空间机械臂非完整运动规划的遗传算法研究

带空间机械臂航天器系统在无外力矩作用时，系统相对于总质心的动量矩守恒而变为非完整系统。由于非完整约束的不可积性，非完整系统的运动规划与控制比一般系统要困难得多。现利用非完整特性研究了自由漂浮空间机械臂的三维姿态运动控制问题。首先导出带空间机械臂的航天器三维姿态运动数学模型，并将系统的控制问题转化为无漂移系统的非完整运动规划问题。在运动规划中，根据最优控制原理和优化理论，提出基于遗传算法的最优运动规划数值算法。通过数值仿真，表明该方法对空间机械臂及航天器三维姿态运动的非完整运动规划是有效的。     

Motion synchronization in a dual redundant HA/EHA system by using a hybrid integrated intelligent control design

This paper presents an integrated fuzzy controller design approach to synchronize a dis-similar redundant actuation system of a hydraulic actuator (HA) and an electro-hydrostatic actu-ator (EHA) with system uncertainties and disturbances. The motion synchronous control system consists of a trajectory generator, an individual position controller for each actuator, and a fuzzy force tracking controller (FFTC) for both actuators. The trajectory generator provides the desired motion dynamics and designing parameters of the trajectory which are taken according to the dynamic characteristics of the EHA. The position controller consists of a feed-forward controller and a fuzzy position tracking controller (FPTC) and acts as a decoupled controller, improving posi-tion tracking performance with the help of the feed-forward controller and the FPTC. The FFTC acts as a coupled controller and takes into account the inherent coupling effect. The simulation results show that the proposed controller not only eliminates initial force fighting by synchronizing the two actuators, but also improves disturbance rejection performance.     

Vision-based bio-inspired guidance law for small aerial vehicle

During predation, a flying insect can form a stealth flight path. This behavior is called motion camouflage. Based on the study results of this behavior, the perception and neurology of flying insects, a novel bio-inspired guidance law is proposed for the terminal guidance for small aerial vehicle with charge-coupled device imaging seekers. The kinematics relationship between a small aerial vehicle and target is analyzed, and a two-dimensional guidance law model is established by using artificial neural networks. To compare with the proportional guidance law, the numerical simulations are carried out in the vertical plane and in the horizontal plane respectively. The simulation results show that the ballistic of the small aerial vehicle is straighter and the normal acceleration is smaller by using the bio-inspired guidance law than by using the proportional guidance law. That is to say, the bio-inspired guidance law just uses the information of the target from the imaging seeker,but the performance of it can be better than that of the proportional guidance law.     

Solar sailing in realistic mode,based on a locally-optimal control laws

Ballistic design of solar sailing missions in the solar system is composed of defining the design parameters, the control programs, and the trajectories that provide performance goals of a flight. The use of a solar sail spacecraft imposes specific restrictions on mission parameters that include the degradation limit on the flight duration, the maximum temperature of solar sail's surface, the minimum distance from the Sun, the maximum angular velocity of the spacecraft's rotation and others.Many authors considered the impact of these restrictions on the design of the mission separately, but they used a sophisticated method of finding the exact optimal motion control or applied the most straightforward laws of motion control. This paper uses local-optimal control laws at the complete mathematical models of motion and functioning of solar sail spacecraft to describe a technique of designing interplanetary missions. The described method avoids the need to obtain an accurate optimal solution to the control problem and does not cause significant computational difficulties.     

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.     

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.     

跨介质航行器波浪环境入水流场演变和运动特性研究

跨介质航行器是一种既可以在空中飞行又可以在水下潜航的新概念航行器,基于仿生学原理,提出一种通过改变外形实现水空介质跨越的航行器模型,通过入水试验装置和计算流体动力学方法,对航行器带攻角从空气到水的介质跨越过程进行了试验和数值仿真研究,得到了跨介质入水过程航行器的运动姿态和入水空泡形态,并通过数值仿真得到了航行器的升力、阻力、速度和加速度演化规律。同时基于数值模拟方法对有波浪情况和静水情况下航行器入水过程空泡演变以及运动特性进行对比。结果表明:提出的航行器构型在水中具有较好的姿态调整能力,波浪的有无和波高的不同都会对航行体入水运动特性造成影响。