The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage, but has never been experimentally demonstrated. For the first time, we present a system that integrates perception, high-level mission planning, and modular robot hardware, allowing a modular robot to autonomously reconfigure in response to an a priori unknown environment in order to complete high-level tasks. Three hardware experiments validate the system, and demonstrate a modular robot autonomously exploring, reconfiguring, and manipulating objects to complete high-level tasks in unknown environments. We present system architecture, software and hardware in a general framework that enables modular robots to solve tasks in unknown environments using autonomous, reactive reconfiguration. The physical robot is composed of modules that support multiple robot configurations. An onboard 3D sensor provides information about the environment and informs exploration, reconfiguration decision making and feedback control. A centralized high-level mission planner uses information from the environment and the user-specified task description to autonomously compose low-level controllers to perform locomotion, reconfiguration, and other behaviors. A novel, centralized self-reconfiguration method is used to change robot configurations as needed.
Other work has focused on reconfiguration planning. present a system in which self-reconfigurable modular boats self-assemble into prescribed floating structures, such as a bridge @cite_6 . Individual boat modules are able to move about the pool, allowing for rapid reconfiguration. In these experiments, the environment is known and external localization is provided by an overhead AprilTag system.
@cite_6
1984426410
We present the methodology, algorithms, system design, and experiments addressing the self-assembly of large teams of autonomous robotic boats into floating platforms. Identical self-propelled robotic boats autonomously dock together and form connected structures with controllable variable stiffness. These structures can self-reconfigure into arbitrary shapes limited only by the number of rectangular elements assembled in brick-like patterns. An @math complexity algorithm automatically generates assembly plans which maximize opportunities for parallelism while constructing operator-specified target configurations with @math components. The system further features an @math complexity algorithm for the concurrent assignment and planning of trajectories from @math free robots to the growing structure. Such peer-to-peer assembly among modular robots compares favorably to a single active element assembling passive components in terms of both construction rate and potential robustness through redundancy. We describe hardware and software techniques to facilitate reliable docking of elements in the presence of estimation and actuation errors, and we consider how these local variable stiffness connections may be used to control the structural properties of the larger assembly. Assembly experiments validate these ideas in a fleet of 0.5 m long modular robotic boats with onboard thrusters, active connectors, and embedded computers.