  In this paper a global reactive motion planning framework for robotic
manipulators in complex dynamic environments is presented. In particular, the
circular field predictions (CFP) planner from Becker et al. (2021) is extended
to ensure obstacle avoidance of the whole structure of a robotic manipulator.
Towards this end, a motion planning framework is developed that leverages
global information about promising avoidance directions from arbitrary
configuration space motion planners, resulting in improved global trajectories
while reactively avoiding dynamic obstacles and decreasing the required
computational power. The resulting motion planning framework is tested in
multiple simulations with complex and dynamic obstacles and demonstrates great
potential compared to existing motion planning approaches.
