栅格地图中多机器人协作搜索目标

目标搜索是多机器人领域的一个挑战.本文针对栅格地图中多机器人目标搜索算法进行研究.首先,利用Dempster-Shafer证据理论将声纳传感器获取的环境信息进行融合,构建搜索环境的栅格地图.然后,基于栅格地图建立生物启发神经网络用于表示动态的环境.在生物启发神经网络中,目标通过神经元的活性值全局的吸引机器人.同时,障碍物通过神经元活性值局部的排斥机器人,避免与其相撞.最后,机器人根据梯度递减原则自动的规划出搜索路径.仿真和实验结果显示本文提及的算法能够实现栅格地图中静态目标和动态目标的搜索.与其他搜索算法比较,本文所提及的目标搜索算法有更高的效率和适用性.     

基于滚动优化和分散捕食者猎物模型的全覆盖路径规划算法

针对多机器人执行全覆盖任务效果差的问题,提出一种基于滚动优化和分散捕食者猎物模型的多机器人全覆盖路径规划算法.首先,利用栅格地图表示作业的环境空间,并基于栅格地图修正捕食者猎物算法中的避开捕食者奖励,添加移动代价奖励和死区回溯机制构建分散捕食者猎物模型;然后,引入滚动优化方法,避免机器人陷入局部最优,预测周期内机器人覆盖栅格的累计奖励值作为适应度函数,并使用鲸鱼优化算法(WOA)求解最优移动序列;最后,在不同环境下进行仿真实验,得到的平均路径长度与生物激励神经网络算法(BINN)和牛耕式A*算法(BA*)相比分别减少了16.69%sim17.33%、10.32%sim20.03%,验证了所提出算法在多机器人全覆盖路径规划中的可行性和有效性.     

基于生物启发模型的AUV三维自主路径规划与安全避障算法

针对自治水下机器人(AUV)的路径规划问题,在三维栅格地图的基础上,给出一种基于生物启发模型的三维路径规划和安全避障算法. 首先建立三维生物启发神经网络模型,利用此模型表示AUV的三维工作环境,神经网络中的每一个神经元与栅格地图中的位置单元一一对应;然后,根据神经网络中神经元的活性输出值分布情况自主规划AUV的运动路径.静态环境与动态环境下仿真实验结果表明了生物启发模型在AUV三维水下环境中路径规划和安全避障上的有效性.     

移动机器人全覆盖信度函数路径规划算法

针对移动机器人全覆盖路径规划问题,给出一种基于栅格信度函数的全覆盖路径规划算法。目的是为了控制移动机器人能够遍历工作区域中所有的可到达点,同时保证能够自动避开障碍物。首先,根据环境的信息对栅格地图进行赋值,使用不同的函数值表示障碍物、已覆盖栅格和未覆盖栅格;其次,判断机器人是否陷入死区引入不同方向信度函数,对栅格函数值进行调整;最后,机器人根据栅格信度函数值规划覆盖路径。本文所提及的算法不仅能够引导移动机器人实现工作区域的全覆盖而且能够快速逃离死区,实现覆盖路径的低重复率。仿真实验中,通过与生物启发神经网络算法的比较,证明本文提及算法有更高的覆盖效率。     

基于内螺旋搜索的生物激励遍历路径规划算法

针对传统生物激励神经网络遍历路径规划的重复覆盖率高和子区域间路径不是最优的问题,提出了基于内螺旋搜索的生物激励遍历路径规划方法.方法 在未知水下环境信息的情况下通过生物激励神经网络算法完成水下地图环境建模与路径规划,在分割出子区域后通过内螺旋算法占主导完成子区域遍历,避免神经元活性值相同引起重复覆盖,子区域间通过A*算法实现最优路径规划.仿真结果表明,相较原方法,上述方法生成的路径分别在重复覆盖率、运行时间、路径长度指标上均有较大提升.     

海流环境下多AUV多目标生物启发任务分配与路径规划算法

针对多障碍物海流环境下多自治水下机器人(AUV)目标任务分配与路径规划问题, 本文在栅格地图构建的 基础上给出了一种基于生物启发神经网络(BINN)模型的新型自主任务分配与路径规划算法, 并考虑海流对路径规 划的影响. 首先建立BINN模型, 利用此模型表示AUV的工作环境, 神经网络中的每一个神经元与栅格地图中的位 置单元一一对应; 接着, 比较每个目标物在BINN地图中所有AUV的活性值, 并选取活性值最大的AUV作为它的获 胜AUV, 实现多AUV任务分配; 最后, 考虑常值海流影响, 根据矢量合成算法确定AUV实际的航行方向, 实现AUV路 径规划与安全避障. 海流环境下仿真实验结果表明了生物启发模型在多AUV水下任务分配与路径规划中的有效性.     

自治水下机器人的自主启发式生物启发神经网络路径规划算法

针对复杂海流环境下自治水下机器人(autonomous underwater vehicle, AUV)的路径规划问题,本文在栅格地图的基础上给出了一种基于离散的生物启发神经网络(Glasius bio-inspired neural networks, GBNN)模型的新型自主启发式路径规划和安全避障算法,并考虑海流对路径规划的影响.首先建立GBNN模型,利用此模型表示AUV的工作环境,神经网络中的每一个神经元与栅格地图中的位置单元一一对应;其次,根据神经网络中神经元的活性输出值分布情况并结合方向信度算法实现自主规划AUV的运动路径;最后根据矢量合成算法确定AUV实际的航行方向.障碍物环境和海流环境下仿真实验结果表明了生物启发模型在AUV水下环境中路径规划的有效性.     

基于免疫自适应遗传算法的机器人栅格地图融合

融合各机器人独自创建的环境地图,实现信息共享,是提高分布式多移动机器人系统环境探索效率的关键.研究了在没有公共参考坐标系及机器人相对位置信息未知情况下的栅格地图融合问题,提出了一种基十免疫自适应遗传算法的栅格地图融合方法,该算法把反映两个栅格地图重叠区域相异程度的优化函数作为抗原,每个可能的平移、旋转平面转换对应一个抗体.仿真结果表明了该算法可以较快的收敛速度和较强的全局搜索能力,搜索到两个栅格地图的最佳重叠区域,实现地图融合.     

基于改进生物启发神经网络的机器人目标搜索方法

针对未知环境下机器人目标搜索的问题,按照机器人能力不同对搜索区域进行划分,目标点在自己运动的过程中会在局部范围内留下信息素并且这些信息素会随着时间的流失而减少,机器人可以探测到这些信息素的多少进而影响机器人下一个搜索位置的选择。本文采用改进生物启发神经网络选取机器人探索范围内活性值最大的点作为下一个搜索位置。为了防止在连续的时间段内多次选择相同的点,引入禁忌搜索,把多次选择相同的点放入禁忌表中,可以有效防止陷入局部最优点。与随机搜索方式和原始的生物启发神经网络进行对比,验证了该方法对动态目标的搜索具有良好的效果。     

一个室内清洁机器人的区域遍历与地图绘制

本文针对室内清洁机器人的区域遍历问题，提出了一种地图绘制的方法：该方法基于栅格地图表示环境，对环境地图进行分区，通过自由区域联结图广度优先搜索及深度优先遍历（WSDC）形成方形区域遍历顺序，对机器人探测环境的路线进行导航。实验研究表明栅格地图表示环境对于区域划分与区域遍历更有效：采用WSDC算法产生的区域遍历顺序导航机器人运动，可以减少机器人的运动距离，提高工作效率。     

Hybrid approach to implement multi-robotic navigation system using neural network,fuzzy logic,and bio-inspired optimization methodologies

Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi-robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta-heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi-robotics to travel from the initial point to such goal. The fundamental purpose of portable multi-robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio-inspired methods that are exploited to optimize hybrid neuro-fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the particle swarm optimization technique in real-time scenarios. Several optimization approaches of bio-inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi-robot navigation accuracy in terms of navigation duration and length of the path.     

未知环境下多机器人搜捕策略研究

针对在未知环境下多机器人围捕入侵者所存在的问题,提出了基于occupancy grid方法构造并合成环境地图指导单个机器人以分散搜索、抛物线模型预测并追踪入侵者、以及多机器人基于leader的可重构队形结构进行围捕的策略,使未知环境的地图构造和对入侵者追踪搜索过程得以同步完成,降低了机器人团队对环境的依赖,对未知环境具有较高的适应能力.最后通过仿真实验验证了该策略的正确性、有效性和鲁棒性.     

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh , 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.      

基于Netvlad神经网络的室内机器人全局重定位方法

针对已知地图的室内机器人全局重定位、绑架恢复问题,提出一种基于改进的Netvlad卷积神经网络的室内机器人全局重定位方法,通过激光雷达获取的障碍物信息引导机器人到达空旷区域,粗定位阶段,使用栅格地图最短连通域距离作为正样本判据,并对Netvlad引入残差网络,通过图像检索得到机器人的粗略位置及角度信息。使用粗定位阶段得到的位置和角度信息作为自适应蒙特卡罗定位的初始值来估计机器人的精确位姿。实验结果表明,与传统定位方法相比,该方法可以使机器人从绑架问题中快速恢复准确位姿。     

基于生物刺激神经网络的多机器人编队方法

多机器人编队控制是多机器人协作领域的重要研究内容之一,如何实现多机器人朝同一目标移动的同时保持队形是多机器人编队的一个热点和难点问题。针对这一问题,提出一种新的基于生物刺激神经网络的多机器人动态编队方法,采用基于leader-referenced编队模型实时计算各机器人的虚拟目标位置,利用生物刺激神经网络进行机器人导航。最后进行仿真实验,实验结果表明该方法在实现多机器人实时避障并保持队形的同时,朝同一目标移动,而且可以很快实现队形变换,具有较好的实时性和灵活性。     

Underwater autonomous motion control of a small-scaled spherical robot with neural networks

Considering the complex and variability of the operating environment of underwater spherical robot, usually it is difficult to solve the control problem when the robot changes its motion state or it is subject to waves and ocean currents, in those cases wherein robots are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. In this paper, with the goal of supporting some autonomous tasks of our small-scaled spherical robot, such as ecological observations and intelligent surveillance, a neural network-based auto-tuning control system was designed and implemented, which has a great advantage of processing online for the robot due to their nonlinear dynamics. The neural network plays the role of automatically estimating the suitable set of control gains that achieves the stability of the system. Simulation results are presented for the underwater swimming, in terms of the motion performance, stability, and velocity of the robot. Finally, the effectiveness of the proposed method was demonstrated by showing that the underwater horizontal and desired triangular trajectory motion were stable, and the design presented in this paper is able to meet future demands of underwater robots in biological monitoring and multi-robot cooperation.

     

Real-time exploration of a multi-robot rescue system in disaster areas

A system procedure is proposed for a multi-robot rescue system that performs real-time exploration over disaster areas. Real-time exploration means that every robot exploring the area always has a communication path to human operators standing by at a base station and that the communication path is configured by ad hoc wireless networking. Real-time exploration is essential in multi-robot systems for USAR (urban search and rescue) because operators must communicate with every robot to support the victim detection process and ad hoc networking is suitable to configure a communication path among obstacles. The proposed system procedure consists of the autonomous classification of robots into search and relay types and behavior algorithms for each class of robot. Search robots explore the areas and relay robots act as relay terminals between search robots and the base station. The rule of the classification and the behavior algorithm refer to the forwarding table of each robot constructed for ad hoc networking. The table construction is based on DSDV (destination-sequenced distance vector) routing that informs each robot of its topological position in the network and other essentials. Computer simulations are executed with a specific exploration strategy of search robots. The results show that a multi-robot rescue system can perform real-time exploration with the proposed system procedure and reduce exploration time in comparison with the case where the proposed scheme is not adopted.