Path planning for coherent and persistent groups

Huang, Tianyu; Kapadia, Mubbasir; Badler, Norman I.; Kallmann, Marcelo

doi:10.1109/icra.2014.6907073

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…This profusion of pattern formation in biology has inspired a commensurate interest in robotics, yielding a growing literature on group coordination behaviors [61]- [64] motivated by the intuition that the heterogeneous action and sensing abilities of a group of robots might enable a comparably diverse range of complex tasks beyond the capabilities of a single individual. For example, group coordination via splitting and merging behaviours creates effective strategies for obstacle avoidance [65], congestion control [35], shepherding [66], area exploration [66], [67], and maintaining persistent and coherent groups while adapting to the environment [64]. In almost all of the robotics work in this area, formation tasks are given based upon rigid specifications taking either the form of explicit formation or relative distance graphs, with few exceptions including the "shape" abstraction of [34] or applications in unknown environments such as area coverage and exploration [68].…”

Section: B the Use Of Hierarchies As Organizational Modelsmentioning

confidence: 99%

Coordinated Robot Navigation via Hierarchical Clustering

2016

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Abstract-We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of hierarchical clustering method. These constructions yield computationally effective vector field planners for both hierarchically invariant as well as transitional navigation in the configuration space. We apply these methods to the centralized coordination and control of n perfectly sensed and actuated Euclidean spheres in a d-dimensional ambient space (for arbitrary n and d). Given a desired configuration supporting a desired hierarchy, we construct a hybrid controller which is quadratic in n and algebraic in d and prove that its execution brings all but a measure zero set of initial configurations to the desired goal with the guarantee of no collisions along the way.

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Section: B the Use Of Hierarchies As Organizational Modelsmentioning

confidence: 99%

Coordinated Robot Navigation via Hierarchical Clustering

2016

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“…Section 4.3 then demonstrates the use of planning for interactive narrative. Additional applications showcasing the use of planners for complex problem domains include footstep-based planning for crowds [Singh et al 2011], path planning for coherent and persistent groups [Huang et al 2014], and multi-actor behavior synthesis [Kapadia et al 2011].…”

Section: Planning Techniques For Character Animationmentioning

confidence: 99%

Geometric and discrete path planning for interactive virtual worlds

Kallmann

Kapadia

2016

ACM SIGGRAPH 2016 Courses

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“…It assigns a leader to each group, and it handles group-coordination strategies based on common ground theory. Huang et al [22] present a path-planning method to simulate coherent and persistent groups. It is based on the Local Clearance Triangulation by Kallmann [10], and it handles groups as deformable shapes.…”

Section: Related Workmentioning

confidence: 99%

Towards social behavior in virtual-agent navigation

Kremyzas

Jaklin²,

Gerærts³

2016

Sci. China Inf. Sci.

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We present Social Groups and Navigation (SGN), a method to simulate the walking behavior of small pedestrian groups in virtual environments. SGN is the first method to simulate group behavior on both global and local levels of an underlying planning hierarchy. We define quantitative metrics to measure the coherence and the sociality of a group based on existing empirical data of real crowds. SGN does not explicitly model coherent and social formations, but it lets such formations emerge from simple geometric rules. In addition to a previous version, SGN also handles group-splitting to smaller groups throughout navigation as well as social subgroup behavior whenever a group has to temporarily split up to re-establish its coherence. For groups of four, SGN generates between 13% and 53% more socially-friendly behavior than previous methods, measured over the lifetime of a group in the simulation. For groups of three, the gain is between 15% and 31%, and for groups of two, the gain is between 1% and 4%. SGN is designed in a flexible way, and it can be integrated into any crowd-simulation framework that handles global path planning and any path following as separate steps. Experiments show that SGN enables the simulation of thousands of agents in real time.

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Path planning for coherent and persistent groups

Cited by 12 publications

References 21 publications

Coordinated Robot Navigation via Hierarchical Clustering

Coordinated Robot Navigation via Hierarchical Clustering

Geometric and discrete path planning for interactive virtual worlds

Towards social behavior in virtual-agent navigation

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