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ClearPath: Highly Parallel Collision Avoidance for Multi-agent Simulation

Uploaded on Nov 1, 2009

We present a new local collision avoidance algorithm between multiple agents for real-time simulations. Our approach extends the notion of velocity obstacles from robotics and formulates the conditions for collision free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent. This resulting algorithm can be parallelized by exploiting data-parallelism and thread-level parallelism. The overall approach, ClearPath, is general and can robustly handle dense scenarios with tens or hundreds of thousands of heterogeneous agents in a few milliseconds. As compared to prior collision avoidance algorithms, we observe more than an order of magnitude performance improvement [Stephen. J. Guy, Jatin Chhugani, Changkyu Kim, Nadathur Satish, Ming C. Lin, Dinesh Manocha, and Pradeep Dubey].
 

Vessels / by Sofian Audry, Stephen Kelly and Samuel St-Aubin, 2013

Sofian Audry, Stephen Kelly, Samuel St-Aubin
In collaboration with Adam Kelly
Aquatic robotic installation
Vessels is a robotic installation consisting of large groups of autonomous water vehicles. With roughly 50 individuals in a population, the robotic agents interact with each other and their environment to form a simple ecosystem. Their collective, emergent behaviour resembles the social interactions in a community of living creatures. Observers may empathize with the robots’ behaviours, ascribe intentions and motivations to their actions, and/or draw correlations between the group dynamic and unseen characteristics of their milieu.
 

Heather Barnett: What humans can learn from semi-intelligent slime

Published on Jul 17, 2014

Inspired by biological design and self-organizing systems, artist Heather Barnett co-creates with Physarum polycephalum, a eukaryotic microorganism that lives in cool, moist areas. What can people learn from the semi-intelligent slime mold? Watch this talk to find out.
 
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