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This document is a thesis on the subject of single-agent on-line pathplanning in continuous,unpredictable and highly dynamic environments. Theproblem is finding and traversing a collision-free path for a holonomic robot,without kinodynamic restrictions, moving in an environment with severalunpredictably moving obstacles or adversaries. The availability of perfectinformation of the environment at all times is assumed.  Several static and dynamic variants of the Rapidly Exploring Random Trees(RRT) algorithm are explored, as well as an evolutionary algorithm for planningin dynamic environments called the Evolutionary Planner/Navigator. Acombination of both kinds of algorithms is proposed to overcome shortcomings inboth, and then a combination of a RRT variant for initial planning and informedlocal search for navigation, plus a simple greedy heuristic for optimization.We show that this combination of simple techniques provides better responses tohighly dynamic environments than the RRT extensions.

Single-Agent On-line Path Planning in Continuous, Unpredictable and Highly Dynamic Environments