Modeling and experimental validation of sawing based lander anchoring and sampling methods for asteroid exploration

This paper presents a novel lander anchoring system based on sawing method for asteroid exploration. The system is composed of three robotic arms, three cutting discs, and a control system. The discs mounted at the end of the arms are able to penetrate into the rock surface of asteroids. After the discs cut into the rock surface, the self-locking function of the arms provides forces to fix the lander on the surface. Modeling, trajectory planning, simulations, mechanism design, and prototype fabrication of the anchoring system are discussed, respectively. The performances of the system are tested on different kinds of rocks, at different sawing angles, locations, and speeds. Results show that the system can cut 15?mm deep into granite rock in 180?s at sawing angle of 60°, with the average power of 58.41?W, and the “weight on bit” (WOB) of 8.637?N. The 7.8?kg anchoring system is capable of providing omni-directional anchoring forces, at least 225?N normal and 157?N tangent to the surface of the rock. The system has the advantages of low-weight, low energy consumption and balance forces, high anchoring efficiency and reliability, and could enable the lander to move and sample or assist astronauts and robots in walking and sampling on asteroids.     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.     

基于模板匹配整帧屏幕视频帧内无损编码方法

当前主流的视频压缩标准,如H.264、HEVC等,主要是针对摄像机拍摄的视频而设计的.而屏幕内容(SC)的视频是由计算机生成的,无噪声,要求更高的压缩效率,同时与摄像机拍摄的视频相比包含更多的结构相似性.提出了一种利用屏幕视频中相似性的模板匹配帧内编码方法,采用由21个像素点组成的模板,以像素点为最小单位对整帧进行模板匹配,并使用哈希函数实现高效的模板查找,最后对残差图像进行熵编码.实验结果显示,与标准的HEVC扩展方法相比,该方法在无损编码上压缩率可以提高一倍,编码时间减小了近一半.      

柔性机械臂辅助空间站舱段对接阻抗控制

空间机械臂辅助舱段对接过程中存在测量与控制误差,易导致对接机构间存在较大接触力,传统FMA (Force MomentAccommodation)控制方法在测量接触力时无法消除大负载惯性力对测量的影响,且测量仪器的引入会进一步降低空间柔性机械臂的刚度。为此,文章提出了柔性机械臂辅助大负载空间舱段对接的阻抗控制方法,采用拉格朗日法推导了空间机械臂的关节输入力矩方程作为前馈输入,建立了含动力学前馈的空间机械臂阻抗控制程序,并以在商业软件ADAMS中建立的空间柔性机械臂与对接舱段组成的系统动力学模型作为控制对象,对系统进行ADAMS灢Matlab联合仿真。仿真结果表明,按照此控制方法,系统可克服外力干扰使目标解析点按照期望的方式运动;同时,通过测量机械臂关节运动参数即可实现对外力的准确感知,而不需额外添加力传感器,既消除了大负载惯性力对测量的影响,也不会导致柔性机械臂刚度的降低。     

冗余空间机械臂粗捕获段无碰撞轨迹规划算法

现有的基于C空间的无碰撞轨迹规划算法需要求解C空间以获得C空间障碍边界。对于多自由度的冗余机械臂,求解过程需要消耗很大的计算量和内存,不适用于计算资源紧张的空间机械臂轨迹的快速规划。文章提出了一种不需要求解C空间的试探性规划算法,包含4个子算法：碰撞检测算法、无碰撞目标构型求解算法、无碰撞路径搜索算法和路径平滑算法。已知期望的末端作用器位置和姿态,利用目标构型求解算法得到无碰撞的目标构型,然后由路径搜索算法在C空间障碍边界未知的情况下,利用碰撞检测算法,采用一定的试探规律,在C空间中搜索出一条无碰撞路径,最终由路径平滑算法使该路径平滑,易于实现。仿真算例表明,该算法是快速有效的,适用于冗余空间机械臂粗捕获段的快速轨迹规划。     

Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval

This paper presents an adaptive path planner for unmanned aerial vehicles (UAVs) to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming (BLP) and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles (SAMs) are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.     

对"航空气象"课程教学改革的思考

论述了对“航空气象”课进行教学改革的必要性,根据“航空气象”课教学中存在的问题,就教学内容、教学方法和教学手段等方面提出了改革的建议。     

柔性转子逆回旋振动特性的研究

论述了具有陀螺效应的柔性转子在各向异性支承条件下回旋振动的特性，推导出了转子在正回旋激励作用下产生逆回旋振动的条件，提出转子振动存在逆回旋带的概念，并举例说明了转子在整个转速范围和振动频率范围内回旋振动的特点，作出了逆回旋带的曲线图。     

空间目标高分辨距离像运动参数估计

建立了宽带线性调频雷达信号的空间目标回波模型，对目标回波信号进行了分析，分析了空间目标高速运动对目标高分辨一维距离像的影响，提出了用多项式变换进行参数估计的方法。利用估计参数进行运动补偿，消除目标速度和加速度对目标一维距离像的影响。最后给出了仿真结果，验证了本文的分析结果正确和算法有效。     

物—伞系统运动稳定性初探

弹道－升力式再入飞行器常采用旋成体外形，但其质心偏离对称轴（纵轴）设置。这种飞行器在着陆前要采用降落伞作为主减速手段。研究质心偏离对称轴的旋成体与降落伞组成的系统的运动稳定性是一项有工程实践意义的课题。作为一种初步探索，在把降落伞视作刚体的条件下，按该系统平面运动方程考察其在平衡状态附近的稳定性，给出了运动稳定性判据。