The development of novel autonomous underwater gliders has been hindered by limited shape diversity, primarily due to the reliance on traditional design tools that depend heavily on manual trial and error. Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions. In this work, we introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes. Our approach involves an algorithm that cooptimizes both shape and control signals, utilizing a reducedorder geometry representation and a differentiable neural-network-based fluid surrogate model. This end-to-end design workflow facilitates rapid iteration and evaluation of hydrodynamic performance, leading to the discovery of optimal and complex hull shapes across various control settings. We validate our method through wind tunnel experiments and swimming pool gliding tests, demonstrating that our computationally designed gliders surpass manually designed counterparts in terms of energy efficiency. By addressing challenges in efficient shape representation and neural fluid surrogate models, our work paves the way for the development of highly efficient underwater gliders, with implications for long-range ocean exploration and environmental monitoring.

This paper outlines the design, specifications, and algorithms for a new modular self-reconfigurable robotic system; at its foundation is a novel modular magnetically geared linear actuator paired with a kinematic coupling connector. Motivating this work is the core idea that high performance actuators as well as inexpensive, precise and repeatable connectors are the key ingredients required for useful real-world self-reconfiguring machines. This work builds upon existing research in the areas of modular self-reconfigurable robots, magnetic lead screws, modular machine tools and kinematic couplings. Magnetic lead screws (MLS) have many desirable characteristics applicable to modular robots, including a tolerance for slight misalignments, high efficiency, zero backlash, robustness, inherent series elasticity, high force capability, and the ability to gracefully separate and reattach. Due to their high mechanical efficiency, MLS actuators are able to be combined in parallel to provide for increased forces and stiffness. Our system implements a MLS through two separable elements: brushless motor powered actuators called carts which pair with modular passive tracks which constrain the carts' movement to a line. This paper also explores the design for a connector which is able to precisely align modules through the use of a 4-way symmetric kinematic coupling.

This paper presents a mobile UVC disinfection robot designed to mitigate the threat of airborne and surface pathogens. Our system comprises a mobile robot base, a custom UVC lamp assembly, and algorithms for autonomous navigation and path planning. We present a model of UVC disinfection and dosage of UVC light delivered by the mobile robot. We also discuss challenges and prototyping decisions for rapid deployment of the robot during the COVID-19 pandemic. Experimental results summarize a long-term deployment at The Greater Boston Food Bank, where the robot delivers (nightly) UVC dosages of at least 10 mJ/cm2to a 4000 ft2area in under 30 minutes. These dosages are capable of neutralizing 99% of coronaviruses, including SARS-CoV-2, on surfaces and in airborne particles. Further simulations present how this mobile UVC disinfection robot may be extended to classic problems in robotic path planning and adaptive multi-robot coverage control.

This paper presents a decentralized control frame-work for lattice-based Modular Self-Reconfigurable Robots (MSRR) which utilizes a novel magnetic fiducial system to facilitate neighbor identification and to enable algorithms which promise scalable functionality for systems with many modules. In this system individual modules autonomously follow simple behaviors while periodically accepting input from a centralized controller. This system is demonstrated with three initial behaviors: (1) Path following: modules follow a three dimensional path based on magnetic fiducial tags embedded in their neighbors, (2) Line formation: modules transform from a 3D structure into a line following a partially decentralized control algorithm, and (3) Light gradient aggregation: the formation of a group of modules guided by a global stimulus (i.e. visible light). This paper provides details of the neighbor identification system, introduces the three behaviors and presents the results of physical experiments performed with a system of twelve 3D M-Block robotic modules.

We present a distributed control strategy for the aggregation of multiple modular robots into one connected structure optimized for use with 3D modular pivoting cube robots such as the 3D M-Blocks [1]. We use the intensity from a light source as input to a decentralized control algorithm that drives the robots together. We describe the algorithm, give provable guarantees on convergence, and discuss experiments carried out in simulation and with a hardware platform of ten 3D M-Blocks modules. In this paper we contribute provably correct algorithms for the aggregation of generic modular robots; we show how these algorithms can be applied on real hardware by evaluating them on the 3D M-Blocks platform.

This paper presents the mechanical design of a modular robot called the 3D M-Block, a 50mm cubic module capable of both independent and lattice-based locomotion. The first M-Blocks described in [1] could pivot about one axis of rotation only. In contrast, the 3D M-blocks can exert on demand both forward and backward torques about three orthogonal axes, for a total of six directions. The 3D M-Blocks transform these torques into pivoting motions which allow the new 3D M-Blocks to move more freely than their predecessors. Individual modules can employ pivoting motions to independently roll across a wide variety of surfaces as well as to join and move relative to other M-Blocks as part of a larger collective structure. The 3D M-Block maintains the same form factor and magnetic bonding system as the one-dimensional M-Blocks [1], but a new fabrication process supports more efficient and precise production. The 3D M-blocks provide a robust and capable modular self-reconfigurable robotic platform able to support swarm robot applications through individual module capabilities and self-reconfiguring robot applications using connected lattices of modules.

In this paper, we present algorithms for self-reconfiguration of modular robots that move by pivoting. The modules are cubes that can pivot about their edges along the x̂, ŷ, or ẑ axes to move on a 3-dimensional substrate. This is a different model from prior work, which usually considers modules that slide along their faces. We analyze the pivoting cube model and give sufficient conditions for reconfiguration to be feasible. In particular, we show that if an initial configuration does not contain any of three subconfigurations, which we call rules, then it can reconfigure into a line. We provide provably correct algorithms for reconfiguration for both 2-D and 3-D systems, and we verify our algorithms via simulation on randomly generated 2-D and 3-D configurations.

We present an automated assembly system that directs the actions of a team of heterogeneous robots in the completion of an assembly task. From an initial user-supplied geometric specification, the system applies reasoning about the geometry of individual parts in order to deduce how they fit together. The task is then automatically transformed to a symbolic description of the assembly-a sort of blueprint. A symbolic planner generates an assembly sequence that can be executed by a team of collaborating robots. Each robot fulfills one of two roles: parts delivery or parts assembly. The latter are equipped with specialized tools to aid in the assembly process. Additionally, the robots engage in coordinated co-manipulation of large, heavy assemblies. We provide details of an example furniture kit assembled by the system.

In this paper, we describe a novel self-assembling, self-reconfiguring cubic robot that uses pivoting motions to change its intended geometry. Each individual module can pivot to move linearly on a substrate of stationary modules. The modules can use the same operation to perform convex and concave transitions to change planes. Each module can also move independently to traverse planar unstructured environments. The modules achieve these movements by quickly transferring angular momentum accumulated in a self-contained flywheel to the body of the robot. The system provides a simplified realization of the modular actions required by the sliding cube model using pivoting. We describe the principles, the unit-module hardware, and extensive experiments with a system of eight modules.