3D Guidance Law for Flexible Target Enclosing with Inherent Safety (2404.16312v3)

Abstract: In this paper, we address the problem of enclosing an arbitrarily moving target in three dimensions by a single pursuer while ensuring the pursuer's safety by preventing collisions with the target. The proposed guidance strategy steers the pursuer to a safe region of space surrounding and excluding the target, allowing it to maintain a certain distance from the latter while offering greater flexibility in positioning and converging to any orbit within this safe zone. We leverage the concept of the Lyapunov Barrier Function as a powerful tool to constrain the distance between the pursuer and the target within asymmetric bounds, thereby ensuring the pursuer's safety within the predefined region. Further, we demonstrate the effectiveness of the proposed guidance law in managing arbitrarily maneuvering targets and other uncertainties (such as vehicle/autopilot dynamics and external disturbances) by enabling the pursuer to consistently achieve stable global enclosing behaviors by switching between stable enclosing trajectories within the safe region whenever necessary, even in response to aggressive target maneuvers. To attest to the merits of our work, we conduct experimental tests with various plant models, including a high-fidelity quadrotor model within Software-in-the-loop (SITL) simulations, encompassing various challenging target maneuver scenarios and requiring only relative information for successful execution.

• The paper introduces a novel 3D guidance law that leverages Lyapunov Barrier Functions to maintain UAV proximity and ensure collision avoidance during aggressive target maneuvers.
• It employs a coupled kinematic model optimizing control inputs in the pitch and yaw planes, thereby reducing energy consumption while ensuring safe enclosure.
• The approach is validated through high-fidelity SITL simulations, confirming robust performance across dynamic scenarios and GPS-denied environments.

Analysis of "3D Guidance Law for Maximal Coverage and Target Enclosing with Inherent Safety"

The research paper "3D Guidance Law for Maximal Coverage and Target Enclosing with Inherent Safety" presents a systematic approach to maneuver a single unmanned aerial vehicle (UAV) around a mobile target in a three-dimensional space. This task is pivotal for applications such as surveillance, reconnaissance, and environmental monitoring, where an autonomous system must effectively monitor objects or areas of interest while ensuring operational safety.

3D Guidance Framework

The authors introduce a 3D guidance strategy that incorporates nonholonomic kinematic constraints and uses the concept of Lyapunov Barrier Function to manage the spatial relationship between the pursuer and the target. The guidance law is designed to ensure that the pursuer, modeled as a UAV, maintains a consistent proximity to the target within a bounded safe operational region. The enforcement of state constraints through the Lyapunov Barrier Function guarantees collision avoidance, even when the target performs aggressive maneuvers.

The paper details the mathematical formulation of the guidance law, employing a coupled engagement kinematic model with control inputs defined by accelerations. The optimization of control inputs considers energy expenditure in both the pitch and yaw planes, optimizing the pursuer's maneuverability without deviating from the desired motion plan.

Validation and Empirical Analysis

The authors validate their approach through Software-in-the-loop (SITL) simulations, involving high-fidelity quadrotor models and dynamic scenarios, including stationary, constant velocity, and maneuvering targets. The implementation of the design is shown to be robust against unmodeled dynamics and system disturbances. The numerical results highlight that the guidance law successfully maintains the pursuer within the safe region, achieving the desired enclosing behavior while mitigating energy expenditure.

Implications and Conclusions

The implications of this paper are significant for future autonomous multi-agent systems where individual agents need to maintain safe and efficient operation in complex environments. This research could inform developments in both theoretical and practical approaches to motion planning and collision avoidance in constrained spaces. The method's reliance on relative information further enhances its applicability for UAVs operating in GPS-denied environments.

This work invites further exploration in several directions. The consideration of aerodynamic model variations, more complex multi-agent interactions, and the potential inclusion of additional environmental constraints could enhance understanding and broaden the application scope. Researchers can build on this framework to explore advanced autonomy capabilities for a diverse range of UAV applications.

In summary, this paper provides a comprehensive and technically rigorous approach to UAV guidance under nontrivial kinematic and operational constraints, offering a valuable contribution to the field of autonomous guidance and navigation.