The advantage of modular self-reconfigurable robot systems is their flexibility, but this advantage can only be realized if appropriate configurations (shapes) and behaviors (controlling programs) can be selected for a given task. In this paper, we present an integrated system for addressing high-level tasks with modular robots, and demonstrate that it is capable of accomplishing challenging, multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) a high-level mission planner, (2) a large design library spanning a wide set of functionality, (3) a design and simulation tool for populating the library with new configurations and behaviors, and (4) modular robot hardware. This paper builds on earlier work by (in: Robotics: science and systems, 2016), extending the original system to include environmentally adaptive parametric behaviors, which integrate motion planners and feedback controllers with the system.
Modular self-reconfigurable robots (MSRR) systems have the potential for great advantage over traditional robot systems in scenarios where flexibility is required: for example, search-and-rescue scenarios where the environment and task requirements may not be well-known . Much of the existing research in MSRR systems has focused on establishing the fundamental capabilities that differentiate these systems from traditional robots. Notably, MSRR systems have demonstrated the ability to form a wide variety of physical morphologies capable of diverse modes of locomotion, suitable to a range of different terrains @cite_7 . The ability to autonomously reconfigure has been demonstrated @cite_34 , and a number of reconfiguration planning algorithms have been developed @cite_26 .
@cite_34 @cite_7 @cite_26
1566800662 1547984505
A unit-modular robot is a robot that is composed of modules that are all identical. In this thesis we study the design and control of unit-modular dynamically reconfigurable robots. This is based upon the design and construction of a robot called Polypod. We further choose statically stable locomotion as the task domain to evaluate the design and control strategy. The result is the creation of a number of unique locomotion modes. The exciting aspect about a modular robot like Polypod is that it does not only describe one robot, but also presents the building blocks from which many different types of robots can be formed. Dynamic reconfigurability adds a new dimension to the capabilities of the robot. To gain insight into these capabilities in the domain of locomotion, we first build a general, functional taxonomy of locomotion modes. We show that Polypod is capable of generating all classes of statically stable locomotion, a feature unique to Polypod. Next, we propose methods to evaluate vehicles under different operating conditions such as different terrain conditions. We then evaluate and compare each mode of locomotion on Polypod within each class. This study leads to interesting insights into the general characteristics of the corresponding classes of locomotion. Finally, since more modules are expected to increase robot capability, it is important to examine the limit to the number of modules that can be put together in a useful form. We answer this question by investigating the issues of structural stability, actuator strength, computation and control requirements. 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 x, y, or z 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.