Robot swarms have the potential to revolutionize areas ranging from warehouse management and agriculture to underwater and space exploration. However, there remains a substantial gap between theory and robot implementation. While algorithms might assume reliable communication, perfect sensing, and instantaneous cognition, most robots have lossy or even no communication, imperfect sensing, and limited cognition speed. In our previous work on implicit vision-based coordination, we demonstrated autonomous three-dimensional behaviors underwater by removing the need for radio communication between robots. Here we explore impressionist algorithms, capable of working with even more minimal information where traditional algorithms are prone to fail. Our case study focuses on classic flocking behaviors, where a robot swarm must coordinate group motion. We demonstrate that reliable alignment, dispersion, and milling can be achieved with only infrequent and imperfect sensory impressions. In simulation studies and theoretical analyses, we investigate the effect of systematically reducing spatial and temporal fidelity of individual information on the success metrics for the group; we also demonstrate physical experiments with Blueswarm robots using simple color detection. Our results show the potential of impressionist algorithms that operate on simpler neighborhood-awareness metrics and still achieve desired global goals.

Groups of robots can be tasked with identifying a location in an environment where a feature cue is past a threshold, then disseminating this information throughout the group – such as identifying a high-enough elevation location to place a communications tower. This is a continuous-cue target search, where multi-robot search algorithms like particle swarm optimization (PSO) can improve search time through parallelization. However, many robots lack global communication in large spaces, and PSO-based algorithms often fail to consider how robots disseminate target knowledge after a single robot locates it. We present a two-stage hybrid algorithm to solve this task: (1) locating a target with a variation of PSO, and (2) moving to maximize target knowledge across the group. We conducted parameter sweep simulations of up to 32 robots in a grid-based grayscale environment. Pre-decision, we find that PSO with a variable velocity update interval improves target localization. In the post-decision phase, we show that dispersion is the fastest strategy to communicate with all other robots. Our algorithm is also competitive with a coverage sweep benchmark, while requiring significantly less inter-individual coordination.

Several animal species self-organize into large groups to leverage vital behaviors such as foraging, construction, or predator evasion. With the advancement of robotics and automation, engineered multi-agent systems have been inspired to achieve similarly high degrees of scalable, robust, and adaptable autonomy through decentralized and dynamic coordination. So far however, they have been most successfully demonstrated above ground or with partial assistance from central controllers and external tracking. Here we demonstrate an underwater robot collective that realizes full spatiotemporal coordination. Using the example of fish-inspired evasive maneuvers, our robots display alignment, formation control, and coordinated escape, enabled by real-time on-board multi-robot tracking and local decision making. Accompanied by a custom simulator, this robotic platform advances the physically- validated development of algorithms for collective behaviors and future applications including collective exploration, tracking and capture, or environmental sampling.

In this paper we present the design of a fin-like dielectric elastomer actuator (DEA) that drives a miniature autonomous underwater vehicle (AUV). The fin-like actuator is modular and independent of the body of the AUV. All electronics required to run the actuator are inside the 100 mm long 3D-printed body, allowing for autonomous mobility of the AUV. The DEA is easy to manufacture, requires no pre-stretch of the elastomers, and is completely sealed for underwater operation. The output thrust force can be tuned by stacking multiple actuation layers and modifying the Young's modulus of the elastomers. The AUV is reconfigurable by a shift of its center of mass, such that both planar and vertical swimming can be demonstrated on a single vehicle. For the DEA we measured thrust force and swimming speed for various actuator designs ran at frequencies from 1 Hz to 5 Hz. For the AUV we demonstrated autonomous planar swimming and closed-loop vertical diving. The actuators capable of outputting the highest thrust forces can power the AUV to swim at speeds of up to 0.55 body lengths per second. The speed falls in the upper range of untethered swimming robots powered by soft actuators. Our tunable DEAs also demonstrate the potential to mimic the undulatory motions of fish fins.