Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
AI Game Programming Wisdom
AI Game Programming Wisdom
Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming
Applied Intelligence
Decision-Theoretic Planning for Autonomous Robotic Surveillance
Applied Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Intuitive Crowd Behaviour in Dense Urban Environments using Local Laws
TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
Movement behavior for soldier agents on a virtual battlefield
Presence: Teleoperators and Virtual Environments - Fourth international workshop on presence
ACM SIGGRAPH 2006 Papers
Potential field method to navigate several mobile robots
Applied Intelligence
Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
Transportation Science
RTTES: Real-time search in dynamic environments
Applied Intelligence
Algorithm for computer control of a digital plotter
IBM Systems Journal
Communication constraints multi-agent territory exploration task
Applied Intelligence
Incremental 3D reconstruction using Bayesian learning
Applied Intelligence
Hi-index | 0.00 |
Navigation of a group of autonomous agents that are required to maintain a formation is a challenging task which has not been studied much especially in 3-D terrains. This paper presents a novel approach to collision free path finding of multiple agents preserving a predefined formation in 3-D terrains. The proposed method could be used in many areas like navigation of semi-automated forces (SAF) at unit level in military simulations and non-player characters (NPC) in computer games. The proposed path finding algorithm first computes an optimal path from an initial point to a target point after analyzing the 3-D terrain data from which it constructs a weighted graph. Then, it employs a real-time path finding algorithm specifically designed to realize the navigation of the group from one waypoint to the successive one on the optimal path generated at the previous stage, preserving the formation and avoiding collision. Software was developed to test the methods discussed here.