Reinforcement Learning for Agile Active Target Sensing with a UAV
Deep RL with motion primitives enables efficient, robust UAV path planning for active target sensing, outperforming heuristics.
Key Findings
Methodology
This paper introduces a deep reinforcement learning framework combined with a library of motion primitives to generate dynamically feasible paths for UAVs. Using A3C algorithm, the policy network is trained to maximize information gain by balancing exploration and exploitation, updating environmental beliefs via Bayesian methods during flight. The approach integrates environment modeling, sensor likelihoods, and motion constraints, enabling real-time adaptive path planning. Extensive simulations demonstrate superior target discovery and classification efficiency over baseline heuristics, with robustness to prior distribution deviations. The framework's core innovation lies in leveraging precomputed motion primitives within a deep RL context, ensuring both dynamical feasibility and adaptability in complex environments.
Key Results
- The proposed method achieved over 92% target discovery rate and 81% entropy reduction, outperforming greedy, DP, and CMAES algorithms by 15-20%. It reduced search time and energy consumption by approximately 20%, maintaining high performance even when prior target distributions deviated significantly, with a KL divergence up to 1.5. Ablation studies confirmed that motion primitives and RL training contributed significantly to robustness and efficiency. The approach scaled well across environments with varying target densities and complexities, demonstrating its practical viability in real-world scenarios.
- Compared to baseline heuristics, the RL-based strategy maintained stable target coverage above 80%, with a consistent entropy reduction rate. It adapted quickly to environment changes, re-planning paths within milliseconds, and effectively balanced exploration and exploitation. The results indicate that integrating motion primitives with deep RL offers a promising solution for autonomous target sensing in dynamic, uncertain environments.
- Ablation experiments revealed that the motion primitive library improved path smoothness and feasibility, while the RL policy enhanced environmental adaptability. The combined approach significantly outperformed static or purely heuristic methods, especially under prior distribution biases, validating the robustness and flexibility of the framework.
Significance
This work advances autonomous UAV capabilities in active sensing tasks, addressing key limitations of traditional heuristic and optimization-based methods. By integrating deep RL with motion primitives, it provides a scalable, robust solution capable of operating in complex, dynamic environments with uncertain prior knowledge. The approach enhances target discovery efficiency, reduces mission time and energy costs, and opens avenues for deploying large-scale UAV swarms in search-and-rescue, environmental monitoring, and military reconnaissance. Its robustness to prior bias and environment variability marks a significant step toward fully autonomous, intelligent aerial systems capable of real-time decision-making in unpredictable scenarios.
Technical Contribution
The primary technical contributions include: 1) a novel combination of motion primitive libraries with deep RL (A3C) for real-time, dynamically feasible path planning; 2) a multi-scale Bayesian environment model that updates target beliefs during flight, improving robustness; 3) an end-to-end learning framework that jointly optimizes exploration and classification objectives. This integration allows the UAV to generate smooth, feasible trajectories while adapting to environment changes, overcoming the limitations of prior heuristic or static methods. The framework also introduces a reward structure based on information entropy reduction and target coverage, guiding the RL policy toward efficient exploration and classification.
Novelty
This study is the first to embed a library of motion primitives within a deep RL framework for active target sensing. Unlike prior works relying solely on heuristic or optimization algorithms, this approach learns policies that inherently respect UAV dynamics, enabling agile, real-time path adjustments. Its robustness to prior distribution deviations and ability to operate in complex, uncertain environments represent a significant innovation, bridging the gap between dynamic feasibility and adaptive exploration in aerial robotics.
Limitations
- The model relies heavily on simulated training data; real-world deployment may face domain gap issues requiring transfer learning or online adaptation.
- The precomputed motion primitives, while ensuring feasibility, may limit flexibility in highly dynamic or cluttered environments, necessitating further extension.
- Current validation is primarily in 2D environments; extending to 3D space and multi-UAV coordination remains an open challenge.
- Computational costs during training and real-time inference could be high, especially in large-scale scenarios, calling for efficiency improvements.
Future Work
Future research will focus on transferring the trained models to real UAV platforms, incorporating 3D path planning, multi-agent coordination, and online learning mechanisms. Enhancing the motion primitive library to adapt dynamically and reducing computational overhead are also key directions. Additionally, integrating perception uncertainties and real-time environment updates will be explored to improve robustness and operational reliability in real-world applications.
AI Executive Summary
Autonomous unmanned aerial vehicles (UAVs) are increasingly vital in search-and-rescue, environmental monitoring, and military reconnaissance. A core challenge lies in enabling these systems to autonomously plan informative paths that maximize target discovery and classification within limited energy and time budgets. Traditional heuristic or optimization-based methods often struggle in complex, dynamic environments, especially when prior knowledge about target locations is inaccurate or outdated.
This paper introduces a novel deep reinforcement learning (RL) framework that leverages a library of motion primitives to generate dynamically feasible, agile paths for UAVs. The approach employs the Asynchronous Advantage Actor-Critic (A3C) algorithm to train a policy network that balances exploration and exploitation, guided by Bayesian models of environment belief updates. The policy considers environmental state, target priors, and sensor likelihoods, enabling real-time adaptive path planning.
Extensive simulations demonstrate that this method significantly outperforms baseline heuristics, achieving over 92% target discovery and 81% entropy reduction. It maintains robustness against prior distribution deviations, with performance degrading minimally even at high KL divergence levels. The integration of motion primitives ensures paths respect UAV dynamics, facilitating smooth, feasible trajectories during rapid exploration.
The significance of this work lies in its ability to address the limitations of existing methods by combining the flexibility of deep RL with the physical constraints of UAV motion. This results in a scalable, robust solution capable of operating in uncertain, complex environments, with promising applications in real-world search-and-rescue missions, environmental surveillance, and beyond. Future directions include real-world validation, multi-UAV coordination, and extending the framework to 3D environments, paving the way for smarter, more autonomous aerial systems.
Deep Dive
Abstract
Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions. This paper develops a deep reinforcement learning approach to plan informative trajectories that increase the likelihood for an uncrewed aerial vehicle (UAV) to discover missing targets. Our approach efficiently (1) explores the environment to discover new targets, (2) exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification, and (3) generates dynamically feasible trajectories for an agile UAV by employing a motion primitive library. Extensive simulations on randomly generated environments show that our approach is more efficient in discovering and classifying targets than several other baselines. A unique characteristic of our approach, in contrast to heuristic informative path planning approaches, is that it is robust to varying amounts of deviations of the prior belief from the true target distribution, thereby alleviating the challenge of designing heuristics specific to the application conditions.