Navigation system for guiding target user

Abstract

A navigation system for guiding a target user, comprising: a user tracking module arranged to track a movement of the target user; a navigation vehicle arranged to guide a target user to move toward a predetermined destination through a trajectory relative to a movement of the tracked target user; wherein the user tracking module is arranged to track movement of the target user from a leading location relative to the tracked target user moving in the forward direction.

Description

Technical Field

[0001] This invention relates to a navigation system for guiding target users, and more particularly, but not limited to, a navigation system for guiding target users across multiple industries and scenarios. Background Technology

[0002] In recent years, with the booming development of e-commerce, warehousing operations have faced new challenges. The shift from B2B to B2C operating models has led to more decentralized warehousing and logistics tasks.

[0003] Traditional "follow-me" robots can help workers move goods and follow them around the warehouse. This reduces the workload of warehouse operators to some extent and improves overall logistics efficiency. Summary of the Invention

[0004] A first aspect of the present invention relates to a navigation system for guiding a target user, comprising:

[0005] A user tracking module, which is configured to track the movement of a target user;

[0006] A navigation vehicle, the navigation vehicle being deployed to guide the target user toward a predetermined destination by means of a trajectory relative to the movement of the target user being tracked;

[0007] The user tracking module is configured to track the movement of the target user from a leading position relative to the target user moving in the forward direction.

[0008] In an embodiment of the first aspect, the user tracking module is arranged to track the movement of the target user from a backward direction relative to the forward movement of the navigation vehicle.

[0009] In an embodiment of the first aspect, the user tracking module is configured to determine the distance between the navigation vehicle and the target user and maintain a minimum distance between them, the minimum distance being greater than or equal to at least a predetermined threshold.

[0010] In an embodiment of the first aspect, a controller is also included, the controller being configured to adjust the trajectory of the navigation vehicle based on the location of the target user.

[0011] In an embodiment of the first aspect, the controller is configured to adjust the speed of the navigation vehicle based on the distance between the navigation vehicle and the target user, the speed of the navigation vehicle being inversely proportional to the distance between the navigation vehicle and the target user.

[0012] In an embodiment of the first aspect, the user tracking module is configured to track the presence of the target user in the region of interest, and autonomously navigate to a predetermined temporary destination in response to the target user not being in the region of interest for a first predetermined time period.

[0013] In an embodiment of the first aspect, an image capture module is also included, the image capture module being arranged to capture multiple images related to the movement of the target user.

[0014] In an embodiment of the first aspect, the image capture module further includes a depth camera arranged to measure the distance between the navigation vehicle and the target user.

[0015] In an embodiment of the first aspect, the image capture module is configured to identify facial features related to the target user from the captured image.

[0016] In an embodiment of the first aspect, the image capture module is configured to re-identify facial features associated with the target user from captured images when the target user is not in the field of view of the image capture module for a predetermined time period.

[0017] In an embodiment of the first aspect, the image capture module is movably positioned on the navigation vehicle.

[0018] In an embodiment of the first aspect, the navigation vehicle further includes a base on which the image capture module is movably mounted, such that the orientation of the image capture module is adjustable to keep the target user within the field of view of the image capture module.

[0019] In an embodiment of the first aspect, the user tracking module further includes a pre-trained object detection algorithm configured to process images associated with the movement of the target user.

[0020] In an embodiment of the first aspect, the pre-trained object detection algorithm includes the YOLO algorithm.

[0021] In an embodiment of the first aspect, an audio capture module is also included, the audio capture module being configured to capture multiple audio outputs associated with the target user.

[0022] In an embodiment of the first aspect, a speech recognition module is also included, the speech recognition module being configured to recognize spoken commands of the target user from the captured audio output.

[0023] In an embodiment of the first aspect, the speech recognition module is arranged to trigger a voice command mode by comparing the captured audio output with a predetermined reference signal.

[0024] In an embodiment of the first aspect, a communication module is also included, the communication module being configured to publish one or more states associated with the navigation vehicle.

[0025] In an embodiment of the first aspect, the object tracking module is configured to track the presence of the target user in the region of interest and to alert the target user in response to the target user being absent from the region of interest for a second predetermined time period longer than a first predetermined time period.

[0026] In an embodiment of the first aspect, the navigation vehicle remains within the target user's line of sight throughout the entire process of guiding the target user to the predetermined destination. Attached Figure Description

[0027] Exemplary embodiments will now be described by way of example only, with reference to the accompanying drawings, wherein:

[0028] Figure 1 This is a schematic diagram of a navigation system according to an embodiment of the present invention, shown from a top view.

[0029] Figure 2 This is a block diagram illustrating the software and hardware components of a navigation system according to an embodiment of the present invention.

[0030] Figure 3 This is a flowchart illustrating the logic of a "Me-Follow" master controller according to an embodiment of the present invention.

[0031] Figure 4 This is a graph illustrating the relationship between object distance and robot speed according to an embodiment of the present invention. Detailed Implementation

[0032] Unbound by theory, the inventors discovered that traditional "follow-me" robots, designed to follow a target person, require the person to frequently look back to ensure the robot is still following. This constant need for visual confirmation can be inconvenient and distracting, especially when carrying important items or walking in crowded environments. Furthermore, if the robot cannot see the target, it may become disoriented or stop operating entirely, leaving people stranded and their belongings lost.

[0033] In this invention, the inventors designed a "Front-Follow Me" robot (more preferably "Me-Follow"). It reverses the roles of the robot and the target person in traditional "Follow-me" robots, allowing the robot to lead the way while the person follows. Therefore, this invention can alleviate one or more problems of existing traditional "Follow-me" robots.

[0034] refer to Figure 1 An embodiment of a navigation system 100 for guiding a target user is shown, comprising: a user tracking module 110 arranged to track the movement of a target user 10; and a navigation vehicle 120 arranged to guide the target user 10 toward a predetermined destination via a trajectory relative to the movement of the tracked target user; wherein the user tracking module 110 is arranged to track the movement of the target user 10 from a leading position moving along a forward direction 30 relative to the tracked target user 10.

[0035] For the purposes of this document, the term "target user" includes any type of user, such as, but not limited to, human and non-human users, such as animals, and visual robots that can visually follow the trajectory of a navigation vehicle to reach their destination. The term "navigation vehicle" also includes any vehicle, such as robots, land vehicles such as cars, bicycles, scooters, water vehicles, flying vehicles, or hybrid vehicles such as water cars, flying cars, etc.

[0036] like Figure 1 The diagram illustrates a navigation system 100 and a target user 10 interacting with it. The navigation system 100 can be represented as a navigation vehicle 120, on which a user tracking module 110 is installed to track the position of the target user 10. The navigation vehicle 120 navigates along a forward direction 30 to a predetermined destination 20, and its trajectory guides the target user 10 toward the destination 20. As the navigation vehicle 120 navigates along the forward direction 30 to the destination 20, the target user 10 can also follow its trajectory along a forward direction 40. Therefore, the navigation vehicle 120 maintains a leading position relative to the target user 10, ensuring that the navigation vehicle 120 remains within the target user 10's line of sight.

[0037] To realize the "Front-Follow Me" concept, the user tracking module 110 can be positioned at the rear of the navigation vehicle 120 and track the movement of the target user 10 from a rearward direction 50 opposite to the forward movement of the navigation vehicle 120 along the forward direction 30. The navigation vehicle 120 maintains a minimum distance D from the target user 10, allowing the target user 10 to be tracked by the user tracking module 110 within the region of interest 60. For example, the target user 10 can be tracked within the field of view by an image capture module.

[0038] See Figure 2 To further understand the overall architecture of a navigation system 100 according to an exemplary embodiment of the present invention, the navigation system 100 is implemented by a computer device 200 equipped with multiple hardware 210 and software 230. The navigation system 100 may be embodied as a navigation vehicle 220, such as a robot with embedded computing device 200.

[0039] Essentially, computing device 200 includes appropriate components necessary to receive, store, and execute appropriate computer instructions to achieve the key functions of a "me-follow" robot. These components may include processing units, including a central processing unit (CPU), a math coprocessor (mathematical processor), a graphics processing unit (GPU) or a tensor processing unit (TPU) for tensor or multidimensional array computations or manipulations, read-only memory (ROM), random access memory (RAM), and input / output devices (e.g., disk drives), and a user interface (e.g., a keyboard, touchscreen). The processing unit may be a single processor to provide the combined functionality of multiple processors. In this example embodiment, computing device 200 is configured to receive data related to device 10 and the environment, measured by external sensing units.

[0040] The hardware 210 of the computing device 200 may further include one or more sensing units to capture multiple images or capture video streams within a predetermined time period. These sensing units may be image capture modules 222. The sensing units are arranged to communicate signalically with the processing unit of the computing device 200, such that the computing device 200 is configured to receive recorded images or videos from the sensing units and process the images or videos in real time.

[0041] In one example embodiment, the image capture module 222 may be a depth camera. For example, the depth camera 222 is arranged to capture a rear 3D view of the robot 220 to detect the presence of the target user 10 within its field of view. For example, the target user 10 may be identified based on certain matches of its facial features. The depth camera 222 may be... RealSense TMThe D435i depth camera combines powerful depth sensing capabilities with an inertial measurement unit (IMU).

[0042] Advantageously, a pivotable aiming base 224 is also provided, allowing the image capture module 222 to be movably and rotatably mounted on the robot 220. For example, movement of the aiming base 224 can be actuated by a motor communicating with the processing unit or microcontroller of the computing device 200, allowing adjustment of the speed and orientation of the aiming base 224 and the orientation of the image capture module 222 to keep the target user 10 within the field of view of the image capture module 222. For example, the microcontroller could be an Espressif ESP32-WROOM-32D.

[0043] The hardware 210 of the computing device 200 may further include one or more sensing units for capturing audio output related to the target user 10. These sensing units may be audio capture modules, i.e., audio receivers 226, such as microphones, for capturing sound data related to the interaction between the target user 10 and the computing device 200. The computing device 200 may also include a speaker unit for providing audible information to the target user 10.

[0044] The computing device 200 may also include other input devices, such as Ethernet ports, USB ports, etc., displays, such as liquid crystal displays, light-emitting displays, or any other suitable displays, and communication links (i.e., communication interfaces). For example, the computing device 200 may also include a main controller 240 for signal communication with the various hardware 210 described above.

[0045] Preferably, the computing device 200 can execute application programs (apps) to implement various functions defined by those applications. Specifically, the computing device 200 includes a plurality of software application programs 230 (i.e., applications) stored in a memory unit (e.g., ROM or RAM) or another memory unit. The software application programs 230 include computer-readable and executable instructions. The computing device 200 is configured to execute these instructions to cause a processor to perform one or more functions defined in the instructions.

[0046] For example, the navigation system can also be combined with integrated AI personnel tracking and voice recognition capabilities, such as voice-activated assistants. Therefore, the system can be used for deep vision automation in transitional care management (TCM) hospital logistics and for Deliverbot-assisted medication dispensing and delivery systems.

[0047] In one example embodiment, the main controller 240 may be a "me-follow" main controller 240, which can serve as a key integration point to seamlessly combine these disparate elements into a unified system. This controller 240 acts as a central hub for processing and decision-making, coordinating inputs from the YOLO personnel tracking system 242, the speech recognition engine 244, and the re-recognition module 246, and then translating these inputs into operable commands for the robot.

[0048] In one example embodiment, a YOLO AI people tracking function 242 is provided, which utilizes advanced computer vision and machine learning algorithms (such as pre-trained object detection algorithms, like the YOLO (You Only Look Once) algorithm) to process images captured by depth camera 222. For example, software application 230 is configured to execute the YOLO algorithm for real-time object detection. By utilizing the YOLO algorithm, the robot's camera 222 feed is analyzed in real time to detect and track human figures associated with target user 10. This enables robot 220 to identify and track specific individuals within its field of view. Therefore, robot 220 can accurately identify and track individuals in its environment.

[0049] More preferably, the YOLO AI people tracking function 242 can be assisted by a target-based people tracking function. The controller 240 can adjust the orientation and movement of the depth camera 222 to keep the tracked person centered in their field of view. The controller 240 continuously calculates the position of the person 10 relative to the robot 220 and adjusts their trajectory accordingly.

[0050] Advantageously, the combination of AI-based people tracking and target-based people tracking enables system 100 to accurately track target individuals 10, even in the presence of occlusion or interference.

[0051] In one example embodiment, a speech-to-text recognition function 244 is also provided, which utilizes state-of-the-art speech recognition technology to enable the robot 220 to understand and process verbal commands, thereby enhancing its interactive capabilities. For example, the software application 230 is configured to activate the speech-to-text recognition function 244 upon receiving a voice command containing keywords. The audio receiver 226 can listen for specific keywords to activate its voice command mode. Once activated, it can understand and process verbal instructions, thereby allowing the user 10 to control the robot 220 or verbally command it.

[0052] In one example embodiment, a lost-person re-identification function 246 is also provided, which implements advanced re-identification technology to ensure that the robot 220 can identify and re-identify the person 10 even after temporary occlusion or loss of visual contact. If the robot 220 cannot see the person 10 it is following temporarily, this function helps it re-identify the correct person when the person 10 re-enters its field of vision. It uses visual cues and patterns to ensure that it continues to follow the correct person 10.

[0053] Advantageously, if the target user 10 disappears from the field of view of the depth camera 222 within a predetermined time period, the controller 240 can command the robot 220 to autonomously navigate to a predetermined temporary destination in response to the target user 10 disappearing from the field of view of the depth camera 222. For example, the controller 240 can command the robot 220 to return to the previous position where the target user 10 was present in the image last captured by the depth camera 222 at a specific timestamp.

[0054] In one example embodiment, a robot publisher function 248 is also provided, which acts as a communication hub for publishing the robot's status, sensor data, and other relevant information to other parts of system 200. This component facilitates seamless communication between the various modules of the robot to ensure synchronized operation and efficient data exchange. It ensures that all modules of robot 220 (tracking, movement, speech recognition, etc.) are synchronized and can respond appropriately to environmental changes or user commands.

[0055] Advantageously, these features can work together to create a seamless following experience, allowing the robot 220 to track and follow the person 10 while responding to voice commands and safely navigating its environment.

[0056] Furthermore, the invention can be designed to be modular, allowing it to connect with any robot using the same communication protocol with subtle pre-defined settings. This modularity ensures that the navigation system 100 can be easily adapted and integrated into various robot platforms, providing flexibility and scalability for different applications.

[0057] Preferably, the navigation system 100 can guide the user 10 to the destination 20 while performing other tasks. By incorporating advanced AI capabilities, the robot of the present invention is not only able to navigate and transport more efficiently, but also adapts to various scenarios, making it a more versatile and reliable solution for a wide range of applications beyond simple tracking or transport tasks.

[0058] For example, computing device 200 may also include an advanced multimodal large language model (LLM) to further enhance the functionality and adaptability of the "me-follow" robot. These additional features can significantly improve the robot's performance, especially in complex environments or specialized applications such as delivery services.

[0059] 1) Visual confirmation of the destination:

[0060] In one example embodiment, the integration of the multimodal LLM enables the robot to analyze images from its camera 222 in real time. This capability allows the robot to verify that it has reached the correct destination by comparing visual cues with provided address information. It also allows the robot to identify specific landmarks or architectural features to ensure accurate navigation. The robot can also identify potential obstacles or challenges in the delivery path.

[0061] 2) Environmental awareness and level of caution:

[0062] In one example implementation, multimodal LLM can help a robot assess its surroundings and determine when extra caution is needed. For instance, the robot can identify high-traffic areas or construction zones where slower movement is required. It can also identify weather conditions (e.g., rain, snow) that may affect navigation or package handling. Furthermore, the robot can detect the presence of children or pets, prompting increased alertness.

[0063] 3) Enhance human-computer interaction:

[0064] In one example embodiment, the integration of a multimodal LLM can also enhance the robot's ability to interact with humans. For instance, the robot can understand and respond to gestures or facial expressions. The robot can provide more natural and context-aware verbal responses. The robot can also provide visual feedback via an integrated display, indicating that it understands complex instructions or situations.

[0065] 4) Adaptive decision-making:

[0066] In one example embodiment, multimodal LLM capabilities allow robots to make more informed decisions based on a combination of visual, textual, and contextual information. For instance, a robot can select the optimal route based on real-time visual assessments of traffic and pedestrian patterns. The robot can adapt its approach to different types of buildings or entrances. It can also make on-site decisions regarding package placement or handover methods.

[0067] 5) Improve security and protection:

[0068] In one example implementation, multimodal LLM can enhance a robot's ability to ensure safe and reliable delivery. For instance, the robot can verify the recipient's identity using facial recognition combined with other authentication methods. The robot can detect suspicious activity or unauthorized attempts to retrieve the package. The robot can also assess the safety of leaving the package in certain locations based on visual cues.

[0069] refer to Figure 3This shows the speed of robot 220 and... Figure 1 The relationship between the minimum distance D between the navigation vehicle 120 and the target user 10 is shown.

[0070] In this example embodiment, the "Me-Follow" master controller 240 can be configured to perform some distance calculations and adjust its trajectory and relative position with respect to the robot 220, such that the robot 220 remains within a minimum distance D from the target user 10, and the target user 10 can be tracked by the depth camera 222 within the field of view.

[0071] For example, robot 220 can use depth camera 222 to measure the distance between itself and the person 10 it is following. This information is crucial for maintaining a safe and consistent following distance. It can use facial features to determine the person's true distance. The main controller 240 will adjust its speed based on the calculated distance to keep the minimum distance D between robot 220 and target user 10 greater than or equal to a predetermined threshold.

[0072] Typically, the speed of robot 220 is inversely proportional to the distance D between robot 220 and target user 10. The closer robot 220 is to target user 10, the higher the speed required for robot 220 to navigate away from target user 10. As an example, when the distance D between robot 220 and target user 10 is less than 1m, robot 220 can navigate at a constant speed of 0.5m / s. As robot 220 moves away from target user 10, it can navigate at a constantly decelerating speed. When robot 220 reaches a specified distance (e.g., 2.5 meters from target user 10), its speed will drop to zero. As target user 10 moves in the forward direction 40 toward robot 220, robot 220 can again navigate away from target user 10.

[0073] Now for reference Figure 4 The operation mode of a navigation system 100 according to an example embodiment of the present invention is further described.

[0074] refer to Figure 4 The navigation method 400 for guiding target user 10 begins at step 410. Step 410 includes receiving a voice command with keywords to trigger the navigation system 100. If the audio receiver 226 receives a voice command with the correct keywords, the speech-to-text recognition function 244 is triggered, and user 10 can verbally command robot 220, and navigation method 400 then proceeds to step 412. If the audio receiver 226 cannot determine the keywords, robot 220 remains in standby mode and repeats step 410.

[0075] Step 412 includes activating the YOLO AI people tracking function 242 and the target-based people tracking function. The depth camera 222 will capture multiple images, which the YOLO (You Only Look Once) algorithm will process to identify the target person 10 based on certain matches of facial features. The orientation and movement of the depth camera 222 can also be adjusted by the aiming base 224 to increase the depth of field during tracking. Step 414 includes locking the target person 10 within the region of interest 60. Once the target person 10 is identified, it will be locked, and navigation will be provided to the same target person 10 until navigation terminates in step 426 or 434.

[0076] Step 416 includes determining whether the target person 10 has disappeared from the region of interest 60. If the target person 10 has disappeared from the region of interest 60, navigation method 400 will proceed to steps 420 and 422. Step 420 includes implementing the person loss re-identification function 246 to re-identify the target person 10. Step 422 includes determining whether the target person 10 can be re-identified. If the target person 10 can be re-identified immediately, navigation method 400 will continue to steps 430 and 432.

[0077] If the target person 10 cannot be re-identified within a predetermined threshold (e.g., 2 minutes), step 420 should be repeated. If the target person 10 cannot be re-identified within the predetermined threshold (e.g., 2 minutes), proceed to steps 424 and 426. Step 424 includes warning the target person 10, and step 426 includes returning the target base 224 to its original position. Navigation method 400 is interrupted.

[0078] If the target person 10 is within the region of interest 60, navigation method 400 skips step 420 and proceeds directly to step 430. Step 430 includes calculating the distance D between robot 220 and target person 10. Step 432 includes issuing instructions to robot 220. Step 434 includes determining whether robot 220 has reached destination 20. If robot 220 has reached destination 20, navigation method 400 completes. If robot 220 has not yet reached destination 20, navigation method 400 repeats step 416.

[0079] According to an exemplary embodiment of the present invention, the "Me-Follow" method has the following advantages:

[0080] 1. Continuous monitoring:

[0081] By placing the robot in front of you, people can easily monitor its movement and location without constantly turning their heads back. This ensures a seamless and uninterrupted following experience and reduces the risk of not seeing the robot or getting lost.

[0082] 2. Hands-free navigation:

[0083] Guided by a robot, people can focus on their surroundings and navigate crowded or complex environments without adding to the burden of guiding the robot. This is especially beneficial when carrying large or heavy items, as people's hands remain free.

[0084] 3. Enhanced security:

[0085] By keeping the robot within sight, people can react quickly to any potential obstacles or hazards, preventing collisions or accidents that may occur if the robot falls behind.

[0086] 4. Improved tracking and recovery:

[0087] The integration of advanced AI-powered human tracking and voice recognition technologies ensures the robot can accurately track target individuals, even in the presence of obstructions or interference. If a person becomes separated from the robot, it can autonomously navigate to a predetermined destination and wait for their return, thus reducing the risk of losing valuables or getting into trouble.

[0088] Advantageously, according to one example of the invention, the market opportunity for innovative "me-follow" robots is vast, covering industries requiring efficient transportation, navigation assistance, or hands-free operation when carrying valuable or large items. For example, "me-follow" robots integrating artificial intelligence for personnel tracking and voice recognition have numerous potential applications across various industries and scenarios:

[0089] 1. Hotel and tourism industry:

[0090] Hotels, resorts, and tourist attractions can use these robots to help guests carry luggage, navigate, and deliver information. Guests can simply instruct the robot to take them to their destination, freeing their hands to enjoy their surroundings without worrying about carrying heavy luggage or getting lost.

[0091] 2. Healthcare and assisted living:

[0092] In healthcare facilities and assisted living communities, these robots can help deliver medical supplies, equipment, or personal belongings of patients or residents. Hands-free navigation and voice control are particularly beneficial for individuals with limited mobility or those carrying medical equipment.

[0093] 3. Retail and warehousing:

[0094] Large retail stores, shopping malls, and warehouses can use these robots to help customers or employees locate and transport goods or inventory. AI-powered people tracking capabilities ensure that the robots stay with designated personnel even in crowded or complex environments.

[0095] 4. Airports and transportation hubs:

[0096] Airports, train stations, and other transportation hubs can use these robots to guide passengers with luggage to boarding gates, baggage claim areas, or other destinations. Voice recognition enables easy navigation and reduces the need for continuous visual monitoring.

[0097] While not strictly necessary, the embodiments described with reference to the accompanying drawings can be implemented as an application programming interface (API) or a set of libraries for use by developers, or can be included in another software application, such as a terminal or personal computer operating system or a portable computing device operating system. Typically, because program modules include routines, programs, objects, components, and data files that assist in performing specific functions, those skilled in the art will understand that the functionality of a software application can be distributed across multiple routines, objects, or components to achieve the same functionality required herein.

[0098] It should also be understood that any suitable computing system architecture can be used when the methods and systems of the present invention are implemented entirely or partially by a computing system. This will include tablets, wearable devices, smartphones, Internet of Things (IoT) devices, edge computing devices, standalone computers, network computers, cloud-based computing devices, and dedicated hardware devices. When the terms "computing system" and "computing device" are used, these terms are intended to cover any suitable computer hardware arrangement capable of implementing the described functions.

[0099] Those skilled in the art will recognize that various changes and / or modifications can be made to the invention illustrated in the specific embodiments without departing from the spirit or scope of the invention as broadly described. Therefore, these embodiments should be considered exemplary rather than restrictive in all respects.

[0100] Unless otherwise stated, any references to prior art included herein should not be construed as an admission that the information is common knowledge.

Claims

1. A navigation system for guiding a target user, characterized by, Comprising: a user tracking module arranged to track movement of a target user; a navigation vehicle arranged to guide the target user towards a predetermined destination by steering in relation to a trajectory of the tracked movement of the target user; wherein the user tracking module is arranged to track movement of the target user from a leading position in relation to the tracked movement of the target user in a forward direction of movement.

2. The navigation system of claim 1, wherein, wherein, the user tracking module is arranged to track movement of the target user from a trailing position in relation to the navigation vehicle in a backward direction of movement opposite to the forward direction of movement.

3. The navigation system of claim 1, wherein, wherein, the user tracking module is arranged to determine a distance between the navigation vehicle and the target user and maintain a minimum distance therebetween, the minimum distance being greater than or equal to at least a predetermined threshold.

4. The navigation system of claim 3, wherein, further comprising a controller configured to adjust a trajectory of the navigation vehicle based on a position of the target user.

5. The navigation system of claim 3, wherein, wherein, the controller is configured to adjust a speed of the navigation vehicle based on the distance between the navigation vehicle and the target user, the speed of the navigation vehicle being inversely proportional to the distance between the navigation vehicle and the target user.

6. The navigation system of claim 1, wherein, wherein, the user tracking module is arranged to track presence of the target user within a region of interest and autonomously navigate to a predetermined interim destination in response to the target user not being within the region of interest for a first predetermined period of time.

7. The navigation system of claim 1, wherein, further comprising an image capture module arranged to capture a plurality of images relating to movement of the target user.

8. The navigation system of claim 7, wherein, wherein, the image capture module further comprises a depth camera arranged to measure a distance between the navigation vehicle and the target user.

9. The navigation system of claim 7, wherein, wherein, the image capture module is arranged to identify facial features associated with the target user from the captured images.

10. The navigation system of claim 9, wherein, wherein, the image capture module is arranged to re-identify facial features associated with the target user from the captured images when the target user is not within a field of view of the image capture module for a predetermined period of time.

11. The navigation system of claim 1, wherein, wherein, the image capture module is movably positioned on the navigation vehicle.

12. The navigation system of claim 11, wherein, wherein, the navigation vehicle further comprises a base on which the image capture module is movably mounted such that a direction of the image capture module is adjustable so as to maintain the target user within a field of view of the image capture module.

13. The navigation system of claim 7, wherein, wherein, the user tracking module further comprises a pre-trained object detection algorithm configured to process images associated with movement of the target user.

14. The navigation system of claim 13, wherein, wherein, the pre-trained object detection algorithm comprises a YOLO algorithm.

15. The navigation system of claim 1, wherein, further comprising an audio capture module arranged to capture a plurality of audio outputs associated with the target user.

16. The navigation system of claim 15, wherein, further comprising a voice recognition module arranged to identify verbal commands of the target user from the captured audio outputs.

17. The navigation system of claim 16, wherein, wherein, the voice recognition module is arranged to trigger a voice command mode by comparing the captured audio outputs to a predetermined reference signal.

18. The navigation system of claim 1, wherein, Also included is a communication module configured to publish one or more statuses associated with the navigation vehicle.

19. The navigation system of claim 6, wherein, wherein, the object tracking module is arranged to track the presence of the target user within a region of interest, and to alert the target user in response to the target user not being within the region of interest for a second predetermined time period that is greater than a first predetermined time period.

20. The navigation system of claim 1, wherein, wherein, the navigation vehicle is within the line of sight of the target user throughout the entire process of guiding the target user to the predetermined destination.

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