Autonomous Task-Based Evolutionary Design of Modular Robots

In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal design. Designing modular robotic systems faces two main challenges: the lack of basic rules of thumb and design bias introduced by human designers. The space of possible designs cannot be easily grasped by human designers especially for new tasks or tasks that are not fully understood by designers. Therefore, evolutionary computation is employed to design modular robots autonomously. Evolutionary algorithms can efficiently handle problems with discrete search spaces and solutions of variable sizes as these algorithms offer feasible robustness to local minima in the search space; and they can be parallelized easily to reducing system runtime. Moreover, they do not have to make assumptions about the solution form. This dissertation proposes a novel autonomous system for task-based modular robotic design based on evolutionary algorithms to search for the optimal design. The introduced system offers a flexible synthesis algorithm that can accommodate to different task-based design needs and can be applied to different modular shapes to produce homogenous modular robots. The proposed system uses a new representation for modular robotic assembly configuration based on graph theory and Assembly Incidence Matrix (AIM), in order to enable efficient and extendible task-based design of modular robots that can take input modules of different geometries and Degrees Of Freedom (DOFs). Robotic simulation is a powerful tool for saving time and money when designing robots as it provides an accurate method of assessing robotic adequacy to accomplish a specific task. Furthermore, it is difficult to predict robotic performance without simulation. Thus, simulation is used in this research to evaluate the robotic designs by measuring the fitness of the evolved robots, while incorporating the environmental features and robotic hardware constraints. Results are illustrated for a number of benchmark problems. The results presented a significant advance in robotic design automation state of the art

Reconfiguring Non-Convex Holes in Pivoting Modular Cube Robots

We present an algorithm for self-reconfiguration of admissible 3D configurations of pivoting modular cube robots with holes of arbitrary shape and number. Cube modules move across the surface of configurations by pivoting about shared edges, enabling configurations to reshape themselves. Previous work provides a reconfiguration algorithm for admissible 3D configurations containing no non-convex holes; we improve upon this by handling arbitrary admissible 3D configurations. The key insight specifies a point in the deconstruction of layers enclosing non-convex holes at which we can pause and move inner modules out of the hole. We prove this happens early enough to maintain connectivity, but late enough to open enough room in the enclosing layer for modules to escape the hole. Our algorithm gives reconfiguration plans with O(n^2) moves for n modules

Distributed Prediction of Unsafe Reconfiguration Scenarios of Modular Robotic Programmable Matter

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks. © 2004-2012 IEEE

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Connected Coordinated Motion Planning with Bounded Stretch

We consider the problem of connected coordinated motion planning for a large collective of simple, identical robots: From a given start grid configuration of robots, we need to reach a desired target configuration via a sequence of parallel, collision-free robot motions, such that the set of robots induces a connected grid graph at all integer times. The objective is to minimize the makespan of the motion schedule, i.e., to reach the new configuration in a minimum amount of time. We show that this problem is NP-complete, even for deciding whether a makespan of 2 can be achieved, while it is possible to check in polynomial time whether a makespan of 1 can be achieved. On the algorithmic side, we establish simultaneous constant-factor approximation for two fundamental parameters, by achieving constant stretch for constant scale. Scaled shapes (which arise by increasing all dimensions of a given object by the same multiplicative factor) have been considered in previous seminal work on self-assembly, often with unbounded or logarithmic scale factors; we provide methods for a generalized scale factor, bounded by a constant. Moreover, our algorithm achieves a constant stretch factor: If mapping the start configuration to the target configuration requires a maximum Manhattan distance of $d$, then the total duration of our overall schedule is $\mathcal{O}(d)$, which is optimal up to constant factors.Comment: 28 pages, 18 figures, full version of an extended abstract that appeared in the proceedings of the 32nd International Symposium on Algorithms and Computation (ISAAC 2021); revised version (more details added, and typing errors corrected