Algorithms for Modular Self-reconfigurable Robots: Decision Making, Planning, and Learning

Modular self-reconfigurable robots (MSRs) are composed of multiple robotic modules which can change their connections with each other to take different shapes, commonly known as configurations. Forming different configurations helps the MSR to accomplish different types of tasks in different environments. In this dissertation, we study three different problems in MSRs: partitioning of modules, configuration formation planning and locomotion learning, and we propose algorithmic solutions to solve these problems. Partitioning of modules is a decision-making problem for MSRs where each module decides which partition or team of modules it should be in. To find the best set of partitions is a NP-complete problem. We propose game theory based both centralized and distributed solutions to solve this problem. Once the modules know which set of modules they should team-up with, they self-aggregate to form a specific shaped configuration, known as the configuration formation planning problem. Modules can be either singletons or connected in smaller configurations from which they need to form the target configuration. The configuration formation problem is difficult as multiple modules may select the same location in the target configuration to move to which might result in occlusion and consequently failure of the configuration formation process. On the other hand, if the modules are already in connected configurations in the beginning, then it would be beneficial to preserve those initial configurations for placing them into the target configuration as disconnections and re-connections are costly operations. We propose solutions based on an auction-like algorithm and (sub) graph-isomorphism technique to solve the configuration formation problem. Once the configuration is built, the MSR needs to move towards its goal location as a whole configuration for completing its task. If the configuration’s shape and size is not known a priori, then planning its locomotion is a difficult task as it needs to learn the locomotion pattern in dynamic time – the problem is known as adaptive locomotion learning. We have proposed reinforcement learning based fault-tolerant solutions for locomotion learning by MSRs

Simultaneous Configuration Formation and Information Collection By Modular Robotic Systems

We study a central problem in modular self-reconfigurable robots - the configuration formation problem - given a set of modules initially distributed arbitrarily within the environment and a desired target configuration involving those modules, how can each module select an appropriate spot or location in the target configuration to move to, so that, after reaching the position, it can readily connect with adjacent modules and form the shape of the desired target configuration. We study the additional navigation criterion for information collection while forming configurations - the modules have to select their navigation paths so that they can increase the amount of information they collect using their on-board sensors, while they are moving towards their positions in the target configuration. Information that the modules can collect from the environment can be of different types such as temperature measurement, algae sample collection, rock/soil sample collection etc. depending on the available on-board sensors of the modules. Each module has a limited energy budget to expend while moving from its initial to goal location. To solve this problem, we propose a budget-limited, heuristic search-based algorithm that finds a path that maximizes the entropy of the expected information along the path. We have analytically proved that our proposed approach converges within finite time. Our experimental results show that our planning approach has lower run-time and fewer messages exchanged than an auction-based allocation algorithm for selecting modules’ spots