Robot Swarm Object Tracking Through Shared Timestamped Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mobile object tracking methods using fixed cameras are prone to occlusions, lighting conditions, and changes in object appearance, and swarm-based methods like Swarm-SLAM require structured movements and cannot generate comprehensive maps.
Innovation Solution
A method utilizing a robot swarm of mobile monitoring devices that acquire and share monitoring data to generate dynamic object descriptors, update descriptions based on timestamp comparisons, and create topological maps of environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed cameras are used for mobile object tracking, then the system structure is simple, but the tracking reliability deteriorates due to occlusions, lighting conditions, and object appearance changes
Solution Approach 1:
The system divides the tracking task among multiple monitoring devices (robots) in a swarm, where each device independently tracks objects from its local perspective. This segmentation allows the system to maintain simple individual device structures while achieving high overall tracking reliability through collective observation, overcoming occlusions and appearance changes that single fixed cameras cannot handle.
Solution Approach 2:
The system changes the parameter of device mobility from static (fixed cameras) to dynamic (mobile robots), enabling monitoring devices to actively reposition themselves to maintain optimal viewing angles and avoid occlusions, thereby significantly improving tracking reliability while keeping individual device complexity low.
2Loss of information
If Swarm-SLAM method is used for collective perception, then the mapping capability is improved, but the method requires structured movements with high fidelity odometry data which is not easily compatible with robot swarms
Solution Approach 1:
The system replaces the static, pre-planned movement structures required by Swarm-SLAM with dynamic, adaptive movement patterns. Robots in the swarm autonomously navigate and reposition themselves based on real-time tracking needs and environmental conditions, eliminating the requirement for high-fidelity odometry data and structured movements while maintaining excellent compatibility with diverse robot swarm configurations.
Solution Approach 2:
Instead of requiring robots to follow pre-defined structured paths to achieve mapping (as in Swarm-SLAM), the system inverts the approach: robots perform natural, unstructured exploration and movement, and the system recovers mapping and positioning information from these spontaneous trajectories through sensor data fusion and collaborative perception algorithms.
3Measurement precision
If extensive feature extraction is performed from video feeds, then object identification capability is improved, but the method becomes particularly subject to occlusions, lighting conditions, and object appearance changes
Solution Approach 1:
The system merges the observations and feature extractions from multiple mobile monitoring devices to create a comprehensive view of each tracked object. By combining data from multiple perspectives, the system maintains high object identification capability while becoming robust to occlusions, lighting changes, and appearance variations that would affect any single device's features extraction.
Solution Approach 2:
The system develops a multi-functional tracking framework that can identify and track objects under diverse conditions (different lighting, occlusions, appearance changes) using the same collaborative perception approach. This universal method works across various environmental conditions and object types, maintaining both identification precision and reliability simultaneously.
Data Source
Figure 1
Figure 2
Figure 3~4A
AI summary
A computer-implemented method for tracking mobile objects is comprising: (S10) moving a plurality of monitoring devices in an environment comprising mobile objects; (S20) with each monitoring device, acquiring monitoring data of the environment to identify mobile objects in the environment at one or more times characterized by timestamps; (S30) generating a description of the environment based on the monitoring data, wherein the description comprises a list of object descriptors of the identified mobile objects, each object descriptor including an identification data, a location data; and a timestamp; and (S40) updating the description of a first monitoring device when the first monitoring device is within communication range of at least a second monitoring device in the environment, the updating being made by comparing the description of the first monitoring device to the description of the at least second monitoring device.