Genki Miyauchi | 宮内 源己

For robot swarms to operate over extended time periods, they need to coordinate when and how to replenish their energy. To address this challenge, we present an analytical model of a cooperative energy-replenishment strategy in which some robots perform tasks while others periodically provide energy. Given the mission parameters, the model optimally synchronizes the robots’ cycles with respect to both throughput and energy efficiency of the swarm, which is challenging due to cyclic dependencies. The model is validated through simulation, benchmarked against two numerical optimizers, and used to compare the cooperative strategy to an individual one that lets all robots work and replenish their own energy. Finally, we analyze the impact of different energy storage technologies on the swarm’s performance.

Human-swarm collaboration requires balancing effective task execution with positive social perception of human-robot teams. In this study, we investigate how human operators, acting as teammates, influence both objective team performance and social perception in human-swarm collaboration. We conducted a user study with 40 participants in a simulated search-and-assembly task with ten robots, where each participant controlled a single robot within predefined swarm configurations, with team behavior shaped by human assistance. We analyzed how varying levels of human assistance relate to task completion time and perceived warmth and competence of the swarm, alongside subjective ratings of joint effort. Results show that human involvement does not uniformly improve performance. Instead, moderate levels of human assistance were associated with the greatest reduction in completion time, outperforming both low and high assistance conditions. Moderate assistance also produced more positive shifts in perceived warmth and competence. Together, these findings highlight that effective human-swarm teaming depends on balancing the level of human assistance so that it supports strong task performance and positive social perception.

Cave networks, pipe systems, and similar maze-like environments pose significant challenges for multi-agent navigation in unknown settings with limited communication. We propose a distributed algorithm that enables agents to collectively traverse an unknown, possibly cyclic graph. Agents enter sequentially at a designated start node and are tasked to localize and reach an undisclosed goal while avoiding collisions. They coordinate via local communication using leader-follower relationships and leader switching. At any moment in time, exploration is performed by only one of the agents, which runs a single-agent maze solver. We prove that the algorithm is complete, that its makespan is asymptotically equivalent (in the number of agents) to that of an optimal full-knowledge strategy, and derive its time and space complexity. Simulations with up to 625 agents show a decreasing average sum-of-fuels as the number of agents increases and demonstrate that the proposed approach outperforms a naïve baseline in which all agents independently execute the single-agent solver.

In multi-agent systems, should limited resources be concentrated into a few capable agents or distributed among many simpler ones? This work formulates the split over n resource sharing problem where a group of n agents equally shares a common resource (e.g., monetary budget, computational resources, physical size). We present a case study in multi-agent coverage where the area of the disk-shaped footprint of agents scales as 1/n. A formal analysis reveals that the initial coverage rate grows with n$. However, if the speed of agents decreases proportionally with their radii, groups of all sizes perform equally well, whereas if it decreases proportionally with their footprints, a single agent performs best. We also present computer simulations in which resource splitting increases the failure rates of individual agents. The models and findings help identify optimal distributiveness levels and inform the design of multi-agent systems under resource constraints.

As groups of robots increasingly collaborate with humans, understanding how humans perceive them is critical for designing effective human-robot teams. While prior research examined how humans interpret and evaluate the abilities and intentions of individual agents, social perception of robot teams remains relatively underexplored. Drawing on the competence–warmth framework, we conducted two studies manipulating swarm behaviors in completing a collective search task and measured the social perception of swarm behaviors when human participants are either observers (Study 1) and operators (Study 2). Across both studies, our results show that variations in swarm behaviors consistently influenced participants’ perceptions of warmth and competence. Notably, longer broadcast durations increased perceived warmth; larger separation distances increased perceived competence. Interestingly, individual robot speed had no effect on either of the perceptions. Furthermore, our results show that these social perceptions predicted participants’ team preferences more strongly than task performance. Participants preferred robot teams that were both warm and competent, not those that completed tasks most quickly. These findings demonstrate that human-robot interaction dynamically shapes social perception, underscoring the importance of integrating both technical and social considerations when designing robot swarms for effective human-robot collaboration.

Maze-like environments, such as cave and pipe networks, pose unique challenges for multiple robots to coordinate, including communication constraints and congestion. To address these challenges, we propose a distributed multi-agent maze traversal algorithm for environments that can be represented by acyclic graphs. It uses a leader-switching mechanism where one agent, assuming a head role, employs any single-agent maze solver while the other agents each choose an agent to follow. The head role gets transferred to neighboring agents where necessary, ensuring it follows the same path as a single agent would. The multi-agent maze traversal algorithm is evaluated in simulations with groups of up to 300 agents, various maze sizes, and multiple single-agent maze solvers. It is compared against strategies that are naïve, or assume either global communication or full knowledge of the environment. The algorithm outperforms the naïve strategy in terms of makespan and sum-of-fuel. It is superior to the global-communication strategy in terms of makespan but is inferior to it in terms of sum-of-fuel. The findings suggest it is asymptotically equivalent to the full-knowledge strategy with respect to either metric. Moreover, real-world experiments with up to 20 Pi-puck robots confirm the feasibility of the approach.

This paper presents a framework for producing robotic fabrics using square lattice formations of interlinked Kilobot modules. The framework supports: (i) fabrics of arbitrary size and shape; (ii) different types of deformable links, namely springs and rods; (iii) easy plug-and-play reconfigurability. Two decentralized straight motion controllers are tested with robotic fabrics comprising up to 81 physical modules: an open-loop controller and a controller from the literature that responds to deformations within the fabric. For spring-based robotic fabrics, the deformation-correcting controller performs best overall, whereas for rod-based robotic fabrics, it is outperformed by the open-loop controller. A decentralized turning motion controller is formally derived and examined for either type of fabric, revealing the ability of the robotic fabrics to move along a curved trajectory using open-loop control. Finally, robotic fabrics are shown to perform basic object manipulation tasks. Robotic fabrics that deploy themselves based on distributed, embodied intelligence could pave the way for novel applications, from patching broken pipes to medical uses within the human body.

Swarm robotics focuses on designing and coordinating large groups of relatively simple robots to perform tasks in a decentralised and collective manner. The swarm provides a resilient and flexible solution for many applications. However, contemporary swarm robots have a significant power problem in that secondary (i.e. rechargeable) batteries are slow to charge and offer lifetimes of only a few years, increasing maintenance costs and pollution due to battery replacement. We imagine a different future, wherein battery-free robots powered by supercapacitors can be recharged in seconds, offer long-life autonomous operation and can rapidly pass charge between one another using trophallaxis. In pursuit of this vision, we contribute the CapBot, a battery-free swarm robot equipped with Mecanum wheels, a Cortex M4F application processor and Bluetooth Low Energy networking. The CapBot fully recharges in 16 s, offers 51 min of autonomous operation at top speed, and can transfer up to 50% of its available charge to a peer via trophallaxis in under 20 s. The CapBot is fully open source and all software and hardware source is available online.

Unmanned Aerial Vehicles (UAVs) are expected to transform logistics, reducing delivery time, costs, and emissions. This study addresses an on-demand delivery scenario, in which fleets of UAVs are deployed to fulfil orders that arrive stochastically. Unlike previous work, it considers UAVs with heterogeneous, unknown energy storage capacities and assumes no knowledge of the energy consumption models. We propose a decentralised deployment strategy that combines auction-based task allocation with online learning. Each UAV independently decides whether to bid for orders based on its energy storage charge level, the parcel mass, and delivery distance. Over time, it refines its policy to bid only for orders within its capability. Simulations using realistic UAV energy models reveal that, counter-intuitively, assigning orders to the least confident bidders reduces delivery times and increases the number of successfully fulfilled orders. This strategy is shown to outperform threshold-based methods which require UAVs to exceed specific charge levels at deployment. We propose a variant of the strategy which uses learned policies for forecasting. This enables UAVs with insufficient charge levels to commit to fulfilling orders at specific future times, helping to prioritise early orders. Our work provides new insights into long-term deployment of UAV swarms, highlighting the advantages of decentralised energy-aware decision-making coupled with online learning in real-world dynamic environments.

This paper studies how groups of robots can effectively navigate through a crowd of agents. It quantifies the performance of platooning and less constrained, greedy strategies, and the extent to which these strategies disrupt the crowd agents. Three scenarios are considered: (i) passive crowds, (ii) counter-flow crowds, and (iii) perpendicular-flow crowds. Through simulations consisting of up to 200 robots, we show that for navigating passive and counter-flow crowds, the platooning strategy is less disruptive and more effective in dense crowds than the greedy strategy, whereas for navigating perpendicular-flow crowds, the greedy strategy outperforms the platooning strategy in either aspect. Moreover, we propose an adaptive strategy that can switch between platooning and greedy behavioral states, and demonstrate that it combines the strengths of both strategies in all the scenarios considered.

To enable long-term operations of swarms of energy-constrained robots, they need to manage both their in-flow and out-flow of energy. We consider two strategies for doing so: In the first strategy, all robots work at a remote location but due to their limited storage capacity must return to charge. In the second strategy, dedicated mobile chargers with finite storage capacity deliver energy to the remote location, substantially shortening the worker robots’ commute. We compare the work performed and the energy efficiency of these strategies using physics-based simulations and reveal conditions under which their performance is close to theoretically derived upper bounds. We assess several factors, including the number of mobile chargers, their storage capacity, transfer losses, and the ratio of energy expended while working and traveling. Our findings confirm that mobile chargers can help increase the work performed, and even overall energy efficiency provided that their energy storage is larger than that of workers.

Simultaneously controlling multiple robot swarms is challenging for a single human operator. When involving multiple operators, however, they can each focus on controlling a specific robot swarm, which helps distribute the cognitive workload. They could also exchange some robots with each other in response to the requirements of the tasks they discover. This paper investigates the ability of multiple operators to dynamically share the control of robot swarms and the effects of different communication types on performance and human factors. A total of 52 participants completed an experiment in which they were randomly paired to form a team. In a 2×2 mixed factorial study, participants were split into two groups by communication type (direct vs. indirect). Both groups experienced different robot-sharing conditions (robot-sharing vs. no-robot-sharing). Results show that although the ability to share robots did not necessarily increase task scores, it allowed the operators to switch between working independently and collaboratively, reduced the total energy consumed by the swarm, and was considered useful by the participants.

We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. The individuals—robotic modules—move stochastically using vibration motors. By coupling them via elastic links, soft-bodied aggregates can be created. We present distributed algorithms that enable the aggregates to move and deform reliably. The concept and algorithms are validated through formal analysis of the elastic couplings and experiments with aggregates comprising up to 49 physical modules—among the biggest soft-bodied aggregates to date made of autonomous modules. The experiments show that aggregates with elastic couplings can shrink and stretch their bodies, move with a precision that increases with the number of modules, and outperform aggregates with no, or rigid, couplings. Our findings demonstrate that mechanical couplings can play a vital role in reaching coherent motion among individuals with exceedingly limited and error-prone abilities, and may pave the way for low-power, stretchable robots for high-resolution monitoring and manipulation.

Involving human operators to support swarms of robots can be beneficial to address increasingly complex scenarios. However, the shared control between multiple operators remains a challenge, especially where communication between the operators is not available. This paper studies the problem of forming a dynamic chain of robots connecting two operators moving within an environment. The robot chain enables operators to share information and robots among themselves. Based on supervisory control theory, we propose a distributed solution which formally guarantees that the deployed robot controllers match the modeled specifications. We validate the controllers through simulations with groups of up to 40 mobile robots in an environment with obstacles, demonstrating the feasibility of the approach.

On the farm of the future, a human agriculturist collaborates with both human and automated labourers in order to perform a wide range of tasks. Today, changes in traditional farming practices motivate robotics researchers to consider ways in which automated devices and intelligent systems can work with farmers to address diverse needs of farming. Because farming tasks can be highly specialised, though often repetitive, a human-robot approach is a natural choice. The work presented here investigates a collaborative task in which a human and robot share decision making about the readiness of strawberries for harvesting, based on visual inspection. Two different robot behaviours are compared: one in which the robot provides decisions with more false positives and one in which the robot provides decisions with more false negatives. Preliminary experimental results conducted with human subjects are presented and show that the robot behaviour with more false positives is preferred in completing this task.

The allocation of tasks to members of a team is a well-studied problem in robotics. Applying market-based mechanisms, particularly auctions, is a popular solution. We focus on evaluating the performance of the team when executing the tasks that have been allocated. The work presented here examines the impact of one such factor, namely task duration. Building on prior work, a new bidding strategy and performance metric are introduced. Experimental results are presented showing that there are statistically significant differences in both time and distance-based performance metrics when tasks have zero vs greater-than-zero duration.

This paper presents the results of preliminary experiments in human-robot collaboration for an agricultural task.