To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.

This paper investigates a special task allocation problem with constraints of path planning. The path planning process involved is defined as the follow-up step of task allocation. The allocation problem is coupled with path constraints, which will affect the utility of allocation solution. A homotopic approach featuring two homotopy primitives is proposed to resolve the complexity caused by the coupling between task allocation and path planning. Reference paths generated by the homotopic approach are transformed to desired task bundles for robots via transformation functions. A distributed task allocation architecture with market mechanism is employed to make consensus on task allocation solution in multi-robot. Simulation results validate the performance of the proposed methods comparing with conventional allocation methods.

This brief proposes the first model reference adaptive control (MRAC) method for aortic pressure (AoP) regulation and maintaining the heart's physiological aerobic metabolism in ex vivo heart perfusion (EVHP). A mathematical model of the EVHP was established to describe the hemodynamic behavior of EVHP and quantify the changes in cardiac parameters. A reference model consisting of a virtual proportional-integral-derivative (PID) controller and the EVHP model was employed to generate the reference trajectory of AoP. An adaptation algorithm tunes the control parameters based on the reference model and the isolated heart. Experiments were conducted using large animal hearts (50 ± 5 kg porcine) to validate the adaptive controller's performance for stepwise and fast switching AoP references. The results confirmed the effectiveness of the proposed controller for regulating the AoP in isolated porcine hearts, in an accurate (mean error less than 2 mmHg) and fast (4-8 s of settling time) manner.

This paper focuses on the dynamics and control of piezoactuators (PEAs) for high-speed large-range scanning applications. Firstly, the nonlinear hysteresis is modeled by using a modified Maxwell resistive capacitor (MRC) model. Secondly, an inverse-based feedforward controller is proposed for this application with hysteresis compensation. Then, the scanning trajectories are preshaped by treating the hysteresis-compensated PEA as a linear system. Finally, experiments are conducted to verify the effectiveness of the proposed approaches.

A hyperspectral short-wave infrared (SWIR) imager with a HgCdTe focal plane detector usually suffers from interference fringes in spectral and spatial dimension due to the etalon effect. It can cause a modulation of sensitivity higher than 15% when the spectral resolution is higher than 10 nm. In this article, a two-step method is proposed and evaluated to remove interference fringes for hyperspectral SWIR imagers. First, we extract the spectral data, conduct harmonic decomposition to suppress the fringes in spectral dimension, and calculate the correction coefficients. Second, we optimize the correction coefficients according to the distribution of the interference fringes in spatial dimension. Sample SWIR images acquired from the Advanced Hyperspectral Imager (AHSI) on China's GaoFen-5 satellite are used to evaluate the performance of the algorithm. It shows that the proposed method can well preserve the original spectral pattern and reduce the peak-to-peak amplitude of spatial fringing from ±15% to ±4%. The method is compared with three other previously proposed methods. It turns out that the newly proposed method has strong adaptability, high accuracy, and high efficiency.

Aerial recovery technology for aircrafts plays a significant role in practical applications, which presents a challenging problem in cooperative trajectory planning. To solve the problem, a hierarchical trajectory planning architecture is proposed in this paper. Firstly, the waypoints of the mother aircraft trajectory are obtained based on the distribution of the child aircrafts by using a hierarchical clustering algorithm. A greedy algorithm is used to obtain the traverse sequence for the waypoints and the mother aircraft trajectory is generated based on the Dubins path. Secondly, a genetic algorithm is employed to optimize the recovery position and recovery time for each child aircraft. Lastly, the trajectory of each child aircraft is generated by using a double-stage trajectory generation method. Simulation results validate the effectiveness of the proposed method for cooperative trajectory planning in aerial recovery.