Multi-Agent Co-ordination system with the ability of swarm behavior implementation

Abstract

This invention introduces a multi-agent robotic coordination system implementing swarm behavior, inspired by biological systems such as bird flocks. It utilizes wireless communication and dynamic electromagnetic attachments for task optimization and energy efficiency. The system enables autonomous and collaborative task execution, reducing human intervention, power consumption, and computational overhead. Its modular design ensures adaptability across industries such as surveillance, logistics, agriculture, and defense. Each robot performs specific computations while collectively achieving a common goal, supported by a central master controller. Applications include pipeline inspection, smart irrigation, and warehouse management. The innovation is characterized by enhanced coordination, obstacle avoidance, and load-sharing mechanisms for continuous and efficient operation.

Specification

Description:The following specification particularly describes the invention and the manner in which it is to be performed.


Description:
Field of Invention
The invention pertains to robotics, specifically multi-agent systems and swarm robotics. It focuses on optimizing collaborative robot operations for diverse applications, including surveillance, logistics, agriculture, defense, and domestic tasks.

Background of Invention
Swarm robotics has garnered significant attention due to its potential to replicate the efficiency and adaptability of natural systems like bird flocks and ant colonies. While aerial swarm systems, such as drones, have shown promise, ground-based applications remain underexplored. Existing solutions often suffer from computational inefficiency, limited adaptability, and high power consumption. This invention aims to overcome these limitations by introducing a ground-based multi-agent coordination system with effective communication, energy optimization, and collaborative task-solving capabilities.

Prior Art
Current swarm robotic systems are primarily limited to academic research and aerial vehicles. Prototypes focus on specialized use cases, such as search and rescue operations. However, these solutions lack the flexibility for diverse industrial applications and are restricted by power and computational inefficiencies. There is minimal implementation of such systems in environments involving high human interaction or ground-level operations.

Inventive Step of Present Invention
The invention introduces novel features, including dynamic electromagnetic attachments for group mobility, distributed computational tasks, and integrated wireless communication using LAN and LoRa protocols. These features optimize power consumption, reduce resource dependency, and enable multi-tasking. By mimicking natural swarm behavior, such as shared energy utilization and obstacle avoidance, the system enhances operational efficiency and adaptability across industries.

Novelty Analysis
Key novel aspects of this invention include:
1. Electromagnetic attachment/detachment mechanisms for linear alignment and energy optimization.
2. Dual-mode wireless communication (LAN and LoRa) for robust connectivity.
3. A master controller for distributed data computation and real-time task scheduling with manual override.
4. Optimized path planning and obstacle avoidance based on coordinated movement.
5. Versatile application across industries due to modularity and adaptability.

Objectives of Invention:
1. To develop an energy-efficient, multi-agent coordination system mimicking swarm behavior.
2. To enhance task efficiency and reduce human error through collaborative robotic operations.
3. To enable adaptability for diverse industrial applications.
4. To reduce power consumption via load-sharing mechanisms.
5. To achieve precise task execution through distributed computation and centralized control.

Industrial Applications
This invention is applicable in various industries, including:
1. Surveillance: Real-time monitoring in institutions and manufacturing.
2. Warehouse Management: Efficient inventory handling and organization.
3. Agriculture: Precision tasks such as seeding, weeding, and irrigation.
4. Defense: Search and rescue, load carriage, and surveillance.
5. Domestic Applications: Cleaning and household automation.
6. Construction: Collaborative material transport and inspection.

Brief Details of Drawings for the Patent Application
Fig. 1 is a Schematic view of all components in swarm robots with magnets. This figure provides a high-level schematic representation of the swarm robot's key components. It illustrates the integration of dynamic electromagnetic attachment mechanisms, communication modules, sensors, computational units, and locomotion systems.

Fig. 2 shows the basic structural design of an individual swarm robot. It highlights the compact, modular framework suitable for seamless attachment to other robots and easy navigation in various environments.
Fig. 3 figure presents a detailed breakdown of the swarm robot with labeled internal components. It includes the electromagnetic system, communication modules, sensors, processors, and power units, demonstrating their physical placement within the robot.
Fig. 4 illustrates the design of the swarm robot without the inclusion of the electromagnetic attachment mechanism. It showcases the robot's standalone functionality and structure for scenarios where attachment features are not required.
Fig. 5 refines the robot's design for applications that do not utilize magnets, emphasizing alternative methods of coordination and energy optimization through computational strategies.
Fig. 6 revisits the swarm robot's design, explicitly detailing the arrangement of key functional components, including sensors and path-planning modules, for effective task execution and obstacle avoidance.
Fig. 7 demonstrates how swarm robots coordinate with each other in a shared workspace. It shows the robots' relative positions and the central control mechanism ensuring spatial alignment and collision avoidance.
Fig. 8 illustrates the movement of eight swarm robots navigating a shared path while avoiding velocity-based obstacles. It showcases the effectiveness of optimized path-planning algorithms in dynamic environments.
Fig. 9 depicts multiple robots moving in opposite directions to create complex patterns. It highlights the system's capability for precision movement and collision avoidance through advanced coordination algorithms.
Detailed Description
The system comprises a network of robots equipped with electromagnetic attachment mechanisms, wireless communication modules, and computational units. The robots operate under a master controller, which processes data and assigns tasks dynamically. The system utilizes coordinated movement patterns inspired by natural swarms to minimize energy usage and optimize performance.
Robots communicate using LAN and LoRa protocols for seamless connectivity. Electromagnetic attachments enable robots to align for reduced friction and shared load distribution. Path planning algorithms ensure obstacle avoidance and efficient navigation. The modular design allows customization for specific industries and tasks.
In surveillance, robots divide camera feed processing for real-time monitoring. In agriculture, robots autonomously fertilize and irrigate fields. Defense applications include terrain exploration and soldier assistance.
Mathematical equations for swarm robots :
Mathematical modelling of swarm robots is for run the motors with stability. Here, with the help of planar multibody dynamic for mathematical modelling.
Planar coordinates
Step 1: Define State and Control Vectors
Define the state vector X and control vector U:
Position of swarm robot global co-ordinates, X=[■(x@y@θ)] (1)
Velocity of swarm robot local coordinates, U=[■(ξ ̇_1@η ̇_1@⋮@ξ ̇_4@η ̇_4 )] (2)
Transformation Matrix:
A=[■("cos" (θ)&-"sin" (θ)@"sin" (θ)&"cos" (θ) )] (3)
r=[■(x@y)] (4)
$=[■(ξ@η)] (5)
Step 2: Local Velocity to Global Velocity Transformation
Transform the local velocities into the global coordinate system[39]:

For motor 1:
Position of motor 1:
ω_1=r_1^p+A$_2^p (6)
First Position of a swarm by multiplying equations (3), (4), (5).
ω_1=[■(x_1@y_! )][■("cos" (θ_1 )&-"sin" (θ_! )@"sin" (θ_1 )&"cos" (θ_! ) )][■(ξ@η)] (7)
Multiplying all in linear formation
[■(x_1+ξ_1 "cos" (θ_1 )-η_1 "sin" (θ_1 )@y_!+ξ_1 "sin" (θ_1 )-η_1 "cos" (θ_1 ) )] (8)
ω_1=[■(1&0&ξ_1 "cos" (θ_1 )-η_1 "sin" (θ_1 )@0&1&ξ_1 "sin" (θ_1 )-η_1 "cos" (θ_1 ) )][■(x ̇_1@y ̇_1@θ_1 )] (9)
Similarly, for motors 2,3,4 we will get angular velocity as shown in equations (10), (11), (12)
ω_2=[■(1&0&ξ_2 "cos" (θ_2 )-η_2 "sin" (θ_2 )@0&1&ξ_2 "sin" (θ_2 )-η_2 "cos" (θ_2 ) )][■(x ̇_2@y ̇_2@θ_2 )] (10)
ω_3=[■(1&0&ξ_3 "cos" (θ_3 )-η_3 "sin" (θ_3 )@0&1&ξ_3 "sin" (θ_3 )-η_3 "cos" (θ_3 ) )][■(x ̇_3@y ̇_3@θ_3 )] (11)
ω_4=[■(1&0&ξ_4 "cos" (θ_4 )-η_4 "sin" (θ_4 )@0&1&ξ_4 "sin" (θ_4 )-η_4 "cos" (θ_4 ) )][■(x ̇_4@y ̇_4@θ_4 )] (12)

Step 3: Velocity of swarm Robot
Combine the transformation matrices and perform the matrix multiplication: by combining and multiplying the matrix equations (9),(10), (11), (12), then we will get equation (14).
ω =[■(1&0&ξ_1 "c" (θ_1 )-η_1 "s" (θ_1 )&0&0&0@0&1&ξ_1 "s" (θ_1 )-η_1 "c" (θ_1 )&0&0&0@0&1&0&ξ_2 "c" (θ_2 )-η_2 "s" (θ_2 )&0&0@0&0&1&ξ_2 "s" (θ_2 )-η_2 "c" (θ_2 )&0&0@0&0&1&0&ξ_3 "c" (θ_3 )-η_3 "s" (θ_3 )&0@0&0&0&1&ξ_3 "s" (θ_3 )-η_3 "c" (θ_3 )&0@0&0&0&1&0&ξ_4 "c" (θ_4 )-η_4 "s" (θ_4 )@0&0&0&0&1&ξ_4 "s" (θ_4 )-η_4 "c" (θ_4 ) )][■(x ̇_1@y ̇_1@θ_1@x ̇_2@y ̇_2@θ_2@x ̇_3@y ̇_3@θ_3@x ̇_4@y ̇_4@θ_4 )]
..(13)
Spatial coordinates
To represent spatial coordinates in three dimensions (x, y, z) and local coordinates (xi, eta, zeta), we can modify the equations accordingly. Let's redefine the state vector and control vector in terms of spatial coordinates and transform the velocity equations into three-dimensional space.

Step 1: Define State and Control Vectors
Define the state vector \( X \) and control vector \( U \) for spatial coordinates:
Position of swarm robot global co-ordinates, X=[■(x@y@z@θ)] (14)
Velocity of local coordinates, U={├ █((ξ_i ) ̇@(η_1 ) ̇@(ζ_1 ) ̇@θ_1@.@.@(ξ_4 ) ̇@η ̇_4@ζ ̇_4@θ_4 )}┤ (15)

Step 2: Local Velocity to Global Velocity Transformation
Transform the local velocities into the global coordinate system using the transformation matrix \( A \) as before:
A= [├ ■(cos(θ)& sin(θ)&0@ -sin(θ)&cos(θ)&0@0&0&1)]┤ (16)
For each motor \( i \), the global velocity is multiplied with equations (14)(15)(16), it can be expressed as:
ω_i={■(1&0&0&ξ_i cosθ_i-η_i sinθ_i@0&1&0&ξ_i sinθ_i-η_i cosθ_i@0&0&1&ζ_i )}{■(x ̇_i@y ̇_i@z ̇_i@θ_i )} (17)
For each motor we need to change the value i=1,2,3,4. By multiplying those all 4 motor equations with equation 17 final expression will come as equation 18.

Step 3: Velocity of Swarm Robot
Combine the transformation matrices for all motors (i=1,2,3,4) and perform matrix multiplication to get the global velocity of equation (18) of the swarm robot in three-dimensional space[40]:
ω={■(1&0&0&ξ_1 cosθ_1-η_1 sinθ_1&0&0&0&0&0&0&0&0&0&0&0&0@0&1&0&ξ_1 sinθ_1-η_1 cosθ_1&0&0&0&0&0&0&0&0&0&0&0&0@0&0&1&ζ_1&0&0&0&0&0&0&0&0&0&0&0&0@0&0&0&0&1&0&0&ξ_2 cosθ_2-η_2 sinθ_2&0&0&0&0&0&0&0&0@0&0&0&0&0&1&0&ξ_2 sinθ_2-η_2 cosθ_2&0&0&0&0&0&0&0&0@0&0&0&0&0&0&1&ζ_2&0&0&0&0&0&0&0&0@0&0&0&0&0&0&0&0&1&0&0&ξ_3 cosθ_3-η_3 sinθ_3&0&0&0&0@0&0&0&0&0&0&0&0&0&1&0&ξ_3 sinθ_3-η_3 cosθ_3&0&0&0&0@0&0&0&0&0&0&0&0&0&0&1&ζ_i&0&0&0&0@0&0&0&0&0&0&0&0&0&0&0&0&1&0&0&ξ_4 cosθ_4-η_4 sinθ_4@0&0&0&0&0&0&0&0&0&0&0&0&0&1&0&ξ_4 sinθ_4-η_4 cosθ_4@0&0&0&0&0&0&0&0&0&0&0&0&0&0&1&ζ_4 )}{■(x ̇_1@y ̇_1@z ̇_1@θ_1@x ̇_2@y ̇_2@z ̇_2@θ_2@x ̇_3@y ̇_3@z ̇_3@θ_3@x ̇_4@y ̇_4@(z_4 ) ̇@θ_4 )} (18)
Equation was created for velocity obstacles with algorithms.
\[VO=V_max-(∑_(j=1)^N▒V_ij )/N \]

Integrating equations of planar coordinates, velocity obstacles with PSO will get.
\[v_i (t+1)=ωv_i (t)+c_1 r_1 (p_i-x_i (t))+c_2 r_2 (p_g-x_i (t))+VO\]
Integrating equations of planar coordinates, velocity obstacles with Rendezvous will get.
\[v_i (t+1)=〖ωv〗_i (t)+c⋅(p_avg-x_i (t))+VO\]

Solutions in Matlab
This program is intended to help 08 swarm robots travel about a specific region. It begins by initializing several parameters, including the maximum relative speed, average relative speed, and area dimensions are discussed in algorithm2. It also establishes variables linked to the computing environment and clears any prior data to start from scratch. The robots are then manufactured and placed in the designated region. The code provides conditions for configuring the robot configuration based on certain criteria, such as a value of 'x' equal to 7. If this condition is fulfilled, the code will specify characteristics for the robots' movement, such as speed, acceleration, and size shown in Fig. 8. The robots are deliberately arranged in a grid-like layout around the region.
The algorithm then employs trigonometric functions to compute the robot locations along the circle's circumference shown in fig.9. Each robot is allocated a place and color depending on the calculated coordinates and assigned attributes. The system incorporates visualization components that show the robots' motions and follow their progress to the rendezvous location in a circular pattern. This picture helps to examine the success of the rendezvous algorithm in coordinating the robots' motions and obtaining the required shape.
, Claims:We Claim:
1. A multi-agent robotic coordination system implementing swarm behavior, comprising:
- a network of robots equipped with wireless communication modules;
- dynamic electromagnetic attachment mechanisms for energy optimization;
- a central master controller for real-time data computation and task assignment;
- path planning algorithms for obstacle avoidance; and
- modular adaptability for diverse industrial applications, enabling autonomous task execution.
2. The system of Claim 1, wherein robots communicate via LAN and LoRa protocols for enhanced connectivity in varying environments.
3. The system of Claim 1, wherein the electromagnetic attachment reduces friction and distributes load across connected robots to optimize power consumption.
4. The system of Claim 1, further comprising obstacle avoidance mechanisms using optimized path planning algorithms.
5. The system of Claim 1, wherein robots autonomously divide computational tasks for efficient resource utilization.
6. The system of Claim 1, configured for agricultural applications, wherein robots autonomously fertilize, seed, and irrigate fields.
7. The system of Claim 1, configured for surveillance applications, wherein individual robots process visual data for real-time monitoring.
8. A method for autonomous multi-agent coordination, comprising:
- enabling communication among robots via dual-mode protocols;
- dynamically attaching robots using electromagnetic mechanisms;
- distributing computational tasks among robots;
- processing data through a master controller for real-time task allocation; and
- optimizing navigation and obstacle avoidance using path planning algorithms.
9. The method of Claim 8, wherein electromagnetic attachments align robots linearly for reduced energy consumption.

10. The method of Claim 8, adaptable for defense applications, wherein robots assist in search operations and terrain navigation.

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