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Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization
Robotics · Submitted 16 Apr 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experiments in a high-fidelity RSN simulator demonstrate that the proposed MCCA outperforms various benchmarks in terms of communication efficiency and navigation time.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Robotic sensor network (RSN) is an emerging paradigm that harvests data from remote sensors adopting mobile robots. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | 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. | gap 0.73 | 0.08 | 0.03 | asserted 1.00 |
| s2 | 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. | contribution 0.94 | 0.83 | 0.27 | asserted 1.00 |
| s3 | 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. | method 0.99 | 0.91 | 0.08 | asserted 1.00 |
| s4 | This approach contrasts with conventional communication control co-design methods that calculate an offline non-executable trajectory. | contribution 0.76 | 0.23 | 0.07 | asserted 1.00 |
| s5 | Experiments in a high-fidelity RSN simulator demonstrate that the proposed MCCA outperforms various benchmarks in terms of communication efficiency and navigation time. | result 1.00 | 0.96 | 0.70 | asserted 1.00 |
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The paper
Original 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.
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Contests 1 paper
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On the other hand, robotic-centric CAMP approaches, e.g., [ref] , consider collision avoidance, but they fail in directly maximizing the throughput, e.g., they just use signal strength for monitoring connectivity.
Edge Accelerated Robot Navigation With Collaborative Motion Planning · Introduction · contest 0.84
Cites as background 1 paper
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Current CAMP can be categorized into communication-centric [ref] and robotic-centric approaches [ref] .
Edge Accelerated Robot Navigation With Collaborative Motion Planning · Related Work · 0.92
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