Multi-Robot Target Tracking with Sensing and Communication Danger Zones: Optimizing Tracking Performance while Ensuring Robot Safety

Concetti Chiave

The core message of this article is to propose a novel multi-robot active target tracking framework that considers the existence of sensing and communication danger zones in the environment. The authors formulate the tracking problem as a nonlinear optimization that balances tracking performance and robot safety, and provide practical approximations to efficiently solve the chance-constrained optimization.

Sintesi

This paper presents a multi-robot target tracking framework that accounts for the presence of sensing and communication danger zones in the environment. The authors categorize the adversarial attacks into two types: those that can induce sensor failures, and those that can jam communication channels between robots.

The authors formulate the tracking problem as a nonlinear optimization that aims to minimize the tracking error while ensuring robot safety. They model the danger zones as probabilistic constraints and provide practical approximations to convert the chance-based constraints into deterministic ones, enabling efficient online planning.

The key highlights of the paper include:

• Formulation of the multi-robot target tracking problem with sensing and communication danger zones as a nonlinear optimization problem.
• Design of safe distance conditions towards the two types of danger zones as probabilistic constraints, and practical approximations to solve the computationally challenging problem.
• Thorough evaluations in simulations demonstrating the risk-aware behaviors of robots under different uncertainty levels and risk requirements.
• Hardware experiments validating the robustness and effectiveness of the proposed approach using a team of Crazyflie drones tracking ground robots.

The authors show that the robots exhibit corresponding risk-aware behaviors in response to changes in uncertainty levels and risk requirements. When the uncertainty in the danger source's position increases or the required risk level decreases, the robots maintain a larger distance from the danger zones to ensure their safety, even at the cost of some tracking performance.

Statistiche

The authors derive the measurement model for the robots, which consists of range and bearing measurements, and use it to compute the uncertainty in the target position estimation.
The probability of sensor failure is estimated through sampling the actual position of the danger source 1000 times from its distribution, and checking if the robot is within the safety clearance for each sample.
The probability of communication jamming is also estimated through sampling the position of the jamming source 1000 times, and checking if the ratio of the robot's distance to the jamming source and its distance to the furthest teammate is below a threshold for each sample.

Citazioni

"To address this challenge, we investigate multi-robot target tracking in the adversarial environment considering sensing and communication attacks with uncertainty."
"We design specific strategies to avoid different danger zones and proposed a multi-agent tracking framework under the perilous environment."
"We evaluate the performance of our proposed methods in simulations to demonstrate the ability of robots to adjust their risk-aware behaviors under different levels of environmental uncertainty and risk confidence."

Approfondimenti chiave tratti da

Multi-Robot Target Tracking with Sensing and Communication Danger Zones

by Jiazhen Li,P... alle arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07880.pdf

Domande più approfondite

How can the proposed framework be extended to handle dynamic danger zones, where the positions of the sensing and communication attack sources change over time

To handle dynamic danger zones where the positions of the sensing and communication attack sources change over time, the proposed framework can be extended by incorporating real-time localization and tracking of these sources. This would involve integrating sensor data from the robots to continuously update the positions of the danger zones. The framework can utilize techniques such as simultaneous localization and mapping (SLAM) to dynamically map the environment and track the movement of the attack sources. By updating the positions of the danger zones in real-time, the robots can adjust their trajectories and behaviors to avoid the evolving threats effectively.

How can the framework be adapted to incorporate other types of adversarial attacks, such as false data injection or denial-of-service attacks, and ensure the overall resilience of the multi-robot system

To adapt the framework to incorporate other types of adversarial attacks like false data injection or denial-of-service attacks, additional constraints and risk models need to be integrated into the optimization problem. For false data injection attacks, the framework can include data integrity checks and verification mechanisms to detect and mitigate the impact of malicious data. In the case of denial-of-service attacks, the system can implement redundancy and fault-tolerant strategies to ensure continuous communication and coordination among the robots. By enhancing the framework with these resilience mechanisms, the multi-robot system can maintain its functionality and performance even in the presence of diverse adversarial threats.

What are the potential applications of this risk-aware multi-robot target tracking approach beyond the environmental surveillance and monitoring scenarios discussed in the paper, and how can it be tailored to those domains

The risk-aware multi-robot target tracking approach proposed in the paper has potential applications beyond environmental surveillance and monitoring scenarios. One such application is in security and defense, where the framework can be utilized for perimeter patrolling, intruder detection, and secure area monitoring. In industrial settings, the approach can be adapted for asset tracking, inventory management, and automated inspection tasks. In the field of healthcare, the framework can support patient monitoring, asset tracking in hospitals, and emergency response coordination. By tailoring the framework to these domains, it can enhance operational efficiency, safety, and security across various industries and applications.