We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and block number, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.

This work presents the development of a brain computer interface as an alternative communication channel to be used in Robotics. It encompasses the implementation of an electroencephalograph (EEG), as well as the development of all computational methods and necessary techniques to identify mental activities. The developed brain computer interface (BCI) is applied to activate the movements of a 120lb mobile robot, associating four different mental activities to robot commands. The interface is based on EEG signal analyses, which extract features that can be classified as specific mental activities. First, a signal preprocessing is performed from the EEG data, filtering noise, using a spatial filter to increase the scalp signal resolution, and extracting relevant features. Then, different classifier models are proposed, evaluated and compared. At last, two implementations of the developed classifiers are proposed to improve the rate of successful commands to the mobile robot. In one of the implementations, a 91% average hit rate is obtained, with only 1.25% wrong commands after 400 attempts to control the mobile robot.