基于a&ω六自由度运动模型的船舶运动参数发生器

针对航海模拟器中船舶运动数学模型建立过程过于复杂、仿真的有效性难以验证的问题,结合船舶自身机动特点设计了一种新的船舶运动参数发生器,可生成航海模拟器所需的各船舶运动参数。首先,在响应模型的基础上,建立了基于线加速度和角速度(linear acceleration and angular velocity,a&ω)的船舶运动模型。然后,通过船舶运动微分方程求解了船舶位置、速度、姿态、航向等运动参数信息。最后,提出相似度评估指标并将其应用于基于实测数据的仿真的验证。仿真验证结果表明,船舶运动参数发生器能有效地模拟船舶在多种机动状况下的运动,各船舶运动参数的相似度均在80%以上,是一种有效的航海模拟器的船舶运动模拟仿真研究方法。

基于船舶水动力的六自由度a&ω运动参数发生器

针对传统轨迹发生器生成的轨迹和运动参数不符合实船特性问题,依据分离式结构模型六自由度船舶运动方程,分析不同运动状态的船体、桨、舵的水动力和力矩,推导静水条件下六自由度线加速度和角速度(a&ω)模型;提出以档位固联转速建立纵向线加速度的二阶模型,用二阶系统的阻尼比和自然频率表示档位切换时船舶的加、减速性能;采取极大似然估计的递推算法辨识模型中的未知参数;基于某船的实测数据,验证该运动参数发生器的有效性,并评估仿真准确度。仿真结果表明,该发生器能够生成线加速度、角速度、姿态、速度、位置共计15维运动参数,且各运动参数的准确度在95%以上。

Solid Edge运动仿真的应用

Solid Edge是一种计算机辅助设计软件,主要用于机械装配、零件建模和图纸生成。运动仿真是一种可在Solid Edge装配件上执行运动模拟的最方便快捷的方法。简要介绍了Solid Edge运动仿真的概念,以及Solid Edge运动仿真的各项功能,并结合实例,描述了Solid Edge运动仿真的应用。

Vehicle kinematics modeling and design of vehicle trajectory generator system

A trajectory generator based on vehicle kinematics model was presented and an integrated navigation simulation system was designed.Considering that the tight relation between vehicle motion and topography,a new trajectory generator for vehicle was proposed for more actual simulation.Firstly,a vehicle kinematics model was built based on conversion of attitude vector in different coordinate systems.Then,the principle of common trajectory generators was analyzed.Besides,combining the vehicle kinematics model with the principle of dead reckoning,a new vehicle trajectory generator was presented,which can provide process parameters of carrier anytime and achieve simulation of typical actions of running vehicle.Moreover,IMU(inertial measurement unit) elements were simulated,including accelerometer and gyroscope.After setting up the simulation conditions,the integrated navigation simulation system was verified by final performance test.The result proves the validity and flexibility of this design.

Central pattern generation underlying Limulus rhythmic behavior patterns

Many behavioral activities of the horseshoe crab Limulus are rhythmic, and most of these are produced in large part by central pattern generators within the CNS. The chain of opisthosomal （‘abdominal＇） ganglia controls gill movements of ventilation and gill cleaning, and the prosomal ring of fused ganglia （brain and segmental ‘thoracic＇ ganglia） controls generation of feeding and locomotor movements of the legs. Both the opisthosomal CNS and the prosomal CNS can generate behaviorally ap- propriate patterns of motor output in isolation, without movements or sensory input. Preparations of the isolated opisthosomal CNS generate rhythmic output patterns of motor activity characterized as fictive ventilatory and gill cleaning rhythms. Moreover, CNS preparations also express longer-term patterns, such as intermittent ventilation or sequential bouts of ventilation and gill cleaning. Such longer-term patterns are commonly observed in intact animals. The isolated prosomal CNS does not spontaneously generate the activity patterns characteristic of walking, swimming, and feeding. However, perfusion of octopamine in the isolated prosomal CNS activates central pattern generators underlying rhythmic chewing movements, and injection of octopamine into in- tact Limulus promotes the chewing pattern of feeding, whether or not food is presented. Our understanding of the ability of neu-romodulators such as octopamine to elicit or alter central motor programs may help to clarify the central neural circuits of pattern generation that oroduce and coordinate these rhythmic behaviors

A Real-Time Collision-Free Path Planning of a Rust Removal Robot Using an Improved Neural Network

In this paper, a real-time collision-free path planning of the rust removal robot in a ship environment is proposed, which is based on an improved biologically inspired neural network algorithm. This improved algorithm is based on the biologically inspired neural network and modified with obstacle detection sensors and kinematic state templates, and is implemented in a ship rust removal robot planning system for dynamic trajectory generation. The real-time optimal trajectory is generated by the biologically inspired neural network, and the moving obstacle detection process of a ship robot working on the wall is simulated with the obstacle detection sensors models. The local real-time trajectory can be re-planned by the updated local map information, where the obstacle detection sensors are used to inspect partial environment information and update the robot nearby information in real time in the original neural network algorithm. At the same time, the method of the kinematic state templates matching and searching is used to solve the pipes’ influence of the rust removal robot climbing on the wall, which can not only provide a smooth path, but also can judge the motion direction and turning angle of the robot. Comparison of the proposed approach with the simulation shows that the improved algorithm is capable of planning a real-time collision-free path with achieving the local environmental information and judging the rust removal robot’s motion direction and turning angle. This proposed algorithm can be good used in the ship rust removal robot. © 2017, Shanghai Jiaotong University and Springer-Verlag GmbH Germany.