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Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization

Zhiyou Ji, Yujie Wan, Guoliang Li, Shuai Wang, Kejiang Ye, Derrick Wing Kwan Ng, Chengzhong Xu

TL;DR

This letter proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance.

Abstract

Robotic sensor network (RSN) is an emerging paradigm that harvests data from remote sensors adopting mobile robots. However, communication and control functionalities in RSNs are interdependent, for which existing approaches become inefficient, as they plan robot trajectories merely based on unidirectional impact between communication and control. This paper proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control (MPC2) design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance. The MPC2 problem jointly optimizes the cross-layer variables of sensor powers and robot actions, and is solved by alternating optimization, strong duality, and cross-horizon minorization maximization in real time. This approach contrasts with conventional communication control co-design methods that calculate an offline non-executable trajectory. Experiments in a high-fidelity RSN simulator demonstrate that the proposed MCCA outperforms various benchmarks in terms of communication efficiency and navigation time.

Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization

TL;DR

This letter proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance.

Abstract

Robotic sensor network (RSN) is an emerging paradigm that harvests data from remote sensors adopting mobile robots. However, communication and control functionalities in RSNs are interdependent, for which existing approaches become inefficient, as they plan robot trajectories merely based on unidirectional impact between communication and control. This paper proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control (MPC2) design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance. The MPC2 problem jointly optimizes the cross-layer variables of sensor powers and robot actions, and is solved by alternating optimization, strong duality, and cross-horizon minorization maximization in real time. This approach contrasts with conventional communication control co-design methods that calculate an offline non-executable trajectory. Experiments in a high-fidelity RSN simulator demonstrate that the proposed MCCA outperforms various benchmarks in terms of communication efficiency and navigation time.
Paper Structure (14 sections, 1 theorem, 14 equations, 6 figures)

This paper contains 14 sections, 1 theorem, 14 equations, 6 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Robot Control
  4. Robot Communication
  5. Mutual Communication Control Assistance
  6. Motion-Assisted Communication Utility
  7. Communication-Assisted Collision Avoidance
  8. Model Predictive Communication and Control
  9. Fast Cross-Layer Optimization
  10. Alternating Optimization
  11. Duality-Based Optimization
  12. Cross-Horizon MM
  13. Simulation Results
  14. Conclusion

Key Result

Proposition 1

$\{\Phi_{k,t}\}$ satisfy the following conditions: (i) Concavity: $\Phi_{k,t}(\mathbf{s}_{t}|\mathbf{s}_{t}^\star)$ is concave in $\mathbf{s}_{t}$. (ii) Lower bound: $\Phi_{k,t}(\mathbf{s}_{t}|\mathbf{s}_{t}^\star)\leq R_{k,t}(\mathbf{s}_{t})$. (iii) $\Phi_{k,t}(\mathbf{s}_{t}^\star|\mathbf{s}_{t}^\

Figures (6)

  • Figure 1: System model of RSN, where blue line denotes global coarse path, red line denotes collision-free executable path, grey boxes denote obstacles, grey balls denote sensors.
  • Figure 2: Quantitative comparison of different schemes.
  • Figure 3: Trajectories of different schemes. The sensor is marked as a red circle and obstacles are marked as black circles. The global route is marked as a black line. Actual robot path is represented by a red line. The robot is marked as a blue box.
  • Figure 4: Simulated scenario 1 in CARLA and the sensor powers, robot trajectories, control commands of different schemes.
  • Figure 5: Simulated scenario 2 in CARLA.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof