基于改进RRT算法的机械臂路径规划

针对快速探索随机树(RRT)路径规划算法缺乏导向性和规划空间增大时算法时间复杂度高的问题,提出一种目标概率偏置与步长控制的改进RRT算法(I-RRT)。I-RRT结合目标概率偏置,以一定概率使采样点偏置为目标点,提高路径规划的导向性,并引入步长控制优化算法,提高运算效率,优化路径。在MATLAB平台建立了算法的仿真实验,结果表明:I-RRT的导向性与算法时间复杂度均优于经典的RRT算法;并在ROS平台上搭建了六自由度机械臂的避障规划与控制实验,实验验证了该算法的有效性。     

Collision-free path planning of cable-driven parallel robots in cluttered environments

Intelligent Service Robotics - Path planning of cable-driven parallel robots (CDPRs) is a challenging task due to cables which may cause various collisions. In this paper, three steps are suggested...     

改进RRT算法陷阱空间下的无人机航迹规划

针对大部分航迹规划算法在陷阱空间下,存在规划时间长、成功率低的问题,提出了一种改进RRT算法。通过将人与RRT算法相结合,由人设置虚拟目标点,引导航迹搜索走出陷阱空间;同时对节点扩展进行优化,保证航迹搜索在可行域内;并设置快速收敛策略,删除冗余节点,使航迹搜索速度加快。最后,通过仿真验证表明,该方法在陷阱空间规划中具有良好的效果,可快速规划可行航迹。     

基于对比优化的RRT路径规划改进算法

针对动态环境下机器人RRT路径规划算法缺乏稳定性和偏离最优解的问题,提出一种基于对比优化的RRT路径规划改进算法。算法在新一周期的环境下,通过对上一周期路径树进行剪枝和重新规划得到一条稳定的路径,同时利用基本RRT算法规划出一条新路径,通过对比两条路径得到较优解。仿真和真实机器人实验结果均表明,改进的算法提高了动态复杂环境下RRT路径规划的稳定性,并保证了规划的路径逼近最优解。     

A real-time path planning algorithm for cable-driven parallel robots in dynamic environment based on artificial potential guided RRT

Microsystem Technologies - This paper deals with the collision-free path planning of cable-driven parallel robots (CDPRs) in a dynamic three-dimensional environment. The proposed algorithm is based...     

赛场环境实时变化的帆船比赛最优路径规划

最优行驶路径规划是帆船比赛取胜的关键环节。文章提出了赛场环境参数实时变化时,基于模糊综合评价的帆船直航比赛最优路径规划方法。该方法利用模糊综合评价思想,综合考虑行驶速度和接近目标两个因素建立综合评价函数进行航行方向决策,赛场环境参数变化时,从当前航行位置相对目标点重新进行路径规划,保证局部状态始终最优,利用宽度优先搜索算法实现全局最优路径搜索。仿真结果证明,该路径规划方法能够取得较好的规划结果,对指导帆船运动员进行科学训练有很好的理论意义和应用价值。     

改进A*算法的移动机器人最短路径规划

针对复杂室内环境下移动机器人路径规划存在实时性差的问题,通过对Dijkstra算法、传统A^*算法以及一些改进的A^*算法的分析比较,提出了对A^*算法的进一步改进的思路。首先对当前节点及其父节点的估计路径代价进行指数衰减的方式加权,使得A^*算法在离目标点较远时能够很快地向目标点靠近,在距目标点较近时能够局部细致搜索保证目标点附近障碍物较多时目标可达;然后对生成的路径进行五次多项式平滑处理,使得路径进一步缩短且便于机器人控制。仿真结果表明,改进算法较传统A^*算法时间减少93.8%,路径长度缩短17.6%、无90°转折点,使得机器人可以连续不停顿地跟踪所规划路径到达目标。在不同的场景下,对所提算法进行验证,结果表明所提算法能够适应不同的环境且有很好的实时性。     

越野环境建模与动态路径规划

针对越野环境下移动机器人的导航与控制问题，提出了一种越野环境下环境建模与动态路径规划方法。该方法能够针对地形的数字高程模型，在综合考虑机器人的性能约束和地形特征等因素的基础上有效地实现越野环境的建模；在此基础上的路径规划采用全局信息与局部信息、前期规划结果与当前规划相结合的方法，满足了越野环境下动态路径规划的要求。实验结果表明，该方法能够很好地适应各种复杂的越野环境。     

Optimal path planning including forces at the gripper

In recent years, a topological method has been established by several authors for the evaluation of time-optimal trajectories for manipulators with tree-like structures and without forces at the gripper, i.e. for free trajectories. In the following, we extend this method to the case of an arbitrary force vector at the robot's hand, which corresponds to some path in contact with a surface. Optimizing the actuator torques in such a way might be seen as a first step to controlling trajectories with regard to position, orientation, and forces in the presence of elasticity. Results of the method confirm the basic ideas of the topological solution.     

一种未知环境下的快速路径规划方法*

为提高机器人在未知环境中的快速路径规划能力,引入自由路径表征可以通过的自由空间,引入风险函数评价机器人切入自由路径过程中发生碰撞的风险。通过搜索最优自由路径、评价碰撞风险压缩表示环境信息,使得未知环境中利用模糊控制器进行局部路径规划的实时性大为提高。与虚拟势场法等传统方法相比,其无局部最小,且极大缓解了狭窄环境中的振荡现象。实验及仿真均表明该方法实时性好、规划所得路径优于已有方法。     

帆船比赛航行路径优化与仿真

针对帆船在海上行驶所具有的模糊性和不定性的复杂环境特点,提出基于分区段优化和进化规划的帆船航行路径优化方法。该方法在比赛区域建立二维平面坐标系,将整个航程划分为若干区段,每个区段以帆船无约束的航向决策综合评价函数为基础,利用进化规划全局优化搜索进行路径优化,综合得到全航程最优路径。在此理论基础上,进行优化路径的仿真。仿真结果表明,该路径优化方法能够取得较好的规划结果,对于帆船科学训练具有很好的指导作用。     

动态环境下基于概率模型检测的路径规划方法

提出了一种动态复杂环境下采用概率模型检测技术进行路径规划的新方法。考虑到实际应用中机器人其移动行为总是受到外界因素的影响,将机器人移动行为看作一个不确定事件,提取环境中的影响因素,构建马尔可夫决策过程模型。采用时态逻辑语言描述机器人目标任务,表达复杂多样的需求行为。运用工具PRISM验证属性,得到满足任务需求的全局优化路径。另外,在全局路径的基础上提出了一种动态避障策略,实现避障局部规划的同时尽量保证机器人最大概率完成任务。通过理论和仿真实验结果证明该方法的正确性和有效性。     

Global path planning methods of UUV in coastal environment

In coastal environment,the motion of unmanned underwater vehicle(UUV)is influenced significantly by complex current.The operational performance of UUV can be greatly improved when the impact of ocean current is considered.A global path planning method of the static obstacle environmental space is addressed in the paper.Firstly,according to the typically coastal vortex,a model of ocean current is proposed and the influence to the motion of UUV is analyzed.Secondly,to satisfy the rapid requirement in path planning,a heuristic A*algorithm is used to design global planning path with multiple constraints.Besides,to meet the UUV’s smooth path requirement,Bezier curve theory is applied.Simulation experiments are performed to illustrate the feasibility of the algorithm in the steady current and vortex environment.     

未知环境下单目视觉移动机器人路径规划

针对室内未知环境下的避障和局部路径规划,提出了一种单目移动机器人路径规划算法,该算法通过对环境图像的自适应阈值分割,获取障碍物与地面交线轮廓点集。通过对现有几种单目测距方法的分析比较,提出一种改进的空间几何约束单目视觉测距计算方法,并依据单目测距的几何关系建立了图像坐标系与机器人坐标系的映射,绘建了一定比例的局部地图。在局部地图上通过改进的人工势场算法为机器人规划路径,改进的人工势场算法解决了传统算法目标点不可到达的问题。通过MATLAB进行仿真实验,结果表明该方法可以规划出有效合理的路径。     

Manoeuvring multitarget tracking method in cluttered environment

A method for tracking a manoeuvring multitarget in a cluttered environment is presented. The clutter or false alarms are assumed to occur uniformly and to be independently distributed. The algorithm is performed by taking a sliding window of length uT (T is the sampling time) at time K. Instead of solving a large problem, the entire set of targets and measurements is divided into clusters so that a number of smaller problems are solved independently. When a set of measurements is received, we form a new data-association hypothesis for the set of measurements lying in the validation gales; with each cluster from time K — u + 1 to K the probability of each track history is computed, and ihen by choosing the largest of these histories we perform the target measurement updated with the adaptive state esiimator. Meanwhile, the covariance-matching technique is adopted so that the accuracy of the adaptive state estimator will be improved. Simulation has shown the effectiveness of the tracking algorithm.     

Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment

Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.     

Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness

Path Planning is a classical problem in the field of robotics. The problem is to find a path of the robot given the various obstacles. The problem has attracted the attention of numerous researchers due to the associated complexities, uncertainties and real time nature. In this paper we propose a new algorithm for solving the problem of path planning in a static environment. The algorithm makes use of an algorithm developed earlier by the authors called Multi-Neuron Heuristic Search (MNHS). This algorithm is a modified A^? algorithm that performs better than normal A^? when heuristics are prone to sharp changes. This algorithm has been implemented in a hierarchical manner, where each generation of the algorithm gives a more detailed path that has a higher reaching probability. The map used for this purpose is based on a probabilistic approach where we measure the probability of collision with obstacle while traveling inside the cell. As we decompose the cells, the cell size reduces and the probability starts to touch 0 or 1 depending upon the presence or absence of obstacles in the cell. In this approach, it is not compulsory to run the entire algorithm. We may rather break after a certain degree of certainty has been achieved. We tested the algorithm in numerous situations with varying degrees of complexities. The algorithm was able to give an optimal path in all the situations given. The standard A^? algorithm failed to give results within time in most of the situations presented.     

未知环境下移动机器人实时路径规划

针对在未知环境下实现移动机器人实时的路径规划问题,提出了一种将快速扩展随机树（RRT）算法与视野域自适应的滚动窗口相结合的路径规划算法。该方法实时获取滚动窗口内的局部环境信息,根据环境的变化,滚动窗口视野域进行自适应调整,通过分析滚动窗口内传感器获取的信息,结合改进后的RRT算法筛选出可行的路径,控制移动机器人到达子目标点,在此过程中动态监测规划好的路径,确保路径合理,并重复上述过程,直至到达目标区域。实验对比分析表明,该方法能实时并有效实现未知环境下移动机器人的路径规划。     

Vectorization path planning for autonomous mobile agent in unknown environment

This paper presents a novel real-time path planning algorithm, called InsertBug, for an autonomous mobile agent in completely unknown environment. By using the algorithm, all the planned paths are described and stored by vectors. The algorithm combines range sensor data with safety radius to determine the blocking obstacles and calculate the shortest path by choosing the intermediate points. When there is obstacle blocking the current path, the intermediate points will be calculated, and the planned path will be regenerated by inserting the intermediate points. The local optimum avoidance strategy is also considered in this algorithm by specifying a fixed direction. The agent will return to the optimal direction after running out of the local optimum. Different simulation parameters are taken to show the advantages of this algorithm. Moreover, the performance of this algorithm has also been evaluated by comparing with another usual method via simulations. The results show that the safety performance and time requirements of this algorithm are significant superior to the algorithm contrasted with.