive object.…”

Section: Related Workmentioning

confidence: 99%

“…23 In computer animation, collaboration and cooperative behaviors are simulated, such as agents pushing boxes in crowd using collision probability fields, 13 collective transport of ants, 24 and ant colony. 25 To successfully transport a passive object, agents should apply appropriate forces or move in proper speed to maneuver the passive object.…”

Section: Related Workmentioning

confidence: 99%

Interaction with proactive and reactive agents in box manipulation tasks in virtual environments

Liu

Volonte

Hsu

et al. 2019

This paper studies the user collaboration experience with proactive and reactive agents in transporting boxes in virtual environments. Two main characters, the avatar and the agent, are controlled by a user and a controller, respectively. The user and the agent communicate with each other by voice. The agent can be proactive or reactive. The user follows the instruction issued by the proactive agent, whereas the user instructs the reactive agent to perform actions. The goal is to transport boxes to goal positions with orientation constraints. We conducted a user study to analyze the behaviors of participants in several aspects, including task completion time, path length, control experience, and co‐presence experience. We report our findings and make suggestions for future development.

“…3,4 Multiple characters performing collaboration behaviors enrich animation content. Techniques for collaboration behaviors of characters are investigated in computer animation 5 and robotics. 6,7 Pushing is a nonprehensile manipulation for transporting objects.…”

Section: Introductionmentioning

confidence: 99%

Generation of multiagent animation for object transportation using deep reinforcement learning and blend‐trees

Chen

Liu

Wong

2021

This paper proposes a framework that integrates reinforcement learning and blend-trees to generate animation of multiple agents for object transportation. The main idea is that in the learning stage, policies are learned to control agents to perform specific skills, including navigation, pushing, and orientation adjustment. The policies determine the blending parameters of the blend-trees to achieve locomotion control of the agents. In the simulation stage, the policies are combined to control the agents to navigate, push objects, and adjust orientation of the objects. We demonstrated several examples to show that the framework is capable of generating animation of multiple agents in different scenarios.