Multi-robot plan adaptation by constrained minimal distortion feature mapping

We propose a novel method for multi-robot plan adaptation which can be used for adapting existing spatial plans of robotic teams to new environments or imitating collaborative spatial teamwork of robots in novel situations. The algorithm selects correspondences between previous and current spatial features by the application of pairwise constraints, and generates the transformation function with a fast regular grid approximation which minimizes distortion. The algorithm requires minimal domain knowledge, is capable of transforming the spatial aspects of collaborative team behavior and performs better in noisy problems with large displacements than the most generally used quadratic differences method. The algorithm can be utilized for rapid plan adaptation, plan generalization or team behavior imitation. Methods are demonstrated on a multi-robot control problem in a random environment.