Tag Archives: Swarm Robotics

Perpetual power for small robots in a swarm

Farshad Arvin, Simon Watson, Ali Emre Turgut, Jose Espinosa, Tomáš Krajník, Barry Lennox, Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging, Journal of Intelligent & Robotic Systems, December 2018, Volume 92, Issue 3–4, pp 395–412, DOI: 10.1007/s10846-017-0673-8.

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, re-charging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-the-fly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system’s ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model’s accuracy and demonstrated the system’s ability to support perpetual operation of multi-robotic system.

Distributing a neural network among the robots of a swarm

Michael Otte, An emergent group mind across a swarm of robots: Collective cognition and distributed sensing via a shared wireless neural network, The International Journal of Robotics Research, DOI: 10.1177/0278364918779704.

We pose the “trained-at-runtime heterogeneous swarm response problem,” in which a swarm of robots must do the following three things: (1) Learn to differentiate between multiple classes of environmental feature patterns (where the feature patterns are distributively sensed across all robots in the swarm). (2) Perform the particular collective behavior that is the appropriate response to the feature pattern that the swarm recognizes in the environment at runtime (where a collective behavior is defined by a mapping of robot actions to robots). (3) The data required for both (1) and (2) is uploaded to the swarm after it has been deployed, i.e., also at runtime (the data required for (1) is the specific environmental feature patterns that the swarm should learn to differentiate between, and the data required for (2) is the mapping from feature classes to swarm behaviors). To solve this problem, we propose a new form of emergent distributed neural network that we call an “artificial group mind.” The group mind transforms a robotic swarm into a single meta-computer that can be programmed at runtime. In particular, the swarm-spanning artificial neural network emerges as each robot maintains a slice of neurons and forms wireless neural connections between its neurons and those on nearby robots. The nearby robots are discovered at runtime. Experiments on real swarms containing up to 316 robots demonstrate that the group mind enables collective decision-making based on distributed sensor data, and solves the trained-at-runtime heterogeneous swarm response problem. The group mind is a new tool that can be used to create more complex emergent swarm behaviors. The group mind also enables swarm behaviors to be a function of global patterns observed across the environment—where the patterns are orders of magnitude larger than the robots themselves.