AUTONOMOUS MOVEMENT AGENTS POWERED BY ARTIFICIAL INTELLIGENCE

Abstract

ABSTRACT AUTONOMOUS MOVEMENT AGENTS POWERED BY ARTIFICIAL INTELLIGENCE The present disclosure introduces an autonomous movement agent powered by artificial intelligence 100, designed for independent navigation and task execution in dynamic environments. The invention utilizes a multi-modal sensor fusion system 102 integrating LiDAR, cameras, ultrasonic sensors, and IMUs for precise environmental mapping. A central processing unit (CPU) / graphics processing unit (GPU) 104 processes sensor data and executes AI algorithms, while the machine learning framework 106 enables the agent to adapt and improve based on real-time feedback. Optimal routes are calculated by the dynamic path planning algorithm 108, and the context-aware decision-making system 114 tailors actions to specific situational factors. Collaborative coordination protocol 110 allows multi-agent communication, and safety and compliance features 112 ensure regulatory adherence and safe operation. The energy efficiency optimization system 118 conserves power dynamically, and the advanced obstacle avoidance system 126 uses predictive analytics to prevent collisions, enhancing adaptability and safety. Reference Fig 1

Specification

Description:AUTONOMOUS MOVEMENT AGENTS POWERED BY ARTIFICIAL INTELLIGENCE
TECHNICAL FIELD
[0001] The present innovation relates to autonomous movement agents powered by artificial intelligence (AI), designed for independent navigation and task execution across diverse environments.

BACKGROUND

[0002] The advent of artificial intelligence (AI) has spurred remarkable progress in automation, yet autonomous movement in complex environments remains a challenge. Existing options for users seeking autonomous agents, such as simple robotic systems in manufacturing or warehouse automation, often lack the advanced perception, adaptability, and decision-making needed for dynamic, real-world applications. Traditional robotics rely on predefined programming and limited sensor data, restricting their ability to respond to unpredictable scenarios and creating a dependency on human intervention for navigation and error correction. Additionally, options like self-driving vehicles or drones with basic autonomous capabilities are constrained by limited environmental awareness, which can impact their safety, efficiency, and adaptability in varied or challenging environments.

[0003] This invention introduces autonomous movement agents powered by advanced AI, offering capabilities that surpass those of conventional systems. By integrating a multi-modal sensor fusion system-encompassing LiDAR, cameras, ultrasonic sensors, and inertial measurement units (IMUs)-the agents achieve a comprehensive environmental understanding, enabling more precise mapping and obstacle detection even under low light or adverse conditions. Unlike conventional systems, these agents employ adaptive machine learning algorithms that continuously update and refine decision-making processes based on real-time environmental feedback. This allows them to self-optimize and enhance performance over time, minimizing human oversight.

[0004] The novelty of this invention lies in its blend of advanced AI, multi-sensor integration, and features such as dynamic path planning, real-time environmental adaptation, and collaborative task coordination. These agents can autonomously adapt to complex, multi-agent tasks, offering significant improvements in efficiency, safety, and reliability across sectors like manufacturing, logistics, transportation, and healthcare. By addressing the limitations of existing autonomous systems, this invention enables more resilient, capable, and versatile agents that can transform automation across industries

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to provide autonomous movement agents powered by artificial intelligence (AI) that can independently navigate and perform tasks in diverse environments without human intervention.

[0006] Another object of the invention is to enhance operational efficiency across industries by offering adaptable agents capable of optimizing tasks based on real-time data and environmental feedback.

[0007] Another object of the invention is to improve safety in shared human environments by equipping agents with advanced multi-sensor fusion, enabling precise obstacle detection and avoidance under various conditions.

[0008] Another object of the invention is to enable autonomous agents to make context-aware decisions by integrating AI-driven algorithms that assess situational factors such as time, location, and task priority.
[0009] Another object of the invention is to reduce dependency on human labor for repetitive or hazardous tasks, allowing autonomous agents to handle these tasks safely and efficiently.

[00010] Another object of the invention is to increase flexibility and scalability by designing agents with cross-domain functionality, making them suitable for various applications like manufacturing, logistics, healthcare, and transportation.

[00011] Another object of the invention is to promote sustainability by incorporating energy efficiency optimization systems and, where possible, energy harvesting mechanisms, enhancing the agents' operational longevity and reducing environmental impact.

[00012] Another object of the invention is to improve user experience by providing agents with a user-centric interaction interface, enabling easy communication and control through voice recognition, gesture control, and mobile app integration.

[00013] Another object of the invention is to facilitate collaborative work among multiple agents through a coordinated communication network that allows seamless information sharing and task synchronization.

[00014] Another object of the invention is to ensure regulatory compliance and reliability through enhanced safety features, including real-time hazard detection, adherence to operational protocols, and customizable task execution for diverse industry needs.

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, autonomous movement agents powered by artificial intelligence is presented. The invention comprises autonomous movement agents powered by artificial intelligence (AI) that can independently navigate, perceive, and adapt to diverse environments. By integrating multi-modal sensors and advanced machine learning algorithms, these agents achieve real-time decision-making and efficient task execution with minimal human oversight. They are designed for applications across various industries, including logistics, manufacturing, healthcare, and smart cities. Key features include dynamic path planning, context-aware decision-making, and energy efficiency optimization. This invention aims to enhance operational efficiency, safety, and adaptability in complex environments.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for autonomous movement agents powered by artificial intelligence.

[00021] FIG 2 is working methodology of autonomous movement agents powered by artificial intelligence.

DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of autonomous movement agents powered by artificial intelligence and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, autonomous movement agents powered by artificial intelligence 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of multi-modal sensor fusion system 102, central processing unit (CPU) / graphics processing unit (GPU) 104, machine learning framework 106, dynamic path planning algorithm 108, collaborative coordination protocol 110, safety and compliance features 112, context-aware decision-making system 114, user-centric interaction interface 116, energy efficiency optimization system 118, customizable task execution protocols 120, predictive maintenance system 122, inter-agent communication network 124, advanced obstacle avoidance system 126, augmented reality (AR) integration 128, multi-layered security protocols 130, cloud-based analytics and reporting 132, energy harvesting mechanism 134 and hybrid learning models 136.

[00029] Referring to Fig. 1, the present disclosure provides details of autonomous movement agents powered by artificial intelligence 100. It is a system designed to enable seamless task execution across complex environments by integrating multi-modal sensor fusion system 102 and machine learning framework 106. The autonomous agent 100 utilizes central processing unit (CPU) / graphics processing unit (GPU) 104 to process real-time data from sensors, optimizing movement through dynamic path planning algorithm 108. In one of the embodiments, the autonomous movement agent 100 may be equipped with context-aware decision-making system 114, collaborative coordination protocol 110, and customizable task execution protocols 120, allowing adaptation to specific operational needs. Additional features include energy efficiency optimization system 118 and predictive maintenance system 122 for sustained operation, while multi-layered security protocols 130 ensure data integrity. Further enhancements such as cloud-based analytics and reporting 132 and energy harvesting mechanism 134 support robust, long-term functionality.

[00030] Referring to Fig. 1, autonomous movement agents powered by artificial intelligence 100 are provided with multi-modal sensor fusion system 102, which integrates data from LiDAR, cameras, ultrasonic sensors, and inertial measurement units (IMUs) to achieve accurate environmental mapping. This system plays a critical role in ensuring that the agents can detect obstacles, recognize objects, and assess distances. The data processed by multi-modal sensor fusion system 102 is sent to central processing unit (CPU) / graphics processing unit (GPU) 104, which utilizes this input for real-time decision-making. Together, these components enable the agents to perceive their surroundings accurately and respond to dynamic environments.


[00031] Referring to Fig. 1, autonomous movement agents 100 are provided with central processing unit (CPU) / graphics processing unit (GPU) 104, responsible for processing high volumes of sensor data and running complex AI algorithms. It works in conjunction with machine learning framework 106 to ensure efficient data analysis and decision-making. The CPU / GPU 104 performs real-time computations that allow the agents to adapt their actions based on environmental feedback. By interfacing with multi-modal sensor fusion system 102, this component ensures timely processing of critical data for seamless navigation and movement.

[00032] Referring to Fig. 1, autonomous movement agents 100 are provided with machine learning framework 106, which encompasses various learning models such as supervised, unsupervised, and reinforcement learning. This framework enables agents to learn from experience, optimize their behaviors, and adapt to changing conditions. It continuously refines the decision-making algorithms run on central processing unit (CPU) / graphics processing unit (GPU) 104 based on real-time data. By collaborating with dynamic path planning algorithm 108, the machine learning framework 106 helps agents make informed navigation decisions that enhance their operational efficiency.

[00033] Referring to Fig. 1, autonomous movement agents 100 are provided with dynamic path planning algorithm 108, which allows agents to determine optimal routes and adjust their paths in response to real-time environmental changes. The algorithm continuously updates movement strategies based on inputs from multi-modal sensor fusion system 102 and decisions processed by machine learning framework 106. Dynamic path planning algorithm 108 ensures that agents navigate efficiently even in complex or unpredictable settings, avoiding obstacles and reaching designated locations with minimal error.

[00034] Referring to Fig. 1, autonomous movement agents 100 are provided with collaborative coordination protocol 110, a system enabling seamless communication and collaboration between multiple agents. This protocol allows agents to share data, such as environmental insights and task statuses, with each other, optimizing collective task execution. Collaborative coordination protocol 110 works closely with inter-agent communication network 124 to synchronize actions and improve coordination in multi-agent scenarios. By facilitating real-time data exchange, it ensures that agents operate cohesively to achieve complex objectives more efficiently.

[00035] Referring to Fig. 1, autonomous movement agents 100 are provided with safety and compliance features 112, which incorporate hazard detection, emergency stop functions, and adherence to regulatory protocols. These features play a crucial role in ensuring that the agents operate safely in environments shared with humans. Safety and compliance features 112 work alongside multi-modal sensor fusion system 102 to detect obstacles and potential hazards in real time. In the event of an imminent threat, these features trigger quick response mechanisms to halt the agent's movement, enhancing operational safety and reliability.

[00036] Referring to Fig. 1, autonomous movement agents 100 are provided with context-aware decision-making system 114, which enables the agents to make informed decisions based on situational factors like time, location, and task priority. This system interprets data from machine learning framework 106 and dynamic path planning algorithm 108 to optimize actions according to the context. By factoring in real-world conditions, the context-aware decision-making system 114 helps the agents prioritize tasks, adjust strategies, and operate more effectively in diverse environments.


[00037] Referring to Fig. 1, autonomous movement agents 100 are provided with user-centric interaction interface 116, which allows human operators to interact with the agents using voice recognition, gesture control, and mobile app integration. This interface is designed to facilitate seamless and intuitive communication between users and agents. The user-centric interaction interface 116 coordinates with central processing unit (CPU) / graphics processing unit (GPU) 104 to interpret user inputs and execute commands accurately, making it easy for operators to control or adjust agent behaviour as needed.

[00038] Referring to Fig. 1, autonomous movement agents 100 are provided with energy efficiency optimization system 118, which monitors the agents' energy consumption and adjusts operational parameters to conserve power. This system is essential for prolonged operation and sustainability, particularly in resource-constrained settings. The energy efficiency optimization system 118 works with dynamic path planning algorithm 108 to manage energy usage based on route complexity and task demands, enabling the agents to perform tasks while maintaining energy efficiency.

[00039] Referring to Fig. 1, autonomous movement agents 100 are provided with customizable task execution protocols 120, which allow users to define specific tasks, priorities, and performance metrics based on operational needs. These protocols give agents flexibility in handling diverse tasks across sectors, such as manufacturing, logistics, and healthcare. Customizable task execution protocols 120 collaborate with machine learning framework 106 to adapt task strategies dynamically, allowing agents to respond to user-defined objectives and optimize task performance effectively.

[00040] Referring to Fig. 1, autonomous movement agents 100 are provided with predictive maintenance system 122, which leverages machine learning to forecast potential failures and maintenance needs. This system analyzes data from central processing unit (CPU) / graphics processing unit (GPU) 104 to identify patterns that may indicate wear or malfunction, ensuring timely maintenance. Predictive maintenance system 122 helps minimize downtime and enhance the lifespan of the agents, contributing to uninterrupted and reliable operation.

[00041] Referring to Fig. 1, autonomous movement agents 100 are provided with inter-agent communication network 124, a dedicated system for enabling real-time data sharing and coordination among multiple agents. This network facilitates seamless information exchange between agents, allowing them to collaborate on complex tasks. Inter-agent communication network 124 works in conjunction with collaborative coordination protocol 110 to enhance multi-agent collaboration, synchronizing actions and optimizing resource allocation in multi-agent environments.

[00042] Referring to Fig. 1, autonomous movement agents 100 are provided with advanced obstacle avoidance system 126, which uses predictive analytics to anticipate the movement of dynamic obstacles such as pedestrians or vehicles. This system receives real-time environmental data from multi-modal sensor fusion system 102 to proactively adjust the agent's path and avoid collisions. The advanced obstacle avoidance system 126 ensures that the agents can operate safely in dynamic environments, enhancing both operational efficiency and user safety.

[00043] Referring to Fig. 1, autonomous movement agents 100 are provided with augmented reality (AR) integration 128, which enables human operators to receive real-time visual feedback and situational awareness. AR integration 128 overlays relevant data and instructions onto the user's environment, improving decision-making and collaboration with the agents. This component interacts with user-centric interaction interface 116 to present operators with intuitive visualizations, enhancing the clarity and effectiveness of human-agent interactions.

[00044] Referring to Fig. 1, autonomous movement agents 100 are provided with multi-layered security protocols 130, designed to ensure data integrity, privacy, and protection against unauthorized access. These protocols are essential for applications involving sensitive information, such as healthcare or finance. Multi-layered security protocols 130 work with inter-agent communication network 124 to safeguard data transmitted among agents and ensure secure operations, maintaining the trustworthiness of the agents in all environments.

[00045] Referring to Fig. 1, autonomous movement agents 100 are provided with cloud-based analytics and reporting 132, which connects agents to cloud platforms for real-time monitoring and performance analysis. This system enables operators to access comprehensive data on agent activities, efficiency, and user interactions, supporting data-driven decision-making. Cloud-based analytics and reporting 132 also collaborates with predictive maintenance system 122 to track performance metrics, ensuring timely identification of improvement areas.

[00046] Referring to Fig. 1, autonomous movement agents 100 are provided with energy harvesting mechanism 134, which collects energy from the environment, such as solar or kinetic energy, to supplement the agents' power needs. This mechanism is particularly beneficial in remote or off-grid applications, enhancing the sustainability and operational longevity of the agents. Energy harvesting mechanism 134 works alongside energy efficiency optimization system 118 to maintain consistent energy availability, supporting uninterrupted agent functionality.

[00047] Referring to Fig. 1, autonomous movement agents 100 are provided with hybrid learning models 136, combining supervised, unsupervised, and reinforcement learning to improve learning efficiency and adaptability. These models enable agents to tackle a wide range of tasks and respond to diverse environments by leveraging various data types. Hybrid learning models 136 work in tandem with machine learning framework 106 to refine decision-making processes, enhancing the agents' ability to operate effectively in complex, real-world scenarios.

[00048] Referring to Fig 2, there is illustrated method 200 for autonomous movement agents powered by artificial intelligence 100. The method comprises:
At step 202, method 200 includes multi-modal sensor fusion system 102 collecting real-time environmental data from LiDAR, cameras, ultrasonic sensors, and IMUs;

At step 204, method 200 includes central processing unit (CPU) / graphics processing unit (GPU) 104 processing this data and running AI algorithms to interpret the surroundings and detect obstacles;

At step 206, method 200 includes machine learning framework 106 analyzing processed data to identify patterns, update decision-making models, and predict potential changes in the environment;

At step 208, method 200 includes dynamic path planning algorithm 108 determining an optimal route based on environmental data and adjusting for any detected obstacles or changes in the surroundings;

At step 210, method 200 includes context-aware decision-making system 114 evaluating situational factors such as time, location, and task priority to refine the navigation strategy in real-time;

At step 212, method 200 includes collaborative coordination protocol 110 enabling multiple agents to communicate and synchronize their tasks, optimizing coordinated movement where necessary;

At step 214, method 200 includes safety and compliance features 112 continuously monitoring the environment for hazards, allowing the agent to trigger emergency stop functions if required to ensure safe operation;

At step 216, method 200 includes user-centric interaction interface 116 enabling operators to provide input or adjust agent behaviour as needed, which is then processed by central processing unit (CPU) / graphics processing unit (GPU) 104;

At step 218, method 200 includes energy efficiency optimization system 118 monitoring energy consumption, adjusting operational parameters to extend battery life and sustain efficient performance during prolonged tasks;

At step 220, method 200 includes predictive maintenance system 122 analyzing real-time performance data to detect any issues, notify maintenance needs, and prevent potential downtime;

At step 222, method 200 includes inter-agent communication network 124 facilitating data sharing and collaboration among agents, allowing them to collectively adapt to dynamic task requirements or environmental changes;

At step 224, method 200 includes advanced obstacle avoidance system 126 using predictive analytics to anticipate the movement of dynamic obstacles, adjusting the agent's path proactively to avoid collisions;

At step 226, method 200 includes cloud-based analytics and reporting 132 capturing performance data and generating insights accessible to operators for monitoring operational metrics and identifying areas for improvement;
At step 228, method 200 includes hybrid learning models 136 integrating feedback from previous steps to continuously refine decision-making models, further enhancing agent adaptability and effectiveness across diverse tasks.


[00049] In different embodiments, the invention of Autonomous Movement Agents can be applied in different ways. It can be applied across a wide range of industries, transforming efficiency and safety. In one of the embodiments in manufacturing, these agents can perform repetitive and precision tasks such as assembly, welding, and quality inspection, working alongside human operators to enhance productivity and reduce human error.

[00050] In another embodiment in logistics and supply chain management, they can autonomously transport goods, manage inventory, and optimize delivery routes within warehouses, thereby reducing labour costs and accelerating operations.

[00051] In yet another embodiment in autonomous vehicles, these agents allow self-driving cars and trucks to navigate complex road environments, potentially improving road safety, reducing traffic congestion, and optimizing fuel efficiency.

[00052] In yet another embodiment in agriculture, autonomous drones and ground vehicles equipped with these agents can perform tasks like crop monitoring, irrigation management, and harvesting, enhancing resource utilization and crop yields.

[00053] In yet another embodiment in smart home and healthcare environments, the agents can be deployed to manage security systems, assist with routine tasks,

[00054] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00055] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00056] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. An autonomous movement agents powered by artificial intelligence 100 comprising of
multi-modal sensor fusion system 102 to gather real-time environmental data from various sensors;
central processing unit (CPU) / graphics processing unit (GPU) 104 to process data and execute AI algorithms for decision-making;
machine learning framework 106 to enhance adaptability by learning from environmental feedback;
dynamic path planning algorithm 108 to calculate and adjust optimal routes; context-aware decision-making system 114 to tailor actions based on situational factors;
collaborative coordination protocol 110 to enable seamless communication between agents;
safety and compliance features 112 to ensure secure and regulated operation;
user-centric interaction interface 116 to facilitate easy user interaction through voice and gesture;
energy efficiency optimization system 118 to manage and conserve energy for prolonged use;
predictive maintenance system 122 to monitor performance and forecast maintenance needs;
inter-agent communication network 124 to allow data sharing among agents;
advanced obstacle avoidance system 126 to anticipate and evade dynamic obstacles;
cloud-based analytics and reporting 132 to provide insights and performance metrics; and
hybrid learning models 136 to support diverse learning techniques for enhanced agent functionality.


2. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the multi-modal sensor fusion system 102 is configured to integrate data from LiDAR, cameras, ultrasonic sensors, and IMUs, enabling high-resolution environmental mapping and precise obstacle detection under diverse conditions, facilitating adaptive navigation.

3. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the central processing unit (CPU) / graphics processing unit (GPU) 104 is configured to process extensive sensor data and execute advanced AI algorithms, supporting real-time decision-making and task execution in dynamic environments.

4. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the machine learning framework 106 is configured to use adaptive learning models, including reinforcement and unsupervised learning, to improve agent behaviour over time based on real-time environmental feedback, enhancing operational adaptability.

5. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the dynamic path planning algorithm 108 is configured to calculate and adjust optimal routes in real time, adapting to environmental changes, obstacle placement, and task priority to ensure efficient navigation.

6. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the collaborative coordination protocol 110 is configured to enable seamless communication and task synchronization among multiple agents, optimizing task execution and resource allocation in multi-agent environments.

7. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the safety and compliance features 112 are configured to ensure regulatory adherence and operational safety through continuous hazard monitoring, emergency stop functions, and compliance with predefined safety protocols.

8. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the energy efficiency optimization system 118 is configured to monitor and adjust energy consumption dynamically, prolonging operational periods by optimizing power usage based on task demands and environmental conditions.

9. The autonomous movement agent powered by artificial intelligence 100 as claimed in claim 1, wherein the advanced obstacle avoidance system 126 is configured to use predictive analytics to anticipate the movement of dynamic obstacles, enabling proactive path adjustment to avoid potential collisions and enhance safety in complex environments.

10. The autonomous movement agents powered by artificial intelligence 100 as claimed in claim 1, wherein method comprises of
multi-modal sensor fusion system 102 collecting real-time environmental data from LiDAR, cameras, ultrasonic sensors, and IMUs;

central processing unit (CPU) / graphics processing unit (GPU) 104 processing this data and running AI algorithms to interpret the surroundings and detect obstacles;

machine learning framework 106 analyzing processed data to identify patterns, update decision-making models, and predict potential changes in the environment;

dynamic path planning algorithm 108 determining an optimal route based on environmental data and adjusting for any detected obstacles or changes in the surroundings;

context-aware decision-making system 114 evaluating situational factors such as time, location, and task priority to refine the navigation strategy in real-time;

collaborative coordination protocol 110 enabling multiple agents to communicate and synchronize their tasks, optimizing coordinated movement where necessary;

safety and compliance features 112 continuously monitoring the environment for hazards, allowing the agent to trigger emergency stop functions if required to ensure safe operation;

user-centric interaction interface 116 enabling operators to provide input or adjust agent behaviour as needed, which is then processed by central processing unit (CPU) / graphics processing unit (GPU) 104;

energy efficiency optimization system 118 monitoring energy consumption, adjusting operational parameters to extend battery life and sustain efficient performance during prolonged tasks;

predictive maintenance system 122 analyzing real-time performance data to detect any issues, notify maintenance needs, and prevent potential downtime;

inter-agent communication network 124 facilitating data sharing and collaboration among agents, allowing them to collectively adapt to dynamic task requirements or environmental changes;

advanced obstacle avoidance system 126 using predictive analytics to anticipate the movement of dynamic obstacles, adjusting the agent's path proactively to avoid collisions;

cloud-based analytics and reporting 132 capturing performance data and generating insights accessible to operators for monitoring operational metrics and identifying areas for improvement;

hybrid learning models 136 integrating feedback from previous steps to continuously refine decision-making models, further enhancing agent adaptability and effectiveness across diverse tasks.

Documents