Abstract
Biological systems achieve amazing adaptive behavior with local agents performing simple sensing and actions. Modular robots with similar properties can potentially achieve self-adaptation tasks robustly. Inspired by this principle, we present a generalized distributed consensus framework for self-adaptation tasks in modular robotics. We demonstrate that a variety of modular robotic systems and tasks can be formulated within such a framework, including (1) an adaptive column that can adapt to external force, (2) a modular gripper that can manipulate fragile objects, and (3) a modular tetrahedral robot that can locomote towards a light source. We also show that control algorithms derived from this framework are provably correct. In real robot experiments, we demonstrate that such a control scheme is robust towards real world sensing and actuation noise. This framework can potentially be applied to a wide range of distributed robotics applications.
Highlights
In nature, biological systems gain a tremendous advantage by using vast numbers of simple and independent agents to collectively achieve group behaviors
We demonstrate that a variety of modular robotic systems and tasks can be formulated within such a framework, including (1) an adaptive column that can adapt to external force, (2) a modular gripper that can manipulate fragile objects, and (3) a modular tetrahedral robot that can locomote towards a light source
This has inspired the area of modular robotics: a class of robotic systems composed of many independent, connected, programmable modules that coordinate among themselves to achieve desired tasks
Summary
Biological systems gain a tremendous advantage by using vast numbers of simple and independent agents to collectively achieve group behaviors. We propose a generalized distributed consensus control framework We extend such a control scheme in several directions and demonstrate how this generalization allows many new application areas in modular robotics. We show that a modular gripper with this local condition is capable of grasping a fragile object using distributed sensing and actuation (Fig. 1 (b)) We extend this approach to a more complicated task: We equip modules in a tetrahedral robot with light and pressure sensors and formulate the robot’s locomotion as a sequence of selfadaptations. We show that the robot is able to locomote towards the light source with a series of “pressure consensus” reaching processes (Fig. 1 (c-d)) This shows that our framework is potentially applicable to other dynamic tasks.
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