Robot Motion Planning Integrating Planning Strategies and
Learning Methods  Luca Maria Gambardella and Cristina Versino
Robot motion planning in a dynamic cluttered workspace requires
the capability of dealing with obstacles and deadlock
situations. The paper analyzes situations where the robot is
considered with its shape and size and it can only perceive the
space through its local sensors. The robot explores the space
using a planner based on an artificial potential field and
incrementally learns a fast way to escape or prevent deadlock
situations using a combination of sensor perceptions, field
information and planner experience. The knowledge acquired is a
high-level network useful for avoiding deadlock areas consisting
of local minimum nodes, backtracking nodes and sub-goal nodes.