No abstract

No abstract

A high-performance electrochromic-energy storage device (EESD) is developed, which successfully realizes the multifunctional combination of electrochromism and energy storage by constructing tungsten trioxide monohydrate (WO·HO) nanosheets and Prussian white (PW) film as asymmetric electrodes. The EESD presents excellent electrochromic properties of broad optical modulation (61.7%), ultrafast response speed (1.84/1.95 s), and great coloration efficiency (139.4 cm C). In particular, remarkable cyclic stability (sustaining 82.5% of its initial optical modulation after 2500 cycles as an electrochromic device, almost fully maintaining its capacitance after 1000 cycles as an energy storage device) is achieved. The EESD is also able to visually detect the energy storage level via reversible and fast color changes. Moreover, the EESD can be combined with commercial solar cells to constitute an intelligent operating system in the architectures, which would realize the adjustment of indoor sunlight and the improvement of physical comfort totally by the rational utilization of solar energy without additional electricity. Besides, a scaled-up EESD (10 × 11 cm) is further fabricated as a prototype. Such promising EESD shows huge potential in practically serving as electrochromic smart windows and energy storage devices.

No abstract

This paper presents a systematical framework to solve the multiple unmanned aerial vehicles (multi-UAV) cooperative task assignment problem. Based on a combinatorial optimization model, it is solved by a digraph-based method and a novel meta-heuristic optimization method, named modified two-part wolf pack search (MTWPS) algorithm. When the number of UAVs/targets is large, in order to reduce the simulation time, we also present a new solution framework based on an easy-computing objective function. Additionally, the parameter and time-sensitive uncertainty are considered in the extended task assignment problem. For the problem with parameter uncertainty, it is formulated by a robust optimization method and solved by a novel combined algorithm, including the classical interior point method and our MTWPS algorithm. For the problem with time-sensitive uncertainty, it is solved by a practical online hierarchical planning algorithm. Finally, numerical simulations and physical experiments demonstrate that the proposed methods can provide a flyable solution for the UAVs and achieve outstanding performance in comparison with other algorithms. Index Terms-Multiple unmanned aerial vehicles (multi-UAV) cooperative task assignments problem, modified two-part wolf pack search (MTWPS) algorithm, robust optimization method, online hierarchical planning algorithm Yongbo Chen received his B.S. degree in Beijing Institute of Technology in 2012. He is currently working toward a dual doctoral degree at Beijing

This paper presents an efficient and feasible algorithm for the path planning problem of the multiple unmanned aerial vehicles (multi-UAVs) formation in a known and realistic environment. The artificial potential field method updated by the additional control force is used for establishing two models for the single UAV, which are the particle dynamic model and the path planning optimization model. The additional control force can be calculated by using the optimal control method. Furthermore, the multi-UAV path planning model is established by introducing "virtual velocity rigid body" and "virtual target point". Then, the motion states of the lead plane and wingmen are obtained from the path planning model. Finally, the path following process based on the quadrotor helicopter PID controllers is introduced to verify the rationality of the path planning results. The simulation results show that the artificial potential method with the additional control force improved by the optimal control method has a good path planning ability for the single UAV and the all UAVs formation. At the same time, the path planning results are available and the UAVs can basically track the UAV formation.

No abstract

We present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control (MPC) framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained non-linear least-squares problem. A convex half-space representation is applied to relax non-convex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition (SVD). The area coverage task is addressed with a sequential quadratic programming (SQP) method. A submap joining approach, called Linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.