An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset ‘UAV_Fire’. A 15-layered self-learning DCNN architecture named ‘Fire_Net’ is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, ‘Fire_Net’ guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified.

Plug-in Hybrid Electric Vehicles (PHEVs), as a new type of environmental-friendly low cost transportation, have attracted growing interests for logistics. The path-planning optimization for PHEV has become a major challenge. In fact, PHEV-based routing optimization is a type of hybrid vehicle routing problem (HVRP). Compared with the traditional Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP), the PHEV routing problem should consider more constraints, such as time limits, capacity constraints (including fuel tank capacity and battery capacity), electric stations, fuel stations and so forth. In this paper, a Mixed Integer Linear Programming formulation is presented and a novel hybrid metaheuristic approach (HMA_SVND) is proposed. Our method is a combination of memetic algorithm (MA), sequential variable neighborhood descent (SVND) and a revised 2_opt method. Comparative studies show that our proposed method outperformed previous works.

This paper focuses on a modified Multi-Depot Unmanned Aerial Vehicle Routing Problem (MMDUAVRP). Comparing to classic multi-depot vehicle routing problem, our studied problem has no constraints to restrict the depot where the Unmanned Aerial Vehicle (UAV) departs and returns. This work aims to minimize the number of UAVs and total distance traveled by all UAVs. This problem is mathematically formulated in this paper and a heuristic-assignment based hybrid large neighborhood search(HLNS) is proposed to solve it. Extensive computational experiments are conducted to verify the performance of HLNS. The HLNS algorithm was first tested on Multi Traveling Salesman Problem which is a simplified version of the MMDUAVRP, and high quality solutions have been obtained. Experimental results by compared with CPLEX and other well-known algorithms suggest that our proposed algorithm provides better solutions within a comparatively shorter period of time. In addition, we also conduct sensitivity analysis on the location of depots and task points that may affect the total cost of solution.

This paper deals with the multi-objective job shop scheduling problem in a robotic cell (MOJRCSP). All the jobs are processed according to their operations order on workstations. Different from classical job shop scheduling problem, the studied problem considers that jobs’ transportation is handled by a robot. Also, the jobs are expected to be finished in a time window, instead of a constant due date. A mixed Integer Programming (MIP) model is proposed to formulate this problem. Due to the special characteristics of the studied problem and its NP-hard computational complexity, a metaheuristic based on Teaching Learning Based Optimization (TLBO) algorithm has been proposed. The proposed algorithm determines simultaneously the operations’ assignments on workstations, the robot assignments for transportation operations, and the robot moving sequence. The objective is to minimize the makespan and the total earliness and tardiness. Computational results further validated the effectiveness and robustness of our proposed algorithm.

Semantic segmentation is one of the most critical modules in road scene understanding. In this paper, we focus on the challenging task of pedestrian’s relative location perception in the semantic graph of complex driving scenes. Prevalent research on semantic segmentation mainly concentrate on improving the segmentation accuracy with less attention paid to computational efficiency. Furthermore, little effort has been made in pedestrian location perception in complex driving scenes. For example, current semantic segmentation methods classify all pedestrians as a mono category, regardless of whether the pedestrians are penetrating into the vehicular lane or standing still in the safe sidewalk area. We propose a pedestrian location perception network (P-LPN). P-LPN can produce real-time semantic segmentation while simultaneously providing location inference for each pedestrian in semantic maps. This enables autonomous driving system to categorize pedestrians into different safety levels. We comprehensively evaluated P-LPN on CityScapes benchmark through comparative studies. Our proposal achieved competitive performance in both accuracy and efficiency. It yields quality inference with real-time speed at ~22 fps.