Configuration Optimization and Advanced Docking Design for Two Classes of Reconfigurable Robotic Systems

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Modular and Reconfigurable Robots (MRR) and Modular and Self-Reconfigurable Robots (MSRR) offer a great economic advantage stemming from the potential of lowering the overall cost by building complex robotic structures from few rudimentary mass-produced modules. MRR technology can provide the manufacturing and automotive industries with flexible solutions to lower cost of the capital equipment and provide the much needed reusability feature. Furthermore, the ability of MSRR systems to efficiently reconfigure its morphology by rearranging the connectivity of its modules enables robots in this class to adapt to changes in the environment which makes such systems very attractive in applications related to surveillance, autonomous exploration, and search and rescue. The goal of this research program is to advance the state-of-the-art in design and utilization of MRR and MSRR, enabling wider scale integration of such systems in applications areas such as manufacturing, exploration, and military. To do so, the applicant has identified the following research objectives for the NSERC DG program being applied for that need to be tackled in order to realize this goal: (i) Development of task-based configuration optimization (TBCO) algorithm for serial-type MRR: this algorithm will utilize a multi-solution IK solver developed by the applicant to identify the most suitable MRR configuration for a given task. In order to boost the computational performance and speed of the TBCO solvers for MRR proposed by the applicant, a novel Memetic Algorithm (MA) will be developed as the core optimization algorithm, (ii) Development of advanced autonomous docking methods and pose optimization algorithms for MSRR in formation: When several mobile MSRRs are linked via dexterous interfaces, they can perform complex tasks through decentralized collaboration. However, in order to enhance the utility of MSRR, and in particular its subcategory referred to Mobile Configuration Change (MCC) class in applications related to exploration and navigation in unstructured environments, maneuverability, dexterity, and ability to navigate on uneven surfaces has to be significantly enhanced. Hence, another key objective of the proposed research is make significant advances towards this goal through: (1) development and experimental validation of a task-based genetic algorithm-based pose optimization algorithm for MSRRs linked in chained formation by dexterous serial arms. To validate our proposed task-based pose optimization algorithm, a formation of three mobile manipulators (existing at the applicant’s research lab) serially connected through their on-board mechanical manipulators will be utilized, and (2) development of a novel mechanism for autonomous docking of MSRR modules based on electromagnetic/permanent magnet male-female interfaces with passive spherical joint and an Extended Kalman Filter (EKF) estimation algorithm. The EKF is proposed for estimation of the relative position and orientation of two docking mobile modules which utilize IR sensors and wheel encoders in indoor applications. Mobile manipulators in the applicant’s research lab will be retrofitted with the proposed docking mechanism and the process will be validated experimentally. For outdoor applications, the methodology will be extended by using a camera and LED markers instead of IR sensing. The applicant has a proven record, capable team, and state-of-the-art laboratory to succeed in accomplishing the above-mentioned research objectives in order to enable stakeholder researchers and industries to reap the benefits of such modular engineering systems.