About this Research Topic
Swarm robotics takes inspiration from large populations of social animals to develop robotic behaviours that solve engineering problems. This field has many real world applications, ranging from infrastructure inspection to space exploration. As natural systems prove their efficiency in the real world every day, the appeal of these solutions, which have evolved over millions of year, is evident. However, swarm robots are subject to hard limitations, regarding both hardware and software. On one hand, the physical implementations of these systems rarely match the complexity of the beings they are modeled after, as robot designs have to be task-agnostic and to befit large-scale production. On the other hand, the natural behaviours that are emulated are often only superficially understood (according to a behaviourist input-output model), and border on the simplistic. Simple controllers are also necessary for the explainability and verification of emerging behaviours.
Rather than a deficiency, limitations can be advantageous and improve resilience. For example, key swarm robotics constraints such as locality and decentralisation (which are also enforced by limited sensors/actuators) are often said to promote robustness, scalability, and flexibility. In practice, these benefits are not always attested for in existing methods, and often have a limited scope. Understanding the potential causes and effects of such features is, however, key to efficiently deploying robots in the real world.
The goal of this Research Topic is thus to discover beneficial features enabled by constraints in swarm robotics. This issue will show that, in the same way that creative constraints promote innovation for researchers and artists, material constraints can drive robots towards new and more intelligent collective behaviours. This will enable a framework in which, rather than trying to overcome their swarms' constraints, researchers and engineers are free to embrace them.
We welcome contributions about the benefits of hardware constraints, and the software limit they impose, for swarm robotic control. Results should preferably include a comparison with/without a given constraints, to demonstrate how the latter benefits the system. Possible topics could include but are not limited to:
-Sensory limitations breeding cooperativity in automatic designs.
-Embodied evolution enabling adaptable behaviours.
-Embodied sensing and communication (with noise, packets dropping, and/or limited range) improving adaptation.
-Real robots performing better than their simulated counterpart.
-Design frameworks that exploit robots' limitations.
-Computation-less controllers.
-Morphological computation.
Rather than a deficiency, limitations can be advantageous and improve resilience. For example, key swarm robotics constraints such as locality and decentralisation (which are also enforced by limited sensors/actuators) are often said to promote robustness, scalability, and flexibility. In practice, these benefits are not always attested for in existing methods, and often have a limited scope. Understanding the potential causes and effects of such features is, however, key to efficiently deploying robots in the real world.
The goal of this Research Topic is thus to discover beneficial features enabled by constraints in swarm robotics. This issue will show that, in the same way that creative constraints promote innovation for researchers and artists, material constraints can drive robots towards new and more intelligent collective behaviours. This will enable a framework in which, rather than trying to overcome their swarms' constraints, researchers and engineers are free to embrace them.
We welcome contributions about the benefits of hardware constraints, and the software limit they impose, for swarm robotic control. Results should preferably include a comparison with/without a given constraints, to demonstrate how the latter benefits the system. Possible topics could include but are not limited to:
-Sensory limitations breeding cooperativity in automatic designs.
-Embodied evolution enabling adaptable behaviours.
-Embodied sensing and communication (with noise, packets dropping, and/or limited range) improving adaptation.
-Real robots performing better than their simulated counterpart.
-Design frameworks that exploit robots' limitations.
-Computation-less controllers.
-Morphological computation.
Keywords: Swarm robotics, Self-Organisation, Emergence, Resilience, Bio-inspiration
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
