We focus on focuses on the local topology inference problem of MRNs under first-order linear formation control, where an inference robot can manoeuvre among the formation robots and observe their motions. Specifically, the inference robot has no knowledge of the formation inputs and interaction parameters, and the observation range is strictly limited.
We focus on the directed topology inference of NSs in state-space representation, where the observations are corrupted by noises. Specifically, we aim to reveal the relationships between the basic inference principles using observations from multiple and single trajectory of NSs, respectively. Meanwhile, we seek to derive the non-asymptotic convergence rate and accuracy of the inference methods about the observation number.
We investigate a novel intelligent physical attack against mobile robots without relying on any prior knowledge. The ultimate goal of the attacker is to learn the obstacle-avoidance mechanism of a mobile robot from external observation, and then leverage it to fool the target robot into a preset trap.