Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Drone Swarm Energy Management

Michael Z. Zgurovsky, Pavlo O. Kasyanov, Liliia S. Paliichuk

TL;DR

This work addresses energy management for a drone swarm operating under partial observability by formulating the problem as a Gaussian POMDP and solving it with a POMDP-DDPG framework. It leverages Gaussian belief reduction to convert the infinite-dimensional belief space into a 1D MDP over the belief mean $\hat{x}_t^{(i)}$, with a deterministic variance sequence $\\sigma_t^2$, enabling an efficient Kalman-filter-based decision process. The authors establish that optimal policies admit a time-varying $(s_t,S_t)$ structure, where drones recharge to $S_t$ whenever $\\hat{x}_t^{(i)}$ falls below $s_t$, and demonstrate four DDPG variants to exploit this structure. Empirical results show that DDPG on belief means achieves near-optimal performance with substantial training-time reductions (roughly 33%–64% faster than baselines) and scales nearly linearly with swarm size, enabling real-time, decentralized energy-aware control for large fleets with robust handling of sensor noise. These findings have practical implications for secure, efficient, and scalable autonomous operations in security, environmental monitoring, and infrastructure inspection scenarios, underpinned by strong theoretical guarantees about complexity and policy structure.

Abstract

This note presents an analytical framework for decision-making in drone swarm systems operating under uncertainty, based on the integration of Partially Observable Markov Decision Processes (POMDP) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The proposed approach enables adaptive control and cooperative behavior of unmanned aerial vehicles (UAVs) within a cognitive AI platform, where each agent learns optimal energy management and navigation policies from dynamic environmental states. We extend the standard DDPG architecture with a belief-state representation derived from Bayesian filtering, allowing for robust decision-making in partially observable environments. In this paper, for the Gaussian case, we numerically compare the performance of policies derived from DDPG to optimal policies for discretized versions of the original continuous problem. Simulation results demonstrate that the POMDP-DDPG-based swarm control model significantly improves mission success rates and energy efficiency compared to baseline methods. The developed framework supports distributed learning and decision coordination across multiple agents, providing a foundation for scalable cognitive swarm autonomy. The outcomes of this research contribute to the advancement of energy-aware control algorithms for intelligent multi-agent systems and can be applied in security, environmental monitoring, and infrastructure inspection scenarios.

Drone Swarm Energy Management

TL;DR

This work addresses energy management for a drone swarm operating under partial observability by formulating the problem as a Gaussian POMDP and solving it with a POMDP-DDPG framework. It leverages Gaussian belief reduction to convert the infinite-dimensional belief space into a 1D MDP over the belief mean , with a deterministic variance sequence , enabling an efficient Kalman-filter-based decision process. The authors establish that optimal policies admit a time-varying structure, where drones recharge to whenever falls below , and demonstrate four DDPG variants to exploit this structure. Empirical results show that DDPG on belief means achieves near-optimal performance with substantial training-time reductions (roughly 33%–64% faster than baselines) and scales nearly linearly with swarm size, enabling real-time, decentralized energy-aware control for large fleets with robust handling of sensor noise. These findings have practical implications for secure, efficient, and scalable autonomous operations in security, environmental monitoring, and infrastructure inspection scenarios, underpinned by strong theoretical guarantees about complexity and policy structure.

Abstract

This note presents an analytical framework for decision-making in drone swarm systems operating under uncertainty, based on the integration of Partially Observable Markov Decision Processes (POMDP) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The proposed approach enables adaptive control and cooperative behavior of unmanned aerial vehicles (UAVs) within a cognitive AI platform, where each agent learns optimal energy management and navigation policies from dynamic environmental states. We extend the standard DDPG architecture with a belief-state representation derived from Bayesian filtering, allowing for robust decision-making in partially observable environments. In this paper, for the Gaussian case, we numerically compare the performance of policies derived from DDPG to optimal policies for discretized versions of the original continuous problem. Simulation results demonstrate that the POMDP-DDPG-based swarm control model significantly improves mission success rates and energy efficiency compared to baseline methods. The developed framework supports distributed learning and decision coordination across multiple agents, providing a foundation for scalable cognitive swarm autonomy. The outcomes of this research contribute to the advancement of energy-aware control algorithms for intelligent multi-agent systems and can be applied in security, environmental monitoring, and infrastructure inspection scenarios.

Paper Structure

This paper contains 41 sections, 2 theorems, 10 equations, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Noisy Observations
  4. Cost Structure
  5. Objective
  6. Decentralization assumption.
  7. Application of Gaussian Belief Reduction
  8. Belief State Representation
  9. Kalman Filter Update
  10. Kalman Filter Update
  11. Optimal Policy Structure
  12. DDPG Implementation for Drone Swarms
  13. Algorithm Variants
  14. Approach 1: DDPG with Observation Histories.
  15. Approach 2: DDPG with Belief States.
  16. ...and 26 more sections

Key Result

Theorem 3.1

For the Gaussian drone energy management problem, the belief-MDP state space reduces from the infinite-dimensional space $\mathcal{P}(\mathbb{R})$ of probability measures to the one-dimensional space $\mathbb{R}$ of belief means $\hat{x}_t^{(i)}$.

Figures (2)

  • Figure 1: Structure of learned policy at different time steps. Left: Time $t=0$ (high uncertainty), $s_0 = 28$, $S_0 = 85$. Center: Time $t=25$ (mid-mission), $s_{25} = 35$, $S_{25} = 78$. Right: Time $t=45$ (near terminal), $s_{45} = 42$, $S_{45} = 72$. Clear $(s_t, S_t)$ structure emerges at all time steps, with $s_t$ increasing and $S_t$ decreasing over time.
  • Figure 2: Sample trajectory showing true battery level $x_t$ (blue solid), belief mean $\hat{x}_t$ (green dashed), noisy observations $y_t$ (orange dots), 95% confidence region (gray shaded), and recharge threshold $s = 35$ (red dashed). Recharge events occur at $t = 12$ and $t = 31$ when $\hat{x}_t < s_t$.

Theorems & Definitions (7)

  • Remark 1
  • Theorem 3.1
  • proof : Proof sketch
  • Remark 2
  • Corollary 3.1: Existence of $(s_t, S_t)$ policies
  • proof
  • Remark 3