In this article, we investigate the problem of controlling a multirobot team to follow a leader in formation, supported by a relative position estimate derived from distance and self-displacement measurements, thus waiving the need of external localization infrastructure. The main challenge of the problem, which is to simultaneously fulfill both relative localization and control tasks, is efficiently and novelly resolved by embedding a distance-displacement-based persistently excited adaptive relative localization technique into a time-varying formation with bounded control input (PEARL-TVF). By assuming that the leader is globally reachable and by selecting proper parameters, it is shown that the PEARL-TVF ensures exponentially convergent localization, which leads to exponentially convergent formation when the leader's behavior is deterministic, and bounded formation error for a nondeterministic leader. Numerical simulations and experiments on quadcopters are provided to verify the theoretical findings.

Though vision-based techniques have become quite popular for autonomous docking of Unmanned Aerial Vehicles (UAVs), due to limited field of view (FOV), the UAV must rely on other methods to detect and approach the target before vision can be used. In this paper we propose a method combining Ultra-wideband (UWB) ranging sensor with vision-based techniques to achieve both autonomous approaching and landing capabilities in GPS-denied environments. In the approaching phase, a robust and efficient recursive least-square optimization algorithm is proposed to estimate the position of the UAV relative to the target by using the distance and relative displacement measurements. Using this estimate, UAV is able to approach the target until the landing pad is detected by an onboard vision system, then UWB measurements and vision-derived poses are fused with onboard sensor of UAV to facilitate an accurate landing maneuver. Real-world experiments are conducted to demonstrate the efficiency of our method.

In this paper we study the distance-based docking problem of unmanned aerial vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown position. To solve the problem, we propose an integrated estimation-control scheme to simultaneously achieve the relative localization and navigation tasks for discrete-time integrators under bounded velocity: a nonlinear adaptive estimation scheme to estimate the relative position to the landmark, and a delicate control scheme to ensure both the convergence of the estimation and the asymptotic docking at the given landmark. A rigorous proof of convergence is provided by invoking the discrete-time LaSalle's invariance principle, and we also validate our theoretical findings on quadcopters equipped with ultra-wideband ranging sensors and optical flow sensors in a GPS-less environment.

In the era of Internet of Things, WiFi fingerprinting based indoor positioning system (IPS) has been recognized as the most promising IPS for indoor location-based service. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods, and extreme machine learning (ELM) is preferred for its fast training speed. However, traditional WiFi based IPS usually requires a central server to collect and process data, which is tremendously vulnerable to server breakdown and communication link failure. To address this issue, we propose Consensus-based Parallel ELM (CPELM) to enhance the robustness by distributing the data on different computation nodes. Specifically, each node keeps updating the corresponding terms in the ELM regression equation as a weighted average of those from neighboring nodes based on the distributed consensus iterative scheme. Upon the agreement of the regression equation within the network, the output weight of ELM can be calculated on some nodes and propagated to other nodes. Extensive simulation with real data has demonstrated that CPELM is able to produce same level of localization accuracy as centralized ELM without incurring additional computational cost, and in the meanwhile provides more robustness to the entire IPS in case of server breakdown and link failures.

We study a distributed constrained optimal consensus problem of discrete-time multi-agent systems under fixed communication delays. Specifically, the total cost is expressed as the sum of individual cost of each agent, and only part of agents have access to the constraint. The constrained optimal consensus is solved if the final consensus value falls within the constraint, and in the meanwhile minimizes the total cost. Based on consensus method and subgradients, we propose a distributed two-step update scheme in which the state of each agent is firstly averaged with the delayed state information from neighbors, followed by a decaying subgradient descent from the individual cost, together with a movement along the projection direction if the agent can access the constraint. We show that the distributed constrained optimal consensus problem under fixed communication delays can be solved if the fixed network is balanced and contains a spanning tree, the constraint is accessible by at least one agent, and the gain on subgradient is decaying but persistent. Simulation results are provided to verify our conclusion.