Abstract
A major goal of autonomous robot collectives is to robustly perform complex tasks in unstructured environments by leveraging hardware redundancy and the emergent ability to adapt to perturbations. In such collectives, large numbers is a major contributor to system-level robustness. Designing robot collectives, however, requires more than isolated development of hardware and software that supports large scales. Rather, to support scalability, we must also incorporate robust constituents and weigh interrelated design choices that span fabrication, operation, and control with an explicit focus on achieving system-level robustness. Following this philosophy, we present the first iteration of a new framework toward a scalable and robust, planar, modular robot collective capable of gradient tracking in cluttered environments. To support co-design, our framework consists of hardware, low-level motion primitives, and control algorithms validated through a kinematic simulation environment. We discuss how modules made primarily of flexible printed circuit boards enable inexpensive, rapid, low-precision manufacturing; safe interactions between modules and their environment; and large-scale lattice structures beyond what manufacturing tolerances allow using rigid parts. To support redundancy, our proposed modules have on-board processing, sensing, and communication. To lower wear and consequently maintenance, modules have no internally moving parts, and instead move collaboratively via switchable magnets on their perimeter. These magnets can be in any of three states enabling a large range of module configurations and motion primitives, in turn supporting higher system adaptability. We introduce and compare several controllers that can plan in the collective's configuration space without restricting motion to a discrete occupancy grid as has been done in many past planners. We show how we can incentively redundant connections to prevent single-module failures from causing collective-wide failure, explore bad configurations which impede progress as a result of the motion constraints, and discuss an alternative “naive” planner with improved performance in both clutter-free and cluttered environments. This dedicated focus on system-level robustness over all parts of a complete design cycle, advances the state-of-the-art robots capable of long-term exploration.
Highlights
Modular self-reconfigurable robots are composed of active modules capable of rearranging their connection topology to adapt to dynamic environments, changing task settings, and partial failures (Yim et al, 2007)
Other operation-specific considerations include the ability of modules to operate, sense, and perceive independently from others; the ability to stay connected without continuous use of power; the ability of modules to move in a multitude of ways to overcome partial failures; and the potential to lower mechanical wear by omitting internally moving parts. All of these design choices warrant custom controllers and to support system robustness, we focus on (1) reactive behaviors that could adapt to dynamic perturbations; (2) naive and simple control schemes that scale well with the number of robots; (3) minimum energy expenditure through efficient path planners; (4) connection redundancy to avoid single module failures from causing complete collective failure; and (5) enabling a large configuration space that facilitates system adaptability to unforeseen perturbations
We find that while with a value of C = 0.7 and α = 0 is an admissible heuristic for local optimization, it results in chain-like configurations, which are more susceptible to temporary live lock
Summary
Modular self-reconfigurable robots are composed of active modules capable of rearranging their connection topology to adapt to dynamic environments, changing task settings, and partial failures (Yim et al, 2007). Isolated efforts to develop scalable control and hardware do not necessarily result in system-level robustness. To facilitate large numbers of robots in the first place, we argue for the importance of incorporating robustness into all levels of design, and demonstrate how this approach leads to tightly co-dependent parameters across hardware and software. We discuss our design approach, an early hardware prototype, and custom controllers. Our focus is explicitly on enabling long-term robustness of an autonomous, self-reconfigurable, modular robot through a hardware-software design cycle, with the idea that we can build on such a robust platform in the future to achieve more advanced behaviors
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