Deep Reinforcement Learning Approach for Single Vehicle Persistent Surveillance with Fuel Constraints

Alapfogalmak

A deep reinforcement learning-based approach is presented to solve the single vehicle persistent surveillance problem with fuel constraints, where the objective is to determine an optimal sequence of visits to targets that minimizes the maximum time elapsed between successive visits while ensuring the vehicle never runs out of fuel.

Kivonat

The article presents a deep reinforcement learning (D-RL) based approach to solve the Single Vehicle Persistent Surveillance with Fuel Constraints (SVPSFC) problem. The SVPSFC problem involves a single unmanned aerial vehicle (UAV) initially stationed at a depot with fuel or time-of-flight constraints, tasked with repeatedly visiting a set of targets with equal priority.
Key highlights:

The SVPSFC problem is formulated as a Markov Decision Process (MDP) that can be solved using D-RL.
To improve transferability, the authors introduce the concept of "dummy targets" to enable the trained D-RL agent to handle varying number of targets.
Action masking is used to enforce fuel restrictions and prevent infeasible actions.
Extensive experiments are conducted to compare the D-RL approach with a greedy baseline heuristic. The results show that the D-RL approach consistently outperforms the greedy baseline in terms of minimizing the maximum revisit time, and is also robust to variations in the UAV's fuel capacity.
Qualitative analysis of the trajectories generated by the D-RL and greedy approaches reveals that the D-RL approach exhibits more efficient and reasonable behavior in preserving fuel reserves and reducing the need for frequent depot visits.

Statisztikák

The UAV travels at a constant speed of 1 unit distance per unit time.
The fuel consumed by the UAV to traverse an edge (i, j) is exactly equal to the Euclidean distance between the vertices i and j.
The UAV's fuel capacity is given by F.

Idézetek

"The objective of the problem is to determine an optimal sequence of visits to the targets that minimizes the maximum time elapsed between successive visits to any target while ensuring that the vehicle never runs out of fuel or charge."
"The choice of Reinforcement Learning (RL) as an algorithmic approach for the SVPSFC is motivated by two key factors. Firstly, though, one can formulate the SVPSFC as a mixed-integer linear program by leveraging existing work for fuel-constrained vehicle routing problems and the PSMs in [2]–[5], these formulations are notoriously hard to solve to optimality for state-of-the-art branch-and-cut approaches."

Főbb Kivonatok

Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints

by Hritik Bana,... : arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06423.pdf

Mélyebb kérdések

How can the proposed D-RL approach be extended to handle heterogeneous target priorities, where certain targets need to be visited more frequently than others

To extend the proposed D-RL approach to handle heterogeneous target priorities, where certain targets require more frequent visits, we can introduce a weighting mechanism into the reward function. By assigning different weights to targets based on their priority levels, the D-RL algorithm can learn to prioritize visits to high-priority targets over others. This can be achieved by modifying the reward function to incorporate these weights, influencing the agent's decision-making process. Additionally, the clock update equations can be adjusted to reflect the varying priorities, allowing the D-RL model to learn optimal strategies that align with the priority levels of the targets. By training the model on a dataset that includes target priorities, the D-RL algorithm can adapt its policy to address the specific needs of the surveillance mission with heterogeneous target priorities.

How can the single-vehicle persistent surveillance problem be formulated and solved in a multi-vehicle setting, where multiple agents need to coordinate their actions to efficiently monitor the targets

In a multi-vehicle setting, where multiple agents need to coordinate their actions for efficient target monitoring, the single-vehicle persistent surveillance problem can be reformulated as a multi-agent coordination problem. Each agent represents a UAV tasked with surveilling a subset of targets, and the objective is to optimize the collective surveillance coverage while considering fuel constraints and target revisit times. To solve this multi-vehicle persistent surveillance problem, a decentralized coordination approach can be implemented, where each agent communicates and collaborates with neighboring agents to avoid redundant coverage and ensure comprehensive monitoring of all targets. By formulating the problem as a multi-agent reinforcement learning task, agents can learn to coordinate their actions, share information, and collectively optimize the surveillance mission's efficiency. Techniques such as centralized training with decentralized execution can be employed to facilitate coordination among multiple UAVs while ensuring scalability and robustness in real-world applications.

What other real-world applications beyond persistent surveillance can benefit from the action masking technique used in this work to enforce constraints during reinforcement learning

The action masking technique used in the single-vehicle persistent surveillance problem to enforce constraints during reinforcement learning can benefit various real-world applications beyond persistent surveillance. One such application is in autonomous driving, where action masking can be utilized to enforce safety constraints and traffic rules during the training of autonomous vehicles. By restricting the agent's actions to comply with traffic regulations, speed limits, and lane-keeping requirements, the autonomous vehicle can learn safe and responsible driving behaviors. Additionally, action masking can be applied in robotics for tasks such as robotic manipulation, where constraints on joint movements or object interactions need to be enforced to prevent collisions or ensure task completion. By incorporating action masking into reinforcement learning algorithms, robots can learn to perform complex manipulation tasks while adhering to specified constraints, enhancing their efficiency and safety in real-world scenarios.