Implementation of an Artificially Empathetic Robot Swarm
Abstract
:1. Introduction
1.1. Motivation
1.2. Empathy Modeling
1.3. Structure of the Paper
2. Materials and Methods
2.1. Empathy Theory
- Emotional and cognitive empathy model: The model was developed from medical and neuroscientific research of the human brain and has its justification in the brain structure. It assumes that empathy can be divided into parts: (1). responsible for recognizing and reacting to emotions; (2). a part responsible for cognitive, more logical, and deductive mechanisms of understanding the inner states of others [24].
- Russian doll model: The model assumes that empathy is learned during human life—it resembles a Russian doll, with layers of different levels of understanding others. The first, most inner layers are mimicry and automatic emotional reactions, the next layers are understanding others’ feelings and the outer layers are taking the perspective of others, sympathizing, and experiencing schadenfreude [25].
- Multi-dimensional model: This model assumes that we have four dimensions of empathy—antecedents, processes, interpersonal outcomes, and intrapersonal outcomes. Antecedents encompass the agent’s characteristics: biological capacities, learning history, and situation. Processes produce empathetic behaviors: non-cooperative mechanisms, simple cognitive mechanisms, and advanced cognitive mechanisms. Intrapersonal outcomes are to resonate or not with the empathy target, and interpersonal outcomes are relationship related [26].
2.2. Available Experimental Environments
- Stand-alone robots, allowing the construction and modeling of swarm behavior.
- Kilobot [3]: This is a swarm-adapted robot with a diameter of 3.3 cm, developed in 2010 at Harvard University. It operates in a swarm of up to a thousand copies, carrying out user-programmed commands. The total cost of Kilobot parts was less than USD 15. Kilobots move in a vibration-based manner. In addition, they are capable of recognizing light intensity, communicating, and measuring the distance to nearby units. Currently, the project is not under active development, but it is still popular among researchers.
- e-puck2 [42]: This is a 7 cm diameter mini mobile robot developed in 2018 at the Swiss Federal Institute of Technology in Lausanne. It supports Wi-Fi and USB connectivity. It has numerous sensors, including IR proximity, sound, IMU, distance sensor, and a camera. The project is being developed using open-source and open-hardware principles.
- MONA [43]: This is an open-hardware/open-source swarm research robotic platform developed in 2017 at the University of Manchester. MONA is a small, round robot with a diameter of 8 cm, equipped with 5 IR transmitters, based on Arduino architecture.
- Colias [44]: This is an inexpensive 4 cm diameter micro-robot for swarm simulation, developed in 2012 at the University of Lincoln. Long-range infrared modules with adjustable output power allow the robot to communicate with its immediate neighbors at a range of 0.5 cm to 2 m. The robot has two boards—an upper board responsible for high-level functions (such as communication), and a lower board for low-level functions such as power management and motion control.
- SwarmUS [45]: This is a project that helps create swarms of mobile robots using existing devices. It is a generic software platform that allows researchers and robotics enthusiasts to easily deploy code in their robots. SwarmUS provides the basic infrastructure needed for robots to form a swarm: a decentralized communication stack and a localization module that helps robots locate each other without the need for a common reference. The project is not in development as of 2021.
- Robot simulation software
- AWS Robomaker: This is a cloud-based simulation service released in 2018 by Amazon, allowing robotics developers to run, scale, and automate simulations without the need to manage any infrastructure. It enables the creation of user-defined, random 3D environments. Using the simulation service, one can speed up application testing and create hundreds of new worlds based on templates that one defines.
- CoppeliaSim [1]: This is a robotics simulator with an integrated development environment; it is based on the concept of distributed control. Each object/model can be individually controlled using a built-in script, plug-in, ROS node, remote API client, or another custom solution. This makes it versatile and ideal for multi-robot modeling applications. It is used for rapid algorithm development, simulation automation of complex processes, rapid prototyping and verification, and robotics-related education.
- EyeSim [46]: This is a virtual reality mobile robot simulator based on the Unity engine, which is able to simulate all the main functions of RoBIOS-7. Users can build custom 3D simulation environments, place any number of robots, and add custom objects to the simulation. Thanks to Unity’s physics engine, robot motion simulations are highly realistic. Users can also add bugs to the simulation, using built-in simulated bug functions.
- Comprehensive services including simulator and hardware platform.
- AWS DeepRacer: This is a 1/18 scale fully autonomous racing car designed in 2017 by Amazon and controlled by Reinforcement Learning algorithms. It offers a graphical user interface that can be used to train the model and evaluate its performance in a simulator. AWS DeepRacer, on the other hand, is a Wi-Fi-enabled physical vehicle that can drive autonomously on a physical track using a model created in simulations.
- Kilogrid [47]: This is an open-source Kilobot robot virtualization and tracking environment. It was designed in 2016 at the Free University of Brussels to extend Kilobot’s sensorimotor capabilities, simplify the task of collecting data during experiments, and provide researchers with a tool to precisely control the experiment’s configuration and parameters. Kilogrid leverages the robot’s infrared communication capabilities to provide a reconfigurable environment. In addition, Kilogrid enables researchers to automatically collect data during an experiment, simplifying the design of collective behavior and its analysis.
2.3. Fuzzy Sets and Their Similarity
- (T1)
- for each , we have ,
- (T2)
- for each and such that we have
- (T3)
- for each such that we have and .
3. Results
3.1. Artificial Empathy of a Swarm
3.1.1. Egoistic Behavior Evaluation Module
3.1.2. Artificially Empathetic Behavior Evaluation Module
3.1.3. Memory Module
3.1.4. Decision Making
3.1.5. Learning
3.2. Simulations
3.2.1. Problem Description
- Call for help;
- Encircling the rat;
- Helping;
- Another robot nearby;
- Rat nearby.
- Detection of a rat in the warehouse—solitary pursuit.
- -
- Robot 1 patrols the warehouse;
- -
- Robot 1 notices a rat;
- -
- Robot 1 starts chasing the rat;
- -
- Robot 1 catches the rat, meaning it approaches the rat to a certain distance.
- Detection of a rat in the warehouse—pursuit handover.
- -
- Robot 1 patrols the warehouse;
- -
- Robot 1 notices a rat in the adjacent area;
- -
- Robot 1 lights up the appropriate color on the LED tower to inform Robot 2 that there is a rat in Robot 2’s area;
- -
- Robot 2, noticing the appropriate LED color, starts chasing the rat;
- -
- Robot 2 catches the rat, meaning it approaches the rat to a certain distance.
- Detection of a rat in the warehouse—collaboration.
- -
- Robot 1 patrols the warehouse;
- -
- Robot 1 notices a rat;
- -
- Robot 1 starts chasing the rat;
- -
- The rat goes beyond Robot 1’s patrol area;
- -
- Robot 1 lights up the appropriate color on the LED tower to inform Robot 2 that the rat entered its area;
- -
- Robot 2, noticing the appropriate LED color, continues chasing the rat;
- -
- Robot 2 catches the rat, meaning it approaches the rat to a certain distance.
- Change of grain color.
- -
- Robot 1 patrols the warehouse;
- -
- Robot 1 notices that the grain color is different than it should be;
- -
- Robot 1 records the event in a report;
- -
- Robot 1 continues patrolling.
- Change of grain color—uncertainty.
- -
- Robot 1 patrols the warehouse;
- -
- Robot 1 notices that the grain color is possibly different than it should be—uncertain information;
- -
- Robot 1 lights up the appropriate color on the LED tower;
- -
- Robot 2, noticing the appropriate LED color, expresses a willingness to help and approaches Robot 1;
- -
- Robot 2 from the adjacent area checks the grain color and confirms or denies Robot 1’s decision;
- -
- Robot 1 records the event in a report if confirmed by Robot 2;
- -
- Robot 2 from the adjacent area returns and continues patrolling;
- -
- Robot 1 also continues patrolling.
- Weak battery.
- -
- Robot 1 has a weak battery;
- -
- Robot 1 lights up the appropriate color on the LED tower, expressing a desire to recharge its battery;
- -
- Robot 2, noticing the appropriate LED color, agrees to let Robot 1 recharge the battery;
- -
- Robot 1 goes to recharge;
- -
- Robot 2 additionally takes over Robot 1’s area for patrolling.
- Exchange of patrol zones.
- -
- Robot 1 passes through its patrol area several times without any events;
- -
- Robot 1 lights up the appropriate color on the LED tower, expressing a desire to exchange the patrol area;
- -
- Robot 2, noticing the appropriate LED color, expresses a desire to exchange the patrol area;
- -
- Robot 1 and Robot 2 exchange patrol areas.
3.2.2. Implementation
3.2.3. Simulation Results
- Egoistic, two rats. Shortly after starting a patrol, both robots spot the same rat and start chasing it. Meanwhile, the second rat destroys the grain located in the middle of the arena. After neutralizing the first rat, one of the robots begins chasing the second pest.
- Empathetic, two rats. The robot on the right spots a rat and signals it with an LED strip. The second robot, noticing this, continues to patrol the surroundings in search of other pests. After a while, it detects the second rat and starts following it. As a result, both rats are neutralized and grain loss is reduced.
- Egoistic, robots run out of battery. Robots detect the same rat. During the chase, the robots interfere with each other, making it difficult to follow and neutralize the rat. Eventually, the rat is neutralized, but before the robots can spot and begin their pursuit of the other pest, both of them run out of battery and the second rat escapes.
- Empathetic, low battery help. The robot on the right starts chasing the detected rat. During this action, the agent signals with an LED strip that it needs assistance, due to a low battery level. The other robot notices this and decides to help to catch the weaker rat. After neutralizing it, the second robot starts searching for other pests.
3.3. Open-Source Physical-Based Experimentation Platform
3.3.1. Proposed Platform Features and Architecture
- Comprehensive support for swarm design process using hardware platform. This feature corresponds to the need to verify AI algorithms in a hardware environment, including early-stage development, consideration of environmental parameters that are unavailable in simulations, and the ability to study algorithms, considering variable environments and interactions. In this area, there are two alternatives: comprehensive algorithm evaluation (Kilogrid + Kilobots, DeepRacer) and simulation software (CoppeliaSim v4+, DynaVizXMR, EyeSim v1.5+, Microsoft Robotics). Alternative solutions only support the design process in simulated environments or require significant financial investments for prototyping, limiting accessibility in early development stages.
- Low cost of building and size of swarm robots. This feature corresponds to the need to evaluate complex behaviors and the latest AI algorithms in a large swarm of robots, considering the requirements for low cost and easy availability of solutions. Alternatives include miniature robots like Kilobots and mini sumo robots. Those solutions are expensive, with costs often including additional resources and services. Additionally, computational power drastically decreases with the robot’s size, limiting capabilities such as running a vision system.
- Remote programming of robots. This feature addresses the need to share research/educational infrastructure without physical access, fostering interdisciplinary and international research collaborations. Alternatives include cloud-based robot simulators like AWS Robomaker and DeepRacer. In competitive solutions, this functionality is only available in simulations or limited environments and specific research areas.
- Standardization and Scalability of experimental environment. This feature corresponds to the need to adapt and expand the experimental platform to different projects while maintaining standardization for experiment repeatability and reproducibility, facilitating comparisons across research centers. Alternatives include open-source software and hardware projects like SwarmUS, Kilobots, colias.robot, as well as simulation software. They lack the ability to expand robot software in any way using high-level languages. Moreover, existing solutions are not designed for result repeatability (e.g., randomness in Kilobots’ movements).
- Open specification and hardware. This feature corresponds to the need for independently building a complete experimental platform. Current solutions include open-source software and hardware projects, such as SwarmUS, Kilobots, and colias.robot. Most competitive solutions are closed, and open solutions often have limited computational resources.
3.3.2. Platform Implementation
First Prototype
Second Prototype
- The robots need to be stopped and physically plugged in for charging when the batteries run out. This causes delays in conducting experiments.
- Current robots are characterized by large dimensions compared to the work area. This minimizes the simultaneous number of robots that can move around the arena.
- The presence of an experimenter is required to activate the robots. This makes it impossible to conduct remote experiments.
Experimentation Arena
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Name | Sym | Description of Boundary Values |
---|---|---|
others close | a | 1 many other agents in the vicinity, 0 for none |
in touch | n | 1 for long contact time, 0 for none |
long search | t | 1 for the long duration of the current search, 0 for not searching |
calling for help | c | 1 for calling for a long time, 0 for not calling |
neutralized | e | 1 if “I am inactive” signal was received from the newly inactive agent; 0 if not |
close to neighbor | d | 1 if the distance to the neighbor is 0; 0 if the distance to the neighbor is far |
target at right | p | 1 for the agent at the immediate right, 0 for the agent not in sight |
target at left | l | 1 for the agent at the immediate left, 0 for the agent not in sight |
fully charged | f | 1 for the fully charged robot, 0 for not charged |
helping | h | 1 for the long duration of helping, 0 for not helping |
reward | describes the chance of success of the current action sequence |
Parameter | ||||||
---|---|---|---|---|---|---|
a | 0.5 | 1.0 | 0.5 | 0.5 | 0.0 | 0.1 |
n | 0.5 | 0.5 | 0.5 | 0.0 | 1.0 | 1.0 |
t | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 |
c | 1.0 | 1.0 | 1.0 | 0.5 | 1.0 | 0.5 |
d | 0.5 | 1.0 | 0.5 | 0.5 | 0.0 | 0.1 |
p | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 |
l | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 |
f | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 |
h | 0.5 | 0.5 | 0.5 | 1.0 | 0.5 | 1.0 |
1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 |
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Siwek, J.; Żywica, P.; Siwek, P.; Wójcik, A.; Woch, W.; Pierzyński, K.; Dyczkowski, K. Implementation of an Artificially Empathetic Robot Swarm. Sensors 2024, 24, 242. https://doi.org/10.3390/s24010242
Siwek J, Żywica P, Siwek P, Wójcik A, Woch W, Pierzyński K, Dyczkowski K. Implementation of an Artificially Empathetic Robot Swarm. Sensors. 2024; 24(1):242. https://doi.org/10.3390/s24010242
Chicago/Turabian StyleSiwek, Joanna, Patryk Żywica, Przemysław Siwek, Adrian Wójcik, Witold Woch, Konrad Pierzyński, and Krzysztof Dyczkowski. 2024. "Implementation of an Artificially Empathetic Robot Swarm" Sensors 24, no. 1: 242. https://doi.org/10.3390/s24010242
APA StyleSiwek, J., Żywica, P., Siwek, P., Wójcik, A., Woch, W., Pierzyński, K., & Dyczkowski, K. (2024). Implementation of an Artificially Empathetic Robot Swarm. Sensors, 24(1), 242. https://doi.org/10.3390/s24010242