Investigation and Validation of Mission Evaluation Models for Space Close-Range Inspection

Space close-range inspection can be used to carry out close-range observation and monitoring of targets for identifying the target’s types and working states, which is of great significance for space missions such as in-orbit services. The effectiveness evaluation of space inspection tasks will significantly affect the studies on the trajectory design, orbit motion control, and task termination conditions. However, the evaluation models in previous studies are too simple such as that they are usually without considering dynamic changes in the satellite orbit relative motion. Besides, these studies fail to build a comprehensive evaluation model for the whole inspection task process. In this paper, taking the most commonly used optical inspection as an example, the novel multifactor inspection task effectiveness evaluation models were investigated, including the constraint models of observation, the relative distance evaluation model, the effective observation time evaluation model, and the target observation angle evaluation model. These models solve the effectiveness evaluation problem for the complete process of an inspection task, which can support the design of inspection strategies and trajectories better by using the evaluation results. In addition, numerical simulations and 20 semiphysical experiments were carried out to validate the proposed evaluation models

Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness

Minimum-Effort Waypoint-Following Differential Geometric Guidance Law Design for Endo-Atmospheric Flight Vehicles

To improve the autonomous flight capability of endo-atmospheric flight vehicles, such as cruise missiles, drones, and other small, low-cost unmanned aerial vehicles (UAVs), a novel minimum-effort waypoint-following differential geometric guidance law (MEWFDGGL) is proposed in this paper. Using the classical differential geometry curve theory, the optimal guidance problem of endo-atmospheric flight vehicles is transformed into an optimal space curve design problem, where the guidance command is the curvature. On the one hand, the change in speed of the flight vehicle is decoupled from the guidance problem. In this way, the widely adopted constant speed hypothesis in the process of designing the guidance law is eliminated, and, hence, the performance of the proposed MEWFDGGL is not influenced by the varying speed of the flight vehicle. On the other hand, considering the onboard computational burden, a suboptimal form of the MEWFDGGL is proposed to solve the problem, where both the complexity and the computational burden of the guidance law dramatically increase as the number of waypoints increases. The theoretical analysis demonstrates that both the original MEWFDGGL and its suboptimal form can be applied to general waypoint-following tasks with an arbitrary number of waypoints. Finally, the superiority and effectiveness of the proposed MEWFDGGL are verified by a numerical simulation and flight experiments

Capturability of 3D RTPN guidance law against true-arbitrarily maneuvering target with maneuverability limitation

The capturability of the Three-Dimensional (3D) Realistic True Proportional Navigation (RTPN) guidance law is thoroughly analyzed. The true-arbitrarily maneuvering target is considered, which maneuvers along an arbitrary direction in 3D space with an arbitrary but upper-bounded acceleration. The whole nonlinear relative kinematics between the interceptor and target is taken into account. First, the upper-bound of commanded acceleration of 3D RTPN is deduced, using a novel Lyapunov-like approach. Second, the reasonable selection range of navigation gain of 3D RTPN is analyzed, when the maneuver limitation of interceptor is considered. After that, a more realistic definition of capture is adopted, i.e., the relative range is smaller than an acceptable miss-distance while the approaching speed is larger than a required impact speed. Unlike previous researches which present Two-Dimensional (2D) capture regions, the inequality analysis technique is utilized to obtain the 3D capture region, where the three coordinates are the closing speed, transversal relative speed, and relative range. The obtained capture region could be taken as a sufficient-but-unnecessary condition of capture. The new theoretical findings are all given in explicit expressions and are more general than previous results

Biotransformation of HSP into 2,5-DHP by HSP hydroxylase from <i>A. tumefaciens</i> S33.

<p>Effects of temperature (a) and pH (b) on the enzymatic formation of 2,5-DHP (<i>squares</i>) from HSP (<i>circles</i>). (a) The reactions were carried out in 50 mM sodium phosphate (pH 8.0) at the temperature indicated. (b) The reactions were performed at 35°C in sodium phosphate buffer at pH indicated. (c) The reaction was performed in 50 mM sodium phosphate (pH 8.0) at 35°C. The values are means of three replicates, and the error bars indicate the standard deviations.</p