Aliah Majeb PhDv.2023-01 Sensing-Based Self-Reconfigurable Strategies for Autonomous Modular Robotic Systems Defense (link)
Dissertation is available here (link) Board of Examiners
AbstractModular robotic systems (MRSs) have become a highly active research today. It has the ability to change the perspective of robotic systems from machines designed to do certain tasks to multi- purpose tools capable of accomplishing almost any task. They are used in a wide range of applications, including reconnaissance, rescue missions, space exploration, military task, etc. Constantly, MRS is built of modules from a few to several hundreds or even thousands . Each module involves actuators, sensors, computational, and communicational capabilities. Usually, these systems are homogeneous where all the modules are identical; however, there could be heterogeneous systems that contain different modules to maximize versatility. One of the advantages of these systems is their ability to operate in harsh environments in which contemporary human-in-the-loop working schemes are risky, inefficient and sometimes infeasible. In this thesis, we are interested in self-reconfigurable modular robotics. In such systems, it uses a set of detectors in order to continuously sense its surroundings, locate its own position, and then transform to a specific shape to perform the required tasks. Consequently, MRS faces three major challenges. First, it offers a great amount of collected data that overloads the memory storage of the robot. Second it generates redundant data which complicates the decision making about the next morphology in the controller. Third, the self reconfiguration process necessitates massive communication between the modules to reach the target morphology and takes a significant processing time to self-reconfigure the robotic. Therefore, researchers’ strategies are often targeted to minimize the amount of data collected by the modules without considerable loss in fidelity. The goal of this reduction is first to save the storage space in the MRS, and then to facilitate analyzing data and making decision about what morphology to use next in order to adapt to new circumstances and perform new tasks. In this thesis, we propose an efficient mechanism for data processing and self-reconfigurable decision-making dedicated to modular robotic systems. More specifically, we focus on data storage reduction, self-reconfiguration decision-making, and efficient communication management between modules in MRSs with the main goal of ensuring fast self-reconfiguration process. First, we propose data storage reduction mechanism in order to eliminate redundant data thus, reducing the amount of data needed to be stored in the MRS. Our objective is to aggregate similar data while preserving the dynamicity of the monitored conditions. We study the similarity between detectors readings according to the aggregation approach, we use Sim function based on a predefined threshold; then we observe if the difference between readings is smaller than the threshold then the readings are considered similar. Otherwise, it was considered different. The second objective of this thesis is to allow MRS to take real-time decisions to decide which morphology, is the most suitable to the current surrounding status. for that reason we propose an efficient model based on the fuzzy logic. Typically, a fuzzy set consists of several elements where each of one has a degree of membership. Then we define a score table (ST) which is a customizable guide used by the robot controller in order to determine the criticality of each monitored condition. The main target of ST is to allow early recognition of events that alert the MRS to change its current configuration to a new one that is suitable to the surrounding. Assume we have a set of predefined events, where each of them imposes MRS to adapt itself to a unique morphology. Then, in order to check the accuracy of the selected morphology, we propose to calculate the strength of the occurred event. This will allow the MRS to periodically check if the current morphology is the most suitable for the monitored condition. The third objective of this thesis is to provide fast reconfiguration process and reduce the communication between modules in MRS. for that reason we propose a robust mechanism for self-reconfigurable robotics called RUN which consist of two stage. At the first stage, we classify the modules into clusters before performing their transitions. To do that, we first select a set of modules to act as cliques for the robotic. A clique module is responsible to allow an efficient communication between the whole modules during the self-reconfiguration process. On one hand, this will allow to reduce the number of messages transmitted between the modules and, on the other hand, to minimize the number of transitions made by each module (thus saving its energy). At the second stage, we propose two communication algorithms, the first algorithm is inter-module that allows efficient communication between the cliques of the clusters and aims to minimize the number of transmitted messages in the robotic system to avoid packet congestion and save the module energy, and the second algorithm is intra-module based on the tree structure that reduces the number of communications between the modules in the same clusters. Forth, we proposed a fast self-reconfiguration technique called FSET, dedicated to MRS. Our proposed technique consists mainly of two stages: root selection and morphology formation. The final goal of these stages is to enhance the time cost to get new morphology of the traditional SET algorithm thus, ensuring fast self-reconfiguration. The root selection stage 3 selects a small number of modules in order to find the best tree roots that affect the topological conditions and lead to the success of the embedding process or not. The morphology formation stage uses the traditional SET algorithm to calculate the embedding truth table where the initial roots used are taken from the first stage. To evaluate the performance of the proposed techniques, simulations on real robot have been conducted. We have analyzed their performances according to complexity, overhead communication, optimality (minimum number of steps), and time efficiency and we show how our techniques can significantly improve the performance of robotic systems through providing a fast reconfiguration process in MRS and reducing the energy consumption of modules. |