Adaptive genetic algorithm for path planning of loosely coordinated multi-robot manipulators

Adaptive genetic algorithm A SA GA, a novel algorithm, which can dynamically modify the parameters of Genetic Algorithms in terms of simulated annealing mechanism, is proposed for path planning of loosely coordinated multi robot manipulators. Over the task space of a multi robot, a strategy of decoupled planning is also applied to the evolutionary process, which enables a multi robot to avoid falling into deadlock and calculating of composite C space. Finally, two representative tests are given to validate A SA GA and the strategy of decoupled planning.

NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

Neural network and genetic algorithm based global path planning in a static environment

Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.

Robot path planning using genetic algorithms

Presents a strategy for soccer robot path planning using genetic algorithms for which, real number coding method is used, to overcome the defects of binary coding method, and the double crossover operation adopted, to avoid the common defect of early convergence and converge faster than the standard genetic algorithms concludes from simulation results that the method is effective for robot path planning.

Global optimal path planning for mobile robot based onimproved Dijkstra algorithm and ant system algorithm

A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.

Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots

为活动机器人计划的即时全球性最佳的路径的一个新奇方法基于蚂蚁殖民地系统(交流) 被建议算法。这个方法包括三步：第一步正在利用 MAKLINK 图理论建立活动机器人的空间模型，第二步正在利用 Dijkstra 算法发现一条非最优的没有碰撞的路径，并且第三步正在利用 ACS 算法优化非最优的路径的地点以便产生全球性最佳的路径。建议方法是有效的并且能在即时路径被使用活动机器人计划的计算机模拟实验表演的结果。建议方法比与优秀人材模型一起基于基因算法计划方法的路径处于集中速度，答案变化，动态集中行为，和计算效率有更好的性能，这被验证了。

Research on Model and Algorithm of Task Allocation and Path Planning for Multi-Robot

Based on the modeling of robot working environment, the shortest distance matrix between points is solved by Floyd algorithm. With the objective of minimizing the sum of the fixed cost of robot and the cost of robot operation, an integer programming model is established and a genetic algorithm for solving the model is designed. In order to make coordination to accomplish their respective tasks for each robot with high efficiency, this paper uses natural number encoding way. The objective function is based on penalty term constructed with the total number of collisions in the running path of robots. The fitness function is constructed by using the objective function with penalty term. Based on elitist retention strategy, a genetic algorithm with collision detection is designed. Using this algorithm for task allocation and path planning of multi-robot, it can effectively avoid or reduce the number of collisions in the process of multi-robot performing tasks. Finally, an example is used to validate the method.

A Rough Set GA-based Hybrid Method for Robot Path Planning

In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.

Non-smooth environment modeling and global path planning for mobile robots

An Approximate Voronoi Boundary Network is constructed as the environmental model by way of enlar-ging the obstacle raster. The connectivity of the path network under complex environment is ensured through build-ing the second order Approximate Voronoi Boundary Network after adding virtual obstacles at joint-close grids. Thismethod embodies the network structure of the free area of environment with less nodes, so the complexity of pathplanning problem is reduced largely. An optimized path for mobile robot under complex environment is obtainedthrough the Genetic Algorithm based on the elitist rule and re-optimized by using the path-tightening method. Sincethe elitist one has the only authority of crossover, the management of one group becomes simple, which makes forobtaining the optimized path quickly. The Approximate Voronoi Boundary Network has a good tolerance to the im-precise a priori information and the noises of sensors under complex environment. Especially it is robust in dealingwith the local or partial changes, so a small quantity of dynamic obstacles is difficult to alter the overall character ofits connectivity, which means that it can also be adopted in dynamic environment by fusing the local path planning.

Using genetic/simulated annealing algorithm to solve disassembly sequence planning

Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.

基于改进Maklink图的多点遍历路径规划算法

针对无人设备在执行如定点巡逻任务和货物搬运作业时,需要解决多点遍历和避障路径规划的问题,开展了一种基于改进Maklink图的全局路径规划算法研究,优化避障作业路径。首先,建立改进Maklink图作为空间模型,简化作业空间;随后,设计一种改进蚁群算法在上述空间模型内规划路径,算法采用针对多点遍历的启发式函数、禁忌搜索、局部最优处理机制和精英个体保留与额外进化策略;最后,对比实验证明了所提算法在不同任务环境下的有效性,具有更强的寻优能力和更好的稳定性,在10个案例中,相对基于概率路线图的算法和传统蚁群算法,路径长度优化提升最大分别可达21.13%和49.31%,稳定性提升最大分别可达99.56%和99.82%。

Novel obstacle-avoiding path planning for crop protection UAV using optimized Dubins curve

In recent years,the crop protection unmanned aerial vehicle(UAV)has been raised great attention around the world due to the advantages of more efficient operation and lower requirement of special landing airport.However,there are few researches on obstacle-avoiding path planning for crop protection UAV.In this study,an improved Dubins curve algorithm was proposed for path planning with multiple obstacle constraints.First,according to the flight parameters of UAV and the types of obstacles in the field,the obstacle circle model and the small obstacle model were established.Second,after selecting the appropriate Dubins curve to generate the obstacle-avoiding path for multiple obstacles,the genetic algorithm(GA)was used to search the optimal obstacle-avoiding path.Third,for turning in the path planning,a strategy considering the size of the spray width and the UAV’s minimum turning radius was presented,which could decrease the speed change times.The results showed that the proposed algorithm can decrease the area of overlap and skip to 205.1%,while the path length increased by only 1.6%in comparison with the traditional Dubins obstacle-avoiding algorithm under the same conditions.With the increase of obstacle radius,the area of overlap and skip reduced effectively with no significant increase in path length.Therefore,the algorithm can efficiently improve the validity of path planning with multiple obstacle constraints and ensure the safety of flight.

多搬运任务下考虑碰撞避免的AGV路径规划

为提升自动导引小车在“货到人”仓库中的运行效率,针对AGV-托盘任务分配、单AGV路径规划及多AGV碰撞避免三个子问题的研究,以最小化AGV行驶距离为目标构建数学模型。首先,根据AGV与托盘的双边匹配问题特点设计改进的匈牙利算法求解匹配结果。其次,提出一种二维编码机制的改进遗传算法(improved genetic algorithm,IGA),采用一种局部搜索算子代替原变异操作,在提高算法搜索性能的基础上使其成功应用于单AGV路径规划问题。然后,利用时空数据设计一种三维网格冲突检测方法,并根据商品SKU数量设定AGV的优先级以降低多AGV执行任务时的碰撞概率。最后,在32 m×22 m的仓库中针对不考虑碰撞与考虑碰撞两种情形进行AGV路径优化分析,给出合理的行驶距离和碰撞次数。IGA与标准遗传算法的对比结果显示,IGA能够在合理的时间内获得更高质量的解,行驶距离减少约1.74%,算法求解时间缩短约37.07%。此外,针对AGV数量灵敏度分析,在不同目标托盘规模下测试不同数量的AGV对行驶距离和碰撞次数的影响,发现14~16台AGV数量是最佳配置,验证了模型的可行性和算法的有效性。

改进蚁群算法的送餐机器人路径规划

蚁群算法拥有良好的全局性、自组织性、鲁棒性,但传统蚁群算法存在许多不足之处。为此,针对算法在路径规划问题中的缺陷,在传统蚁群算法的状态转移公式中,引入目标点距离因素和引导素,加快算法收敛性和改善局部最优缺陷。在带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)上,融合蚁群算法和遗传算法,并将顾客时间窗宽度以及机器人等待时间加入蚁群算法状态转移公式中,以及将蚁群算法的解作为遗传算法的初始种群,提高遗传算法的初始解质量,然后进行编码,设置违反时间窗约束和载重量的惩罚函数和适应度函数,在传统遗传算法的交叉、变异操作后加入了破坏-修复基因的操作来优化每一代新解的质量,在Solomon Benchmark算例上进行仿真,对比算法改进前后的最优解,验证算法可行性。最后在餐厅送餐问题中把带有障碍物的仿真环境路径规划问题和VRPTW问题结合,使用改进后的算法解决餐厅环境下送餐机器人对顾客服务配送问题。

改进Q-Learning的路径规划算法研究

针对Q-Learning算法学习效率低、收敛速度慢且在动态障碍物的环境下路径规划效果不佳的问题,本文提出一种改进Q-Learning的移动机器人路径规划算法.针对该问题,算法根据概率的突变性引入探索因子来平衡探索和利用以加快学习效率;通过在更新函数中设计深度学习因子以保证算法探索概率;融合遗传算法,避免陷入局部路径最优同时按阶段探索最优迭代步长次数,以减少动态地图探索重复率;最后提取输出的最优路径关键节点采用贝塞尔曲线进行平滑处理,进一步保证路径平滑度和可行性.实验通过栅格法构建地图,对比实验结果表明,改进后的算法效率相较于传统算法在迭代次数和路径上均有较大优化,且能够较好的实现动态地图下的路径规划,进一步验证所提方法的有效性和实用性.

基于视觉算法的采摘机器人控制优化研究

为了优化采摘机器人控制,基于视觉技术设计了控制系统。首先,在大津分割法的基础上,计算图像中的角点,将角点从分割后的二值图中去除,降低背景干扰点簇的影响;其次,建立褐菇几何特征参数,以此参数对褐菇进行特征图像提取;再次,采用遗传学算法,对褐菇采摘路径进行优化,与传统往复前进式路径规划相比路程缩短了30.5%;最后,对系统进行实地褐菇采摘测试,结果显示:辨别成功率分布区间为[87%,91.7%],采摘成功率分布区间为[80%,85.75%],单个褐菇平均采摘时间分布区间为[12.4s,13.2s],系统具有良好的采摘性能。

基于分区进化遗传算法的无人船自导航规划研究

为解决无人船中的自导航路径规划问题,先对无人船的任务进行分层研究,再对其中的路径规划层进行分析。在完成航行环境的栅格化处理后,采用分区进化遗传算法(PEGA)对航行路径进行适应度值选取和交叉变异处理,以选出最优航行路径。实验结果表明,PEGA算法具有稳定的寻优性能,在迭代至30代时达到稳定状态,且稳定适应度值为0.58,并且该算法还能通过不同的目标需求完成不同的最优路线规划。

客户分级优先的即时配送路径规划方法

为了使即时配送企业能以较低的成本提高配送准时性,从而维护并发展高价值客户,首先,针对即时配送客户的特点改进RFM模型,基于已有数据使用DBSCAN算法进行客户聚类,根据聚类结果使用GBDT算法构建客户分级预测模型对即时配送客户进行分级预测。在此基础上,以即时配送的固定成本、变动成本及客户超时点种类、数量为优化目标,构建基于客户分级优先的即时配送路径优化模型,再设计遗传算法对该模型进行求解。最后,以沈阳市某一站式冷链即时配送企业为对象进行实例分析。结果显示,相比该企业原配送方案,应用客户分级优先的即时配送路径规划方法规划后的方案在配送总成本仅提高4.8%的情况下,高价值、潜在高价值客户超时点数量由6减少为2,且超时点均为边缘客户,同时配送总时间减少了7.3%,验证了该方法的有效性。采用该配送路径规划方法,企业的配送成本虽然会小幅增加,但因配送准时性提升,可以更好地维护高价值客户,同时发展潜在高价值客户向高价值客户转变,进而保持或提高长期收益。

改进遗传算法的移动机器人避障路径规划

为解决复杂多变环境下,常规路径规划算法生成路径长、不连贯、存在多余转折点和大转折角度、容易碰撞障碍物等问题,提出一种改进遗传算法的动态避障算法.结合行间随机选择策略和A*算法插入中间点策略改进初始化算法;在传统遗传算法中设置障碍物安全距离,引入删除和优化算子,优化适应度函数;采用动态窗口法结合改进遗传算法得到路径最优解.结果表明:改进后算法生成的路径平滑,与A*算法、某改进遗传算法相比,路径更短、碰撞次数更少,在复杂动态环境下具有良好的避障性能,为移动机器人在现实场景中的安全、高效导航提供了可行的解决方案.