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A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value. [ABSTRACT FROM AUTHOR]

A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value.

Trajectory planning for quadrotor has been extensively studied in terms of safety, smoothness, and dynamical feasibility. However, few methods have been proposed for the optimization of efficiency in long-distance navigation. Quadrotor often has high computational complexity when performing tasks that involve at least multiple computing modules such as location, mapping, planning, etc. Therefore, improving planning efficiency and flight efficiency can save computing resources and improve transport efficiency to complete more tasks. This paper presents a real-time trajectory planning method that can achieve long-distance navigation with less planning number and calculation time while greatly reducing flight time to reach the target point quickly. Our method is built on hard constraints such as safety distance, free-space flight corridors, and smoothness constraints that can ensure trajectory quality. For each scenario, our improved Theta* algorithm can obtain a shortest initial trajectory with several key waypoints. Low-quality segments of the initial trajectory are then screened and optimized by local replanning detection and flight corridor-based optimization, respectively. Flight efficiency, continuity, and dynamical feasibility are greatly boosted by the distributed time allocation method. Experimental results show that the flight time of our method is 22%–56% less than that of the state-of-the-art hard-constrained methods in about 50 m flight, and total calculation time is 19%–84% less, which is attributable to the reduction of planning number. The proposed trajectory planning method is also integrated into a quadrotor platform and its competence is validated by presenting autonomous flight in unknown indoor environments. [ABSTRACT FROM AUTHOR]

Consensus-based bundle algorithm (CBBA) is a decentralized task allocation algorithm that can produce feasible and conflict-free task assignment solution for multi-UAV system in the search and rescue scenarios. Further considering the new emerging tasks, this paper studies how to realize task reasssignment in the time-sensitive and dynamic environment. Effective task replanning algorithm aims to maximize the score value of task replanning solution when ensuring the timely allocation of the new task. Thus, an extension of CBBA called CBBA with local replanning (CBBA-LR) is proposed to produce reliable task replanning solution with quick response to the new task. Firstly, the capable matrix is adopted in CBBA-LR to denote the capable relationship between UAVs and tasks. Only capable UAVs for the new task are included in the task replanning. Then, the performing time list is introduced to the bid lists. For each UAV, CBBA-LR selects the assigned tasks whose performing times overlap the time window of the new task as the potential reset tasks. The setting of potential reset tasks effectively reduces the number of tasks included in the replanning process. After that, each UAV selects the nearest task to the new task from the potential reset task set as the reset task. Hence, CBBA-LR resets the most likely insert position of the new task from each UAV's path. Finally, CBBA runs based on the reset task schedules to get the task replanning solution. Numerical simulations demonstrate the solution quality and convergence time of CBBA-LR from four perspectives: different time windows of the new task, different locations of the new task, continuous appearance of new tasks and different scales of search and rescue scenarios. The simulation results verify the feasibility and superiority of CBBA-LR compared with other replanning strategies. [ABSTRACT FROM AUTHOR]

Most of the existing dynamic RRT path planning algorithms cannot keep the planned path away from obstacles,which may cause the robot to have insufficient time to avoid obstacles.To solve this problem,this paper proposed an improved RRT,denoted as APFG-RRT,which utilized artificial potential fields to guide the RRT grow to goal and away from obstacles.In order to further increase the convergence rate and speed up the jump out of local minima,it introduced a strategy of selecting the goal as the random sample at an adaptive probability.Finally,it adopted global planning combined with local replanning to improve its real-time performance in dynamic environment.Simulation experiments show that APFG-RRT has higher computational efficiency and lower memory requirements compared with the initial RRT and Goal-bias RRT,and the given path can be effectively away from obstacles,which improves the success rate of dynamic path planning. [ABSTRACT FROM AUTHOR]

As an important part of the intelligent planning technology, hierarchical task network (HTN) is successfully applied to the task planning in unmanned aerial vehicle platform, emergency plan formulation and other fields. Due to effect the increasing uncertain factors on the implementation the influence of the plan, it is urgent to use replanning technology, thus the replanning technology based on HTN has become a hotspot in recent years. First of all, a three-layer replan framework based on HTN is proposed by summarizing the current research status. Then, the corresponding technical route and implementation details are represented from three levels of plan repair, local replanning and global replanning in the framework respectively, and the advantages and disadvantages of the current method are pointed out for different levels. Finally, the future research direction of this problem is prospected, and the key problems to be solved and the solutions are putting forward. [ABSTRACT FROM AUTHOR]