Abstract
Designing the individual robot rules that give rise to desired emergent swarm behaviors is difficult. The common method of running evolutionary algorithms off‐line to automatically discover controllers in simulation suffers from two disadvantages: the generation of controllers is not situated in the swarm and so cannot be performed in the wild, and the evolved controllers are often opaque and hard to understand. A swarm of robots with considerable on‐board processing power is used to move the evolutionary process into the swarm, providing a potential route to continuously generating swarm behaviors adapted to the environments and tasks at hand. By making the evolved controllers human‐understandable using behavior trees, the controllers can be queried, explained, and even improved by a human user. A swarm system capable of evolving and executing fit controllers entirely onboard physical robots in less than 15 min is demonstrated. One of the evolved controllers is then analyzed to explain its functionality. With the insights gained, a significant performance improvement in the evolved controller is engineered.
Highlights
Background and Previous WorkCommon approaches to engineering swarm behaviors include bioinspiration, evolution, reverse engineering, and hand design.[5,6,7,8,9,10] Controller architectures include neural networks, probabilistic finite state machines (FSM), behavior trees, and hybrid combinations.[11,12,13,14] See Francesca and Birattari for a recent review.[15]We use BT for our controller architecture because they are modular, human readable, and extendable
The distributed island model evolutionary algorithm running on the swarm
We have demonstrated a swarm that is capable of evolving new controllers within the swarm itself, removing the tie to off-line processing power
Summary
Common approaches to engineering swarm behaviors include bioinspiration, evolution, reverse engineering, and hand design.[5,6,7,8,9,10] Controller architectures include neural networks, probabilistic finite state machines (FSM), behavior trees, and hybrid combinations.[11,12,13,14] See Francesca and Birattari for a recent review.[15]. When controllers are discovered through evolution or other automatic methods using a simulated environment, the problem of transferability of the controller to real robots arises, the socalled reality gap Approaches to minimizing this include using high-fidelity simulation with periodic testing on real robots,[21,22] injection of noise within a simulation,[23] including transferability within the fitness function of the automatic method,[24,25] and reducing the representational power of the controller.[12] We apply a combination of techniques, injecting noise, minimizing the effect of problematic areas of simulation such as collisions by avoiding behaviors that give rise to them, and using the ability of behavior trees to encapsulate predesigned useful sub-behaviors. The time is ripe to move swarms into the real world
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