We address the challenge of achieving flocking behavior in swarm robotic systems without compromising the privacy of individual robots’ aggregation capabilities. Traditional flocking algorithms are susceptible to privacy breaches, as adversaries can deduce the identity and aggregation abilities of robots by observing their movements. We introduce a novel control mechanism for privacy-preserving flocking, leveraging the Laplace mechanism within the framework of differential privacy. Our method mitigates privacy breaches by introducing a controlled level of noise, thus obscuring sensitive information. We explore the trade-off between privacy and utility by varying the differential privacy parameter$\epsilon$. Our quantitative analysis reveals that$\epsilon \leq 0.13$represents a lower threshold where private information is almost completely protected, whereas$\epsilon \geq 0.85$marks an upper threshold where private information cannot be protected at all. Empirical results validate that our approach effectively maintains privacy of the robots’ aggregation abilities throughout the flocking process.Note to Practitioners—This paper was motivated by the problem of preserving privacy of individual robots in a swarm robotic system. Existing approaches to address this issue generally consider that accomplishing complex tasks requiring explicit information sharing between robots, while explicit communication in public channel carries the risk of information leakage. It is not always like this in real adversarial environments, and this assumption restricts the investigation of privacy in autonomous systems. This paper suggests that an individual robot can use its sensors onboard to perceive states of other neighbors in a distributed way without explicit communication. Despite avoiding information leakage during explicit information sharing between robots, the configuration of swarm can still reveal sensitive information about the ability of each robot. In this paper, we propose a privacy-preserving approach for flocking control using the Laplace mechanism based on the concept of differential privacy. The solution prevents an adversary with full knowledge of the swarm’s configuration from learning the sensitive information of individual robots, thus ensuring the security of swarm robots in terms of sensitive information during ongoing missions.

This article presents a distributed control strategy for herding groups of evaders towards a predefined goal region using a team of robotic herders. In herding problems, evaders tend to move away from each other to increase their coverage regions. This makes it challenging to develop control solutions since the wandering evaders need to be collected while driving the herd. To address this, we propose the distributed outmost push strategy, where each robotic herder pushes the evader that is farthest from the goal region. The intuition behind this strategy is that robotic herders should focus on evaders that are further from the goal region as they are more likely to be missed during the herding process. The outmost evaders are selected from the local field of view, and the robotic herders make decisions in a decentralized manner. We also analyze the convergence of the designed dynamics and the minimum sensing range required for herders. The proposal's effectiveness and generality are validated through numerical simulations and real robotic experiments.

Homogeneous swarm robots are of significant research interest due to their robustness, flexibility, and scalability in completing complex tasks across various applications. This paper focuses on trapping heterogeneous targets using swarm robots, with emphasis on their different strengths. These targets consist of weak, strong, and group-moving individuals, where stronger targets exhibit larger body size, higher physical strength, and stronger resistance ability. Our goal is to develop an adaptive controller that enables swarm robots to self-organize and distribute themselves to trap targets, while adjusting encirclement thickness and robot group size based on target strength. We leverage local implicit information generated from density-based interaction to improve inter-robot and robot-target interactions through adaptive allocation and transformation mechanisms. The feasibility of our approach is validated through numerical simulations and experiments involving up to 50 physical robots and one human-controlled transformer.