Robotic Specialization in Autonomous Robotic Structural Assembly

Robotic in-space assembly of large space structures is a long-term NASA goal to reduce launch costs and enable larger scale missions. Recently, researchers have proposed using discrete lattice building blocks and co-designed robots to build high-performance, scalable primary structure for various on-orbit and surface applications. These robots would locomote on the lattice and work in teams to build and reconfigure building-blocks into functional structure. However, the most reliable and efficient robotic system architecture, characterized by the number of different robotic 'species' and the allocation of functionality between species, is an open question. To address this problem, we decompose the robotic building-block assembly task into functional primitives and, in simulation, study the performance of the the variety of possible resulting architectures. For a set consisting of five process types (move self, move block, move friend, align bock, fasten block), we describe a method of feature space exploration and ranking based on energy and reliability cost functions. The solution space is enumerated, filtered for unique solutions, and evaluated against energy and reliability cost functions for various simulated build sizes. We find that a 2 species system, dividing the five mentioned process types between one unit cell transport robot and one fastening robot, results in the lowest energy cost system, at some cost to reliability. This system enables fastening functionality to occupy the build front while reducing the need for that functional mass to travel back and forth from a feed station. Because the details of a robot design affect the weighting and final allocation of functionality, a sensitivity analysis was conducted to evaluate the effect of changing mass allocations on architecture performance. Future systems with additional functionalities such as repair, inspection, and others may use this process to analyze and determine alternative robot architectures

Heterogeneous Self-Reconfiguring Robotics: Ph.D. Thesis Proposal

Self-reconfiguring robots are modular systems that can change shape, or reconfigure, to match structure to task. They comprise many small, discrete, often identical modules that connect together and that are minimally actuated. Global shape transformation is achieved by composing local motions. Systems with a single module type, known as homogeneous systems, gain fault tolerance, robustness and low production cost from module interchangeability. However, we are interested in heterogeneous systems, which include multiple types of modules such as those with sensors, batteries or wheels. We believe that heterogeneous systems offer the same benefits as homogeneous systems with the added ability to match not only structure to task, but also capability to task. Although significant results have been achieved in understanding homogeneous systems, research in heterogeneous systems is challenging as key algorithmic issues remain unexplored. We propose in this thesis to investigate questions in four main areas: 1) how to classify heterogeneous systems, 2) how to develop efficient heterogeneous reconfiguration algorithms with desired characteristics, 3) how to characterize the complexity of key algorithmic problems, and 4) how to apply these heterogeneous algorithms to perform useful new tasks in simulation and in the physical world. Our goal is to develop an algorithmic basis for heterogeneous systems. This has theoretical significance in that it addresses a major open problem in the field, and practical significance in providing self-reconfiguring robots with increased capabilities

Soft Scalable Self-Reconfigurable Modular Cellbot

Hazardous environments such as disaster affected areas, outer space, and radiation affected areas are dangerous for humans. Autonomous systems which can navigate through these environments would reduce risk of life. The terrains in these applications are diverse and unknown, hence there is a requirement for a robot which can self-adapt its morphology and use suitable control to optimally move in the desired manner. Although there exist monolithic robots for some of these applications, such as the Curiosity rover for Mars exploration, a modular robot containing multiple simple units could increase the fault tolerance. A modular design also enables scaling up or down of the robot based on the current task, for example, scaling up by connecting multiple units to cover a wider area or scaling down to pass through a tight space.Taking bio-inspiration from cells, where – based on environmental conditions – cells come together to form different structures to carry out different tasks, a soft modular robot called Cellbot was developed which was composed of multiple units called ‘cells’. Tests were conducted to understand the cellbot movement over different frictional surfaces for different actuation functions, the number of cells connected in a line (1D), and the shapes formed by connecting cells in 2D. A simulation model was developed to test a large range of frictional values and actuation functions for different friction coefficients. Based on the obtained results, cells could be designed using a material with frictional properties lying in the optimal locomotion range. In other cases, where the application has diverse terrains, the number of connected units can be changed to optimise the robot locomotion. Initial tests were conducted using a ‘ball robot’, where the cellbot was designed using balls which touch ground to exploit friction and actuators to provide force to move the robot. The model was extended to develop, a ‘bellow robot’ which was fabricated using hyper-elastic bellows and employed pneumatic actuation. The amount of inflation of a cell and its neighbouring cells determined if the cell would touch the ground or be lifted up. This was used to change cell behaviour where a cell could be touching ground to provide anchoring friction, or lifted to push or pull the cells and thereby move the robot. The cells were connected by magnets which could be disconnected and reconnected by morphing the robot body. The cellbot can thus reconfigure by changing the number of connected units or its shape. The easy detachment can be used to remove and replace damaged cells. Complex cellbot movements can be achieved by either switching between different robot morphologies or by changing actuation control.Future cellbots will be controlled remotely to change their morphology, control, and number of connected cells, making them suitable for missions which require fault tolerance and autonomous shape adaptation. The proposed cellbot platform has the potential to reduce the energy, time and costs in comparison to traditional robots and has potential for applications such as exploration missions for outer space, search and rescue missions for disaster affected areas, internal medical procedures, and nuclear decommissioning.<br/

Towards formal models and languages for verifiable Multi-Robot Systems

Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.Comment: Changed formattin

Recent developments in self-assembling multi-robot systems

Purpose of Review This review studies recent developments towards the physical design and control of self-assembling multi-robot systems. Recent Findings A wide range of novel robotic systems have been developed lately, for potential applications in terrestrial, aquatic, and aerospace environments. They increasingly make use of connectors which enable modules to join with each other at arbitrary points instead of discrete locations. Although the majority of contemporary algorithms are shape-driven, an increased focus on task-driven algorithms is observed. Summary Self-assembling multi-robot systems allow the same set of robots to adopt specific morphologies for different tasks. The requirements for robots to be able to connect to each other, locomote, and communicate have led to a wide range of physical designs realising different trade-offs. While algorithms are validated extensively in simulation, only a small portion are yet tested on real robotic platforms. Future research should investigate the real-world application of these systems, possibly aided by the introduction of standardised and open hardware

Integrated Reconfigurable Autonomous Architecture System

Advances in state-of-the-art architectural robotics and artificially intelligent design algorithms have the potential not only to transform how we design and build architecture, but to fundamentally change our relationship to the built environment. This system is situated within a larger body of research related to embedding autonomous agency directly into the built environment through the linkage of AI, computation, and robotics. It challenges the traditional separation between digital design and physical construction through the development of an autonomous architecture with an adaptive lifecycle. Integrated Reconfigurable Autonomous Architecture System (IRAAS) is composed of three components: 1) an interactive platform for user and environmental data input, 2) an agent-based generative space planning algorithm with deep reinforcement learning for continuous spatial adaptation, 3) a distributed robotic material system with bi-directional cyber-physical control protocols for simultaneous state alignment. The generative algorithm is a multi-agent system trained using deep reinforcement learning to learn adaptive policies for adjusting the scales, shapes, and relational organization of spatial volumes by processing changes in the environment and user requirements. The robotic material system was designed with a symbiotic relationship between active and passive modular components. Distributed robots slide their bodies on tracks built into passive blocks that enable their locomotion while utilizing a locking and unlocking system to reconfigure the assemblages they move across. The three subsystems have been developed in relation to each other to consider both the constraints of the AI-driven design algorithm and the robotic material system, enabling intelligent spatial adaptation with a continuous feedback chain