Daniel Drew

Connor Mattson, Varun Raveendra, Ricardo Vega et al.

Given a swarm of limited-capability robots, we seek to automatically discover the set of possible emergent behaviors. Prior approaches to behavior discovery rely on human feedback or hand-crafted behavior metrics to represent and evolve behaviors and only discover behaviors in simulation, without testing or considering the deployment of these new behaviors on real robot swarms. In this work, we present Real2Sim2Real Behavior Discovery via Self-Supervised Representation Learning, which combines representation learning and novelty search to discover possible emergent behaviors automatically in simulation and enable direct controller transfer to real robots. First, we evaluate our method in simulation and show that our proposed self-supervised representation learning approach outperforms previous hand-crafted metrics by more accurately representing the space of possible emergent behaviors. Then, we address the reality gap by incorporating recent work in sim2real transfer for swarms into our lightweight simulator design, enabling direct robot deployment of all behaviors discovered in simulation on an open-source and low-cost robot platform.

Mark Selden, Jason Zhou, Felipe Campos et al.

Decentralized control in multi-robot systems is dependent on accurate and reliable communication between agents. Important communication factors, such as latency and packet delivery ratio, are strong functions of the number of agents in the network. Findings from studies of mobile and high node-count radio-frequency (RF) mesh networks have only been transferred to the domain of multi-robot systems to a limited extent, and typical multi-agent robotic simulators often depend on simple propagation models that do not reflect the behavior of realistic RF networks. In this paper, we present a new open source swarm robotics simulator, BotNet, with an embedded standards-compliant time-synchronized channel hopping (6TiSCH) RF mesh network simulator. Using this simulator we show how more accurate communications models can limit even simple multi-robot control tasks such as flocking and formation control, with agent counts ranging from 10 up to 2500 agents. The experimental results are used to motivate changes to the inter-robot communication propagation models and other networking components currently used in practice in order to bridge the sim-to-real gap.

Quinna Nguyen, C. Luke Nelson, Daniel S. Drew

Modern high-performance mobile electronics impose extreme constraints on thermal management, and traditional cooling methods often fail to meet requirements for power density, form factor, and durability. Jet impingement cooling offers a compelling solution but is typically hindered by the need for bulky ancillary hardware. Here, we demonstrate that compact arrays of reduced-scale electroaerodynamic (EAD) plasma actuators, which are silent, solid-state devices with no moving parts, can be used for direct jet impingement cooling of electronics. The main contribution is the first rigorous experimental demonstration and system-level validation of multi-stage, ducted electroaerodynamic jet arrays as a compact, fan-replacement impingement cooling solution for mobile electronics. We characterize the performance of both single- and multi-stage ducted actuators, including thermographic analysis of heat transfer coefficients and spatial cooling profiles. We also quantify the relationship between actuator stage count and cooling efficiency, showing that increasing the number of ion acceleration stages enhances jet velocity and heat transfer performance at a reduced efficiency. The actuators are then assembled into an array and directly compared to a conventional fan with similar coverage area, showing competitive performance at a fraction of the volume, weight, and power. Finally, we integrate the array onto a commercial edge AI system and show that thermal regulation during extended inference workloads matches that of a stock fan, without any moving mechanical components or noise. These results confirm that multi-stage EAD jet arrays are not only viable but advantageous for thermal management in mobile and high-performance systems, paving the way toward silent and miniaturized solid-state cooling solutions.

D. S. Drew, M. Devlin, E. Hawkes et al.

Modular soft robots combine the strengths of two traditionally separate areas of robotics. As modular robots, they can show robustness to individual failure and reconfigurability; as soft robots, they can deform and undergo large shape changes in order to adapt to their environment, and have inherent human safety. However, for sensing and communication these robots also combine the challenges of both: they require solutions that are scalable (low cost and complexity) and efficient (low power) to enable collectives of large numbers of robots, and these solutions must also be able to interface with the high extension ratio elastic bodies of soft robots. In this work, we seek to address these challenges using acoustic signals produced by piezoelectric surface transducers that are cheap, simple, and low power, and that not only integrate with but also leverage the elastic robot skins for signal transmission. Importantly, to further increase scalability, the transducers exhibit multi-functionality made possible by a relatively flat frequency response across the audible and ultrasonic ranges. With minimal hardware, they enable directional contact-based communication, audible-range communication at a distance, and exteroceptive sensing. We demonstrate a subset of the decentralized collective behaviors these functions make possible with multi-robot hardware implementations. The use of acoustic waves in this domain is shown to provide distinct advantages over existing solutions.

Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli et al.

Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long design times. With the rising number of robotic and mechatronic systems deployed across areas ranging from industrial automation to intelligent toys, the need for a general approach to generating low-level controllers is increasing. To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at <50Hz. To our knowledge, this is the first use of MBRL for controlled hover of a quadrotor using only on-board sensors, direct motor input signals, and no initial dynamics knowledge. Our controller leverages rapid simulation of a neural network forward dynamics model on a GPU-enabled base station, which then transmits the best current action to the quadrotor firmware via radio. In our experiments, the quadrotor achieved hovering capability of up to 6 seconds with 3 minutes of experimental training data.