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

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Mathematical Modelling and Methods for Load Balancing and Coordination of Multi-Robot Stations

The automotive industry is moving from mass production towards an individualized production, individualizing parts aims to improve product quality and to reduce costs and material waste. This thesis concerns aspects of load balancing and coordination of multi-robot stations in the automotive manufacturing industry, considering efficient algorithms required by an individualized production. The goal of the load balancing problem is to improve the equipment utilization. Several approaches for solving the load balancing problem are suggested along with details on mathematical tools and subroutines employed.Our contributions to the solution of the load balancing problem are fourfold. First, to circumvent robot coordination we construct disjoint robot programs, which require no coordination schemes, are flexible, admit competitive cycle times for several industrial instances, and may be preferred in an individualized production. Second, since solving the task assignment problem for generating the disjoint robot programs was found to be unreasonably time-consuming, we model it as a generalized unrelated parallel machine problem with set packing constraints and suggest a tailored Lagrangian-based branch-and-bound algorithm. Third, a continuous collision detection method needs to determine whether the sweeps of multiple moving robots are disjoint. We suggest using the maximum velocity of each robot along with distance computations at certain robot configurations to derive a function that provides lower bounds on the minimum distance between the sweeps. The lower bounding function is iteratively minimized and updated with new distance information; our method is substantially faster than previously developed methods. Fourth, to allow for load balancing of complex multi-robot stations we generalize the disjoint robot programs into sequences of such; for some instances this procedure provides a significant equipment utilization improvement in comparison with previous automated methods

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

Cooperative area surveillance strategies using multiple unmanned systems

Recently, the U.S. Department of Defense placed the technological development of intelligence, surveillance, and reconnaissance (ISR) tools at the top of its priority list. Area surveillance that takes place in an urban setting is an ISR tool of special interest. Unmanned aerial vehicles (UAVs) are ideal candidates to perform area surveillance because they are inexpensive and they do not require a human pilot to be aboard. Multiple unmanned systems increase the rate of information flow from the target region and maintain up to date information. The purpose of the research described in this dissertation is to develop and test a system that coordinates multiple UAVs on a wide area coverage surveillance mission. The research presented in this document implements a waypoint generator for multiple aerial vehicles that is especially suited for large area surveillance. The system chooses initial locations for the vehicles and generates a set of balanced sub-trees which cover the region of interest (ROI) for the vehicles. The sub-trees are then optimally combined to form a single minimal tree that spans the entire region. The system transforms the tree path into a series of waypoints suitable for the aerial vehicles. The output of the system is a set of waypoints for each vehicle assigned to the coverage task. Results from computer simulation and flight testing are presented.Ph.D.Committee Chair: Dr. George Vachtsevanos; Committee Member: Ayanna Howard; Committee Member: Dr. Thomas Michaels; Committee Member: Eric Johnson; Committee Member: Linda Will

A survey on multi-robot coverage path planning for model reconstruction and mapping

There has been an increasing interest in researching, developing and deploying multi-robot systems. This has been driven mainly by: the maturity of the practical deployment of a single-robot system and its ability to solve some of the most challenging tasks. Coverage path planning (CPP) is one of the active research topics that could benefit greatly from multi-robot systems. In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. We classified the topics into: viewpoints generation approaches; coverage planning strategies; coordination and decision-making processes; communication mechanism and mapping approaches. This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives

Distributed approaches for coverage missions with multiple heterogeneous UAVs for coastal areas.

This Thesis focuses on a high-level framework proposal for heterogeneous aerial, fixed wing teams of robots, which operate in complex coastal areas. Recent advances in the computational capabilities of modern processors along with the decrement of small scale aerial platform manufacturing costs, have given researchers the opportunity to propose efficient and low-cost solutions to a wide variety of problems. Regarding marine sciences and more generally coastal or sea operations, the use of aerial robots brings forth a number of advantages, including information redundancy and operator safety. This Thesis initially deals with complex coastal decomposition in relation with a vehicles’ on-board sensor. This decomposition decreases the computational complexity of planning a flight path, while respecting various aerial or ground restrictions. The sensor-based area decomposition also facilitates a team-wide heterogeneous solution for any team of aerial vehicles. Then, it proposes a novel algorithmic approach of partitioning any given complex area, for an arbitrary number of Unmanned Aerial Vehicles (UAV). This partitioning schema, respects the relative flight autonomy capabilities of the robots, providing them a corresponding region of interest. In addition, a set of algorithms is proposed for obtaining coverage waypoint plans for those areas. These algorithms are designed to afford the non-holonomic nature of fixed-wing vehicles and the restrictions their dynamics impose. Moreover, this Thesis also proposes a variation of a well-known path tracking algorithm, in order to further reduce the flight error of waypoint following, by introducing intermediate waypoints and providing an autopilot parametrisation. Finally, a marine studies test case of buoy information extraction is presented, demonstrating in that manner the flexibility and modular nature of the proposed framework.Esta tesis se centra en la propuesta de un marco de alto nivel para equipos heterogéneos de robots de ala fija que operan en áreas costeras complejas. Los avances recientes en las capacidades computacionales de los procesadores modernos, junto con la disminución de los costes de fabricación de plataformas aéreas a pequeña escala, han brindado a los investigadores la oportunidad de proponer soluciones eficientes y de bajo coste para enfrentar un amplio abanico de cuestiones. Con respecto a las ciencias marinas y, en términos más generales, a las operaciones costeras o marítimas, el uso de robots aéreos conlleva una serie de ventajas, incluidas la redundancia de la información y la seguridad del operador. Esta tesis trata inicialmente con la descomposición de áreas costeras complejas en relación con el sensor a bordo de un vehículo. Esta descomposición disminuye la complejidad computacional de la planificación de una trayectoria de vuelo, al tiempo que respeta varias restricciones aéreas o terrestres. La descomposición del área basada en sensores también facilita una solución heterogénea para todo el equipo para cualquier equipo de vehículos aéreos. Luego, propone un novedoso enfoque algorítmico de partición de cualquier área compleja dada, para un número arbitrario de vehículos aéreos no tripulados (UAV). Este esquema de partición respeta las capacidades relativas de autonomía de vuelo de los robots, proporcionándoles una región de interés correspondiente. Además, se propone un conjunto de algoritmos para obtener planes de puntos de cobertura para esas áreas. Estos algoritmos están diseñados teniendo en cuenta la naturaleza no holonómica de los vehículos de ala fija y las restricciones que impone su dinámica. En ese sentido, esta Tesis también ofrece una variación de un algoritmo de seguimiento de rutas bien conocido, con el fin de reducir aún más el error de vuelo del siguiente punto de recorrido, introduciendo puntos intermedios y proporcionando una parametrización del piloto automático. Finalmente, se presenta un caso de prueba de estudios marinos de extracción de información de boyas, que demuestra de esa manera la flexibilidad y el carácter modular del marco propuesto