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Non-cooperative Stochastic Target Encirclement by Anti-synchronization Control via Range-only Measurement

Fen Liu, Shenghai Yuan, Wei Meng, Rong Su, Lihua Xie

TL;DR

This work addresses encirclement of a non-cooperative stochastic moving target in GPS-denied environments using two agents that rely only on range measurements to estimate the target state $s(k)$ and its estimate $\hat{\boldsymbol s}(k)$. It introduces a target position estimator (TPE) and a distributed anti-synchronization controller (DASC) to achieve symmetric encirclement, with Lyapunov-based convergence proofs for $\hat{\boldsymbol e}(k)$ and $\boldsymbol e_s(k)$ via $V_1$ and $V_2$. The approach is validated through Matlab simulations and real-world UAV experiments using Tello drones and range estimation via ORBSLAM-derived relative poses; a video demonstration is provided. The work advances robust pursuit-evasion in constrained sensing settings and opens avenues for extending to Brownian-motion models and multi-target scenarios.

Abstract

This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. Considering the extreme environment, such as GPS denial, weight limit, and lack of ground guidance, two agents can only rely on their onboard single-modality perception tools to measure the distances to the target. The distance measurement allows for creating a position estimator by providing a target position-dependent variable. Furthermore, the construction of the unique distributed anti-synchronization controller (DASC) can guarantee that the two agents track and encircle the target swiftly. The convergence of the estimator and controller is rigorously evaluated using the Lyapunov technique. A real-world UAV-based experiment is conducted to illustrate the performance of the proposed methodology in addition to a simulated Matlab numerical sample. Our video demonstration can be found in the URL https://youtu.be/JXu1gib99yQ.

Non-cooperative Stochastic Target Encirclement by Anti-synchronization Control via Range-only Measurement

TL;DR

This work addresses encirclement of a non-cooperative stochastic moving target in GPS-denied environments using two agents that rely only on range measurements to estimate the target state and its estimate . It introduces a target position estimator (TPE) and a distributed anti-synchronization controller (DASC) to achieve symmetric encirclement, with Lyapunov-based convergence proofs for and via and . The approach is validated through Matlab simulations and real-world UAV experiments using Tello drones and range estimation via ORBSLAM-derived relative poses; a video demonstration is provided. The work advances robust pursuit-evasion in constrained sensing settings and opens avenues for extending to Brownian-motion models and multi-target scenarios.

Abstract

This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. Considering the extreme environment, such as GPS denial, weight limit, and lack of ground guidance, two agents can only rely on their onboard single-modality perception tools to measure the distances to the target. The distance measurement allows for creating a position estimator by providing a target position-dependent variable. Furthermore, the construction of the unique distributed anti-synchronization controller (DASC) can guarantee that the two agents track and encircle the target swiftly. The convergence of the estimator and controller is rigorously evaluated using the Lyapunov technique. A real-world UAV-based experiment is conducted to illustrate the performance of the proposed methodology in addition to a simulated Matlab numerical sample. Our video demonstration can be found in the URL https://youtu.be/JXu1gib99yQ.

Paper Structure

This paper contains 8 sections, 3 theorems, 31 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Problem formulation
  3. Estimator and controller design
  4. Estimator design
  5. Controller design
  6. Convergence analysis
  7. SIMULATIONS AND EXPERIMENTS
  8. Conclusions

Key Result

Lemma 1

Johnstone1982Exponential The sequence $\{p_{12}(k)\}, k\in[M,M+N-1], \forall M\in Z$ is persistently exciting for $\|\boldsymbol u_1(k)-\boldsymbol u_2(k)\|\leq\overline{\mu}$, that is, $0<\hat{\vartheta}I_n\leq\sum_{k=M}^{M+N-1}p_{12}(k) p_{12}^T(k)\leq\check{\vartheta}I_n<\infty$, then the followi where and $N$ is the motion period that occurs when the two tasking agents are moving around the t

Figures (8)

  • Figure 1: Overall illustration of proposed work.
  • Figure 2: The trajectories of the target estimation error and the AS error.
  • Figure 3: The trajectories of the two agents and the target for k=1:300.
  • Figure 4: The overall experiment setup.
  • Figure 5: The trajectories of all UAVs in the different time periods when UAV0 (target) is given motion rule.
  • ...and 3 more figures

Theorems & Definitions (6)

  • Definition 1
  • Lemma 1
  • Theorem 1
  • proof
  • Theorem 2
  • proof