How to work effectively under occlusion-rich environments remains a challenge for airborne vision-based ground target tracking, due to the natural limitation of monocular vision. Given this, a novel cross-drone binocular coordination approach, inspired by the efficient coordination of human eyes, is proposed and developed in this article. The idea, derived from neural models of the human visual system, is to utilize distributed target measurements to overcome occlusion effects. Therefore, a binocular coordination controller is developed. It enables two distributed pan-tilt cameras to execute synergistic movements similar to human eyes. The proposed approach is able to work based on binocular vision or monocular vision, hence caters for various environments. Both testbed experiments and field experiments are provided for performance evaluation. Testbed experiments highlight its advantages over independent tracking in terms of accuracy while being robust to a partial perception ratio of up to 43%. Field experiments with a pair of DJI M210 drones further demonstrate its effectiveness in the real scene.

The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage with multiple energy-limited UAVs in dynamic environments, which we call MCTA (Multi-UAV Coverage through Two-step Auction). Specifically, the online two-step auction mechanism is proposed to select the optimal action. Then, an obstacle avoidance mechanism is designed by defining several heuristic rules. After that, considering energy constraints, we develop the reverse auction mechanism to balance workload between multiple UAVs. Comprehensive experiments demonstrate that MCTA can achieve a high coverage rate while ensuring a low repeated coverage rate and average step deviation in most circumstances.

Flocking navigation, involving alignment-guaranteed path following and collision avoidance against obstacles, remains to be a challenging task for drones. In this paper, we investigate how to implement flocking navigation when only one drone in the swarm masters the predetermined path, instead of all drones mastering their routes. Specifically, this paper proposes a hierarchical weighting Vicsek model (WVEM), which consists of a hierarchical weighting mechanism and a layer regulation mechanism. Based on the hierarchical mechanism, all drones are divided into three layers and the drones at different layers are assigned with different weights to guarantee the convergence speed of alignment. The layer regulation mechanism is developed to realize a more flexible obstacle avoidance. We analyze the influence of the WVEM parameters such as weighting value and interaction radius, and demonstrate the flocking navigation performance through a series of simulation experiments.

Flocking navigation and obstacle avoidance in complex environments remain challenging for multiple unmanned aerial vehicle (multi-UAV) systems, especially when only one UAV (termed as information UAV) knows the predetermined path and the communication range is limited. To this end, we propose a hierarchical weighting Vicsek model (HWVEM). In this model, a hierarchical weighting mechanism and an obstacle avoidance mechanism are designed. Based on the hierarchical weighting mechanism, all the UAVs are divided into different layers, and assigned with different weights according to the layer to which they belong. The purpose is to align the rest of UAVs with the information UAV more efficiently. Subsequently, the obstacle avoidance mechanism that utilizes only the local information is developed to ensure the system safety in an environment filled with obstacles differing in size and shape. A series of simulations have been conducted to demonstrate the high performance of HWVEM in terms of convergence time, success rate, and safety.

Binocular intersection is practically indispensable for stereo visual systems that extract 3D information from images of two distinct cameras. This paper proposes a multi-UAV binocular intersection approach where the two distinct cameras are fixed separately on multiple flying unmanned aerial vehicles (UAVs), rather than physically connected with each other as in the pre-existing works. Eventually, both modeling and algorithms are developed for the multi-UAV binocular intersection with extremely limited communication. As the formulated model states, once one flying vehicle detects the target and shares related information only in a one-shot manner, the other vehicles steer their pan-tilt units (PTUs) to reconstruct a spatial binocular intersection automatically. The binocular intersection algorithm is further improved from two aspects to maximize the probability of target capture. On one hand, the search scope is narrowed by developing an iterative algorithm with priori terrain height and variance. On the other hand, an approaching navigation law is designed to reduce view occlusion caused by terrain. Extensive simulations with different target speeds and flight altitudes are carried out to evaluate the effectiveness of the proposed method. The results show that the probability of binocular intersection is over 90% and is subject to little fluctuation against episodic parameter changes. Furthermore, the developed algorithms are verified localization-free and time-saving as well, and hereafter are potentially practicable for micro aerial swarm applications with extremely limited onboard computation and communication.