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.
Similarly, a great deal of work has been done to develop behaviors for MSRR. Many efforts focus on distributed control strategies, taking advantage of the distributed nature of MSRR hardware @cite_37 . Distributed strategies include central pattern generators @cite_5 and hormone-based control @cite_36 . Genetic algorithms have been used to automatically generate both modular robot designs and behaviors @cite_12 .
@cite_36 @cite_5 @cite_37 @cite_12
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This article addresses the problem of how modular robotics systems, i.e. systems composed of multiple modules that can be configured into different robotic structures, can learn to locomote. In particular, we tackle the problems of online learning, that is, learning while moving, and the problem of dealing with unknown arbitrary robotic structures. We propose a framework for learning locomotion controllers based on two components: a central pattern generator (CPG) and a gradient-free optimization algorithm referred to as Powell's method. The CPG is implemented as a system of coupled nonlinear oscillators in our YaMoR modular robotic system, with one oscillator per module. The nonlinear oscillators are coupled together across modules using Bluetooth communication to obtain specific gaits, i.e. synchronized patterns of oscillations among modules. Online learning involves running the Powell optimization algorithm in parallel with the CPG model, with the speed of locomotion being the criterion to be optimized. Interesting aspects of the optimization include the fact that it is carried out online, the robots do not require stopping or resetting and it is fast. We present results showing the interesting properties of this framework for a modular robotic system. In particular, our CPG model can readily be implemented in a distributed system, it is computationally cheap, it exhibits limit cycle behavior (temporary perturbations are rapidly forgotten), it produces smooth trajectories even when control parameters are abruptly changed and it is robust against imperfect communication among modules. We also present results of learning to move with three different robot structures. Interesting locomotion modes are obtained after running the optimization for less than 60 minutes. Aiming to real easy application in several quadruped robot platforms, this paper introduces a new method of modeling central pattern generators (CPG) to control quadruped locomotion. Not only can this new model generate all the primary gaits of quadrupeds stably with limit cycle effect, but it also has the ability of tuning the periodic outputs with arbitrary waveforms. The core idea is to combine strong points of two mathematical tools: Fourier series and Recurrent neural networks. In addition, a new biomimetic controller is also introduced using the proposed CPG model and several reflex modules. Finally, dynamic simulations are performed to validate the efficiency of the proposed controller. We present control strategies that implement planar microassembly using groups of stress-engineered MEMS microrobots (MicroStressBots) controlled through a single global control signal. The global control signal couples the motion of the devices, causing the system to be highly underactuated. In order for the robots to assemble into arbitrary planar shapes despite the high degree of underactuation, it is desirable that each robot be independently maneuverable (independently controllable). To achieve independent control, we fabricated robots that behave (move) differently from one another in response to the same global control signal. We harnessed this differentiation to develop assembly control strategies, where the assembly goal is a desired geometric shape that can be obtained by connecting the chassis of individual robots. We derived and experimentally tested assembly plans that command some of the robots to make progress toward the goal, while other robots are constrained to remain in small circular trajectories (orbits) until it is their turn to move into the goal shape. Our control strategies were tested on systems of fabricated MicroStressBots. The robots are 240-280 Âµm A 60 Âµm A 7-20 Âµm in size and move simultaneously within a single operating environment. We demonstrated the feasibility of our control scheme by accurately assembling five different types of planar microstructures. We present a decentralized, scalable approach to assembling a group of heterogeneous parts into different products using a swarm of robots. While the assembly plans are predetermined, the exact sequence of assembly of parts and the allocation of subassembly tasks to robots are determined by the interactions between robots in a decentralized fashion in real time. Our approach is based on developing a continuous abstraction of the system derived from models of chemical reactions and formulating the strategy as a problem of selecting rates of assembly and disassembly. These rates are mapped onto probabilities that determine stochastic control policies for individual robots, which then produce the desired aggregate behavior. This top-down approach to determining robot controllers also allows us to optimize the rates at the abstract level to achieve fast convergence to the specified target numbers of products. Because the method incorporates programs for assembly and disassembly, changes in demand can lead to reconfiguration in a seamless fashion. We illustrate the methodology using a physics-based simulator with examples involving 15 robots and two types of final products.