Loading...
ALGORITHM DESIGN AND ANALYSIS IN DISTRIBUTED MULTI-ROBOT MULTI-TARGET TRACKING
Xin, Pujie
Xin, Pujie
Citations
Altmetric:
Genre
Thesis/Dissertation
Date
2024-05
Advisor
Committee member
Group
Department
Mechanical Engineering
Subject
Permanent link to this record
Collections
Research Projects
Organizational Units
Journal Issue
DOI
http://dx.doi.org/10.34944/dspace/10222
Abstract
This research dissertation addresses the significant challenge of Multi-Robot Multi-Target Tracking (MR-MTT), a critical system in various scenarios, including search-and-rescue missions, surveillance, and environmental monitoring. MR-MTT involves coordinating a team of robots to track multiple dynamic targets in diverse environments. This challenge requires efficient coordination among the robots to ensure effective tracking of all targets. The core of this challenge lies in developing efficient strategies for estimation, communication, and control within these robotic systems. Our goal is to create and test different solutions to this general problem.
A significant focus of this research is on the estimation aspect of MR-MTT. The system employs a novel distributed Multiple Hypothesis Tracker (MHT) for accurate estimation of both the number and states of multiple targets. A standout feature of our methodology is the introduction of an innovative data association method, designed to reallocate target tracks among the robots, thereby enhancing the collective tracking accuracy and efficiency of the team. This approach is particularly beneficial in scenarios with numerous targets and highly dynamic movements, as it allows for more flexible and responsive tracking.
In addition to estimation, a substantial portion of this research focuses on the development of advanced control strategies to enhance the team's efficiency in locating all targets and achieving this goal more swiftly. We have integrated Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Immune System (AIS)into the MR-MTT systems with an estimation method using the PHD filter. This integration aims to optimize the robots' trajectories and search patterns, leveraging the strengths of these metaheuristic-based algorithms to strike a balance between exploration and exploitation. Such optimization is crucial for enhancing the overall efficiency and effectiveness of MR-MTT systems.
Furthermore, this dissertation includes a comprehensive system-level analysis and prediction component through dimensionless variable analysis. We have developed an analytical framework to evaluate and predict the performance of MR-MTT systems under various operational scenarios and environmental conditions. This framework is intended to provide deep insights into the critical performance determinants and their interrelations, guiding the design and optimization of MR-MTT systems. The anticipated outcomes of this research include improved accuracy in target tracking, enhanced performance metrics for MR-MTT systems, and valuable insights for the future design and management of sophisticated multi-robot systems.
The expected outcomes of this research are multifold: enhanced accuracy in target tracking, improved performance metrics for MR-MTT systems, and valuable insights for the future development and management of complex multi-robot systems. Through this proposal, we aim to contribute significantly to the field of robotic coordination and tracking, addressing critical needs in various high-impact applications.
Description
Citation
Citation to related work
Has part
ADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu