Persistent surveillance for heterogeneous robots considering movement randomness and energy allocation.

Area of Science:

• Robotics and Autonomous Systems
• Environmental Monitoring
• Optimization Theory

Background:

• Persistent surveillance requires robots to continuously monitor areas while managing movement and energy.
• Heterogeneous robot teams (UGVs and UAVs) offer unique advantages for environmental inspections.
• Existing methods lack robust solutions for privacy-preserving, energy-efficient persistent surveillance with random movement.

Purpose of the Study:

• To develop a privacy-preserving persistent surveillance framework for heterogeneous robots.
• To address energy allocation challenges in dynamic, unpredictable robotic surveillance.
• To improve measurement efficiency in real-time environmental inspection applications.

Main Methods:

• A framework utilizing unmanned aerial vehicles (UAVs) with Markov Chain-based stochastic movement for probabilistic measurements.
• Formulation of persistent surveillance with movement randomness and energy allocation (PSREA) as a non-convex optimization problem.
• Development of a clustering-based algorithm for task network simplification and an iterative two-stage algorithm to solve the optimization problem.

Main Results:

• The proposed algorithms effectively handle a large number of task nodes in complex surveillance scenarios.
• The methods successfully address the non-convex nature of the PSREA optimization problem.
• Numerical results show significant improvements in inspection performance compared to benchmark approaches.

Conclusions:

• The developed framework and algorithms provide an effective solution for privacy-preserving, energy-efficient persistent surveillance.
• The approach enhances measurement efficiency and robustness in environmental inspection tasks using heterogeneous robots.
• This work contributes a novel method for optimizing complex robotic surveillance operations.