Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-tagged Objects (1808.04445v2)

Abstract: We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.

• The paper proposes a novel online UAV path planning algorithm using a partially observable Markov decision process and a track-before-detect filter for robust multi-object detection and tracking in low-SNR environments.
• A separable multi-object likelihood function is derived for raw signals, improving the computational efficiency of the multi-object track-before-detect filter.
• The method integrates safety by incorporating void probability for maintaining UAV-object distance constraints and demonstrates superior performance over traditional detection-based methods, especially in low-SNR conditions.

Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-tagged Objects

The paper addresses the technical challenge of using unmanned aerial vehicles (UAVs) for the online path planning problem in the context of detecting and tracking multiple radio-tagged objects. This challenge is relevant across various applications, including wildlife monitoring and search-and-rescue missions. The approach proposed involves the strategic planning of UAV trajectories to optimize the detection and tracking of objects tagged with low-power radio signals, which are often in environments characterized by low signal-to-noise ratios (SNRs).

The heart of this work is a novel path planning algorithm based on a partially observable Markov decision process (POMDP), leveraging a track-before-detect (TBD) multi-object filter. This is a marked improvement over traditional methods that typically rely on a detection-based approach, which is inadequate in low SNR conditions due to information loss during thresholding. The proposed filter is designed using a random finite set (RFS) framework, incorporating a jump Markov system to better handle the dynamics and maneuverability of various objects.

Key Contributions

1. POMDP Framework with TBD Filter: A POMDP framework is integrated with a novel TBD filter to plan UAV trajectories. The TBD approach bypasses the traditional detection step, allowing for the direct processing of noisy signal data and effectively managing multiple maneuvers of objects.
2. Separable Multi-object Likelihood: The derivation of a separable multi-object likelihood function for the raw signals in the time-frequency domain contributes to the efficiency of the multi-object TBD filter, significantly enhancing computational feasibility.
3. Incorporation of Safe Distance Constraints: The solution integrates a void probability approach to ensure UAV-object distance constraints are maintained, which is crucial in practical applications to avoid disturbing wildlife or violating airspace safety protocols.
4. Robust Performance in Low SNR Environments: Numerical evaluations demonstrate superior performance in scenarios with low SNRs. The algorithm successfully detects and tracks multiple objects while accounting for their various motion and dynamic mode changes.

Strong Numerical Results and Claims

The evaluations conducted provide demonstrable efficacy with compelling numerical results indicating that the TBD-based path planning significantly outperforms detection-based counterparts, particularly highlighted by the improvement in tracking precision within low SNR contexts. This underlines the practicality and strength of the proposed method in real-world applications where traditional methods may falter.

Implications for Future AI Developments

The proposed approach opens up avenues for further enhancement of UAV path planning in complex environments with uncertainty and myriad dynamic elements. The integration of RFS-based TBD filters within a POMDP framework exemplifies how advanced probabilistic models can be employed to improve autonomous systems. As AI techniques continue to evolve, combining machine learning insights with probabilistic modeling frameworks could enhance the adaptability and intelligence of UAVs in dynamic and uncertain environments.

Future Directions

In extending this research, one could explore integrating machine learning models to learn object behaviors and signal patterns dynamically, further reducing computational costs and enhancing real-time processing capability. Moreover, real-world deployments could yield valuable insights into constraints and environmental factors not captured in simulations, guiding practical improvements and adaptations.

In conclusion, this paper offers a robust framework and methodology that can significantly enhance the efficacy of UAV-based detection and tracking systems, thereby contributing to advancements in both theoretical research and practical applications in robotics and autonomous systems.