Color-Coded Light Signals Based Communication for Swarming Behaviors in Mobile Robotic System

Kaushal, Abhishek; Sharma, Anuj Kumar; Srivastava, Ashish Kumar; Singh, Gyanendra Kumar

doi:https://doi.org/10.1155/2023/8660847

Journal of Sensors

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Abstract Introduction Results and Discussion Conclusions Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 8660847 | https://doi.org/10.1155/2023/8660847

Color-Coded Light Signals Based Communication for Swarming Behaviors in Mobile Robotic System

Abhishek Kaushal,¹Anuj Kumar Sharma,¹Ashish Kumar Srivastava,²and Gyanendra Kumar Singh³

Academic Editor: Lei Chu

Received24 Feb 2023

Revised11 Jul 2023

Accepted18 Jul 2023

Published03 Aug 2023

Abstract

Swarm robotics deals with the design, development, and control of multirobot system. The individual agents of the swarm robotics system are simple, with limited capabilities in order to enhance easy scalability of the system. The current researches in swarm robotics are limited due to the complexity of their control and communication system. This paper presents minimobile robots that uses color-coded light signals for communication over short distances and can also communicate over WiFi mesh for global information sharing. Color-coded light-based communication enables the robots to control the directionality and range of communication signals, thus making the communication highly efficient comparing to other conventional communication techniques. The proposed robot design helps the agents to communicate with each other and thereby a swarming behavior emerges from their interaction.

1. Introduction

The swarm robotics is inspired from the swarming behavior observed in nature among insects (like bees, termites, ants, etc.) and other social animals. The swarm robots [1] demonstrate such behaviors by organizing simple robots to supportively accomplish tasks [2]. The swarm robotic system is generally decentralized in nature. The robots are governed by local interactions among the agents of the swarm and with the environment, due to which a collective global behavior of the swarm emerges [3]. Therefore, a small change in the individual robot’s behavior can lead to a completely different collective behavior of the group. The physical structure of robots of the swarm must be homogeneous in order to achieve standardization and reduction in cost during scaling. The aim of swarm robotic system is replacing few complex robots with multiple simple robots interacting with each other to accomplish a common task. The use of multiple simple robots in a swarm makes the overall system more flexible, robust, and failure of few agents does not have critical effect on the operation of whole system [1]. As the study of swarm robotics requires the presence of multiple robots, many researchers prefer software simulations over the use of physical robots due the high complexity of the robotic platforms, as well as the associated cost. These simulations require high degree modeling of the robots as well as the environment which is very difficult to achieve and thus making them often imprecise. These simulations models rely on many assumptions and exceptions for making them less computational expensive and give results with acceptable limits. Ultimately, these simulation models call for robotic platforms for validation when operating in a real-world environment. The advantages of swarm robotic platforms in comparison to single robot system are multitasking, easily scalable to large number according to task, have high reliability and robustness, due to simplicity of the agents these systems are in general economical and energy efficient [4].

Various robotics platforms have been developed previously for studying the swarming behavior of multirobot systems, Alice [5] a miniature robot with distance sensors was used to study the aggregation behavior [6] and other research applications. Another robot E-puck [7] developed for education and research is equipped with numerous sensors, making it one of the most sophisticated robots. The E-puck robot requires additional modules for range and bearing sensing [8]. Jasmine [9] a microrobot developed for swarm robotics application uses infrared sensors environment sensing and sensing presence of nearby robots. A light-weight robot Kilobot [10] which uses vibration motors for its movement is used for study of swarm robotic behaviors, Kilobot has limited sensors and can only operate on smooth surfaces. GRITSBot [11] is a differential drive robot with multilayer PCB structure, the robot uses infrared sensors for sensing objects and other robots. Mona [12] is a low-cost robot with support for various programing environment and is used for both research and in education. Pheeno [13] a multipurpose robotics platform, it has a programable gripper module can be used for cooperative multirobotics tasks. AMiR [14] an open source, low-cost robot developed for mostly study of honeybee aggregation (BEECLUST). Colias [15] and its extended version Colias-IV [16] swarm robotics platform were primarily developed for swarm robotics applications, the basic version of Colias robots have distance, range, bearing, bump sensors, while the extended version is equipped with a camera and thus can be used for bioinspired vision sensing. ColCOSф [17, 18] another swarm system using artificial pheromone for communicating with other robots of the swarm, ColCOSф utilizes liquid crystal display screen base onto which color pheromones are displayed, robots use color sensors to detect these pheromones and act accordingly. Phormica [19] a swarm system which uses photochromic pheromone for inter-robot coordination, this system utilizes ultraviolet (UV) light-based artificial pheromone system in which UV lights are mounted on the robots that activates the portion of the arena simulating a pheromone trail. Another study suggests artificial pheromone using ferromagnetic materials (ferrofluid) to form magnetic trails that can be detected by robotic system, and thus enabling the system usage in outdoor environments [20].

The developed robotic platforms are generally inspired from the natural swarm system. The robots developed all share the common properties of small size, limited sensing capability, and decentralized control. Table 1 shows some of the robotic platforms which were developed in the field of swarm robotics in chronological order.

As per the literature available, most of the robotic platforms are open source but are limited by the hardware complexity which makes them difficult to reproduce. Few robots like E-puck [7] and Pheeno [13] despite being versatile are limited in use because of the high cost of the individual robot. Generally, robots mentioned above use infrared emitters and sensors for local communication which requires filtering of the daylight either by using filtering lens on the sensor or by some modulation techniques. As the infrared light is invisible to human eye, it becomes difficult to track the state of the individual agent of the swarm unless an additional signaling system is used. In case of using a camera module whether on robot or externally, it becomes computationally expensive and increases the cost of overall system.

The swarm robotic system thus can be used for various complex problems of optimization and target localization [27, 28], search and rescue [31–33], and population discovery [34]. Such application is possible due to development of efficient computational intelligence [35]. Strategy like use of chemotaxis as virtual boundary was studied for operating the swarm of robots in an unbounded area [36], use of finite state machines was also suggested in various swarm system use cases [37], and for tasks such as aggregation and dispersion which involve multiple targets tracking neural network with evolutionary algorithms can give us efficient results [38]. Robot bean optimization algorithm inspired by evolutionary techniques used by plants have been proven to be effective for search and collaborative tasks of swarm system [39]. Exploration techniques based on improved random walk method can efficiently cover larger area in comparison to Brownian motion or L´evy flight thus can make exploration quicker [40, 41]. Gradient and grouping are another such techniques for area coverage [42]. In the case of aggregation, dynamic interaction among the robots can lead to self-organization of the group [43]. Various other target searching strategies are being explored by researchers around the world in view of various environmental constraints [44].

This paper proposes a miniature, 3D-printed autonomous robot platform for swarm behavioral study. The developed robots use reflective infrared sensors for obstacle detection. The following manuscript discusses about a novel method of communication in robot swarm using visible light signals [45] that communicate inherently with proximity, along with traditional fully connected RF communication. The major advantage of the proposed system over previous works is that visible light communication provides two information, i.e., data to be communicated and location of the sending robot. Section 2 describes the hardware specification of the robot along with its design architecture. A brief description about the communication modes is used by the robots. Section 3 discusses about the experiments to study the swarming behaviors of the robot. Last of all, Section 4 concludes the work and comments about its future scope.

2. System Design and Methodology

This section discusses about the design aspects of the robot prototype developed and communication technique used by the robots for inter-robot communication.

2.1. Robot Design

The developed robots are based on differential drive robot model making the motion of robots nonholonomic in nature, the differential drive model is known to be simple and cost-effective making it suitable for swarm robotics applications. The chassis and the wheels of the robots are 3D printed using polylactic acid material, two 87 rpm micrometal gear motors connected to a DRV8833 dual motor driver are placed at the bottom of the chassis, addressable RGB LEDs are placed on all sides of the robots for transmitting color-coded light signals. Broadcom APDS9960 RGB color sensor is placed in front of the robot for capturing and decoding the color light signals, three infrared sensors (TCRT5000L) are placed in front of the robot, the left and right sensors are at angle |θ₁| = 45⁰ and |θ₂| = 45⁰, respectively, which are used for obstacle detection, a three-axis magnetometer is placed at the top of the robot which is used to determine the angle of rotation of the robot and heading direction with respect to Earth’s magnetic axis; lastly, three light dependent resistors are placed in front of the robot one at center and two on both sides, for detecting the direction of the incoming light signal. All the sensors and actuators are connected to a ESP32-D0WDQ6 microcontroller development board which processes all the sensors information and generates the required actuation signals. The fabricated robot is shown in Figure 1.

The architecture of the robot is shown in Figure 2, which gives information about the various functional units of the robot and their interconnection for power and data transfer. Figure 2(a) shows the location and orientation of sensors used for color, light intensity, and obstacle sensing on the robot. The orientation of the sensors is in reference to the local robot reference frame. Figure 2(b) gives connection block diagram of the robot and shows all the components of the robot along with their interconnection.

(a)

(b)

The robot is powered by a single-cell 3.7 V, 500 mAh, LiPo battery. The battery is connected to the battery charging and protection circuit based around TP4056 IC.

2.2. Obstacle Avoidance

The robot uses the three infrared sensors to detect obstacles and other robots. These sensors are able to detect obstacles up to distance of 25 mm. Algorithm 1 describes the obstacle avoidance behavior of the robots.

Data: IR sensor readings: (S1, S2, S3)
Result: Obstacle avoidance motion
1 Function Avoid Obstacle ():
2 Read sensor values S1, S2, S3
3 if (S1 > threshold) or (S2 > threshold) or (S3 > threshold) then
4 if (S1 < threshold) *and* (S2 > threshold) *and* (S3 < threshold) then
5 Move back
6 Turn left
7 else
8 if (S1 < threshold) *and* (S2 < threshold) *and* (S3 > threshold) then
9 Turn left
10 else
11 Turn right
12 end
13 end
14 else
15 Move forward
16 end
17 end

2.3. Communication Technique

Consider two robots communicating as shown in Figure 3, the transmitter LED and the APDS9960 sensor are positioned at p_t, p_r ϵ ℝ², with orientation angle θ_t, θ_r, respectively, assuming a global Cartesian coordinate system. Then:where α is the transmitting angle, β is the reception angle, and the distance between the transmitter and the sensor is given by d_tr. As the color light is transmitted as an on–off signal, the transmitted light signal s(t): ℝ→{0, 1}. Thus, the received light signal intensity can be described as follows:where T is duration of received signal, µ_c is the coefficient of attenuation for the transmission channel, and n(t) is the additive white Gaussian noise [46]. The channel attenuation coefficient µ_c includes the attenuation caused by transmitter angle, propagation medium, and sensor angle.

Equations (1) and (2) give transmitting angle and reception angles that is used for estimating the direction of incoming light signal, thus the receiver robot turns accordingly to orient in the direction of incoming signal by minimizing . Equation (4) is used for calculating the intensity “I” of incoming light signals considering attenuations.

The robots communicated with each other using color-coded light signals. Robots have a triangular sensing zone and can detect the incoming light signals reliably up to a distance of 250–300 mm under medium light conditions. The light signals follow inverse square law, i.e., the intensity “I” of light is inversely related to the square of the distance “d” from the source “s”, mathematically shown in Equation (5):

The reliable color perception distance is dependent on the environmental lighting condition, the color sensing range decreases rapidly with the increase ambient light intensity. The effect of ambient light on different color perception is shown in Figure 4. Figure 4 also shows maximum sensing distance of each color under three different lighting conditions, i.e., dark lighting condition which gave ambient light reading value in range of 4–6 from APDS9960 sensor, while medium light with ambient light reading values 305–310, and high lighting with ambient light reading values in the range of 719–725.

The intensity of the received light signal can be correlated with the distance of the source thus enabling the receiver robot to assess the distance of the sender robot. The light intensity sensors give the direction information of the incoming light signals, making the system capable of detecting the direction and distance of the source. The robots vary the intensity of emitted light signals in order to control the extent to which the information is to be sent, as represented in Figure 5.

(a)

(b)

(c)

Thereby, varying the color of the emitted signals control commands can be transmitted to neighboring robots, the intensity of the light signals controls the extent of information is to be transmitted. The four addressable RGB LEDs on the robot can be individually controlled in order to send the data in a particular direction, this enables the communication system to conserve battery and makes the system operation efficient in comparison to traditional radio-based communication systems, and Figure 6 illustrates this direction-controlled communication.

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(b)

(c)

(d)

3. Results and Discussion

The developed robots can be individually manually controlled or can be operated in full autonomy giving flexibility to control the behavior of the swarm. To evaluate the behavior of the developed system, simulations were performed in MATLAB 2019b for basic behaviors along with verification experiments with the developed robots, which were operated in 1 m² arena. Various experiments relating to swarming behaviors were performed using color-coded communication technique. The color-coded command signals and their associated behavior are mentioned in Table 2.

The robots were also run at different speeds in order to observe the effect of robot speed on different behaviors. In order to vary the speed of the robots pulse width modulation (PWM) speed control technique was employed. Table 3 shows robot wheel speeds at different PWM values, where V_b (87 rpm) is the motor-rated speed at 3.3 V, as mentioned in Section 2.1. Below PWM value 40, the power to robot’s motor became too weak to move the robot.

3.1. Aggregation

The robots signal neighboring robots to reduce the distance between the transmitter and receiver robots. The received signal is decoded and robots try to minimize the inter-robot distance, the robots flow algorithm mentioned in Figure 7 to aggregate. The simulation of this behavior along with real-world implementation is shown in Figure 8.

(a)

(b)

The robots were able to aggregate via signaling one another or any one robot can send aggregation command to other robots via color-coded light signal, the other robots then aggregate at the point of the commander robot. The time to aggregate is dependent on the threshold aggregation distance, which is the minimum distance between the robots for after aggregation, the smaller the distance, i.e., the closer the robots are needed to be after aggregation the more the time requirement, as can be seen in Figure 9(a). The distance threshold was set based on the received light intensity.

(a)

(b)

Moving speed of the robots also had an impact of on the aggregation time, higher the moving speed of the robot, quicker they will aggregate. But at very high speed, it was observed that the robots started missing the light signals, so experimentally it was found that maximum PWM speed value of 75 at which robot showed reliable color recognition. The effect of robot speed on aggregation time is shown in Figure 9(b). The effect of number of robots can also be seen in Figure 9(b), a greater number of robots, higher aggregation time is required, as the robots start blocking the color light signals for other robots of the swarm.

3.2. Dispersion

Dispersion of robots of the swarm gives the advantage of covering a larger area for exploration. Robots repel each other if they are too close. Dispersion behavior was realized by control flowchart, as shown in Figure 10, using which robots signal each other to maximize the distance between the transmitting–receiver robots by emitting color-coded light signal. The simulation and real-world implementation are illustrated in Figure 11.

(a)

(b)

The dispersion time of the robots are related to the dispersion distance, which describes the maximum distance a robot must travel in order for a successful dispersion. The dispersion distance can be set by setting the received light intensity threshold or by setting the on time of the motors when moving away. The relationship between dispersion time and distance is shown in Figure 12(a).

(a)

(b)

The robot speed also impacted the total dispersion time of the robots, as higher the robot speed, lower the time, as shown in Figure 12(b). The dispersion time got increased with the increased number of robots due to inter-robot interactions during dispersion. The robots tried to move away from the commander robot as well as other robots of the swarm. The effect of number of robots on the dispersion time can be seen in Figure 12(b).

3.3. Clustering

Clustering involves the formation of small local groups within the swarm. Robots are segregated into different groups by the color or light they emit, same color robots are attracted to each other while other color robots are repelled. Figure 13 shows control flowchart which was used to implement clustering behavior, that divides the group of four robots into two clusters red and green, the color signals for cluster formation were predefined. The robots avoid other colors by using the same principle as used in dispersion. The simulation and real robot verification are shown in Figure 14.

(a)

(b)

Robot clustering is a combination of robot aggregation and dispersion behavior mentioned in Sections 3.1 and 3.2.

3.4. Organised Movement/Leader Following

The movement of swarm of robots in a particular order or in a specific geometrical shape from one location to another is termed as organized movement. In this behavior, a leader robot can be chosen which can be controlled by a human operator over BLE or WiFi app or can operate in complete autonomy. The individual robots try to be close to a specific color light, due to which robots move in organized manner. Thereby controlling the color of specific LED (generally rear) on the robot body, the robots show leader following behavior. The leader robot is controlled remotely by the operator using WiFi-based application, which requires leader’s IP address, while the follower robots are controlled via control scheme, as shown in Figure 15, respectively. The leader robot is controlled over WiFi using User Datagram Protocol. Figure 16 shows simulation and real-world implementation of such a behavior using developed robot prototypes.

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(b)

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3.5. Swarm Synchronization

This behavior utilizes global communication method which connects the members of the swarm over a WiFi-based mesh network. Global communication method is chosen so as to synchronize all the robots at once, instead of local neighbors only when using light-based communication. All the robots are synchronized to have a common understanding of time which makes them work more efficiently. Swarm synchronization takes place over the ad hoc mesh network, the system self-organizes itself not needing any central controller or router. Each agent of the swarm is recognized by its 32-bit unique chip ID number, making the overall system simpler to setup and quick to operate. Figure 17 shows a swarm of four robots synchronizing together to blink at the same time.

Routing: An agent synchronization message is used to share the information regarding the routing. The message includes the information about the number of nodes directly connected and all of the subconnected nodes. Thus, making all agents aware of the number of nodes connected to the mesh network. Synchronization: The robot sends a NODE_SYNC_REQUEST to all its neighboring robots. The neighbors reply with NODE_SYNC_REPLY message, containing information regarding number of robots connected to it. Then, OFFSET is calculated, and the blink period is adjusted accordingly for the synchronous blinking of all the robots together. Time offset and round-trip delay are calculated by Equations (6) and (7), respectively.:where t₀ is the internal clock value at the time of packet generation, t₁ is the timestamp on receive request, t₂ is the timestamp at response generation, and t₃ is the timestamp when response is received. This results in robots adjusting their blinking period in such a manner that they start to blink in a synchronized manner.

4. Conclusions and Future Scope

The proposed robots are versatile in nature and provide numerous features in comparison to their size. The following points conclude the work:(i)The robots provide both short-range (color coded based) and long-range (over WiFi) communication capabilities and thus can be used for studying swarming behavior covering a wider area.(ii)Robots were able to easily recognize each other based on the color-coded light signals and thus were able to transfer information regarding their pose in the environment of operation to each other.(iii)The developed robots can be operated in both centralized and decentralized modes using WiFi-based communication (global) and color-coded-based communication (local) techniques, respectively.(iv)The robot showed various swarming behaviors using visible light communication in concurrence to the simulation results.

The current work demonstrates the usage of color-coded light-based communication technique within swarm of robots. Color-coded light communication allows for the simultaneous transmission of multiple signals using different colors, allowing various robots to converse freely and without hindrance. Distinct colors may be allocated to each robot, enabling efficient communication and coordination between them as can be seen in Section 3.4. This improves spatial awareness and makes it possible for several robotic systems operating in the same area to coordinate effectively.

The current work uses only red, green, and blue colors to communicate which can be further expanded in the future to include other colors using thresholding techniques or machine learning based color recognition techniques. One possible limitation of such technique is superimposition of different color light signals in these situations, the receiver robot might not be able to correctly recognize the color, solutions to such situations need further investigation and will be explored in the future researches. Furthermore, a future version of the robot can be developed which might be equipped with more versatile sensors for better interaction of the swarm with the environment, motor encodes for better localization of individual robots of the swarm. Furthermore, more swarming behaviors can be explored.

Data Availability

No underlying data were collected or produced in this study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Copyright © 2023 Abhishek Kaushal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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