A fast two-stage ACO algorithm for robotic path planning

Ant colony optimization (ACO) algorithms are often used in robotic path planning; however, the algorithms have two inherent problems. On one hand, the distance elicitation function and transfer function are usually used to improve the ACO algorithms, whereas, the two indexes often fail to balance between algorithm efficiency and optimization effect; On the other hand, the algorithms are heavily affected by environmental complexity. Based on the scent pervasion principle, a fast two-stage ACO algorithm is proposed in this paper, which overcomes the inherent problems of traditional ACO algorithms. The basic idea is to split the heuristic search into two stages: preprocess stage and path planning stage. In the preprocess stage, the scent information is broadcasted to the whole map and then ants do path planning under the direction of scent information. The algorithm is tested in maps of various complexities and compared with different algorithms. The results show the good performance and convergence speed of the proposed algorithm, even the high grid resolution does not affect the quality of the path found.     

基于改进遗传算法的机器人路径规划

标准遗传算法在解决各类优化问题中获得成功,但它在具体的应用中由于缺乏对特定知识的利用,其性能有待提高.针对机器人路径规划的实际应用,通过优化设计标准遗传算法中的交叉算子和变异算子,提出一种应用于机器人路径规划的改进型遗传算法.在把地图特征信息引入遗传算子的操作过程中提高了算法的进化效率.计算机仿真实验结果证明该算法在收敛速度、最优解输出概率方面相对于基本遗传算法有了显著提高.     

Online probabilistic learning for fuzzy inference system

Online learning is a key methodology for expert systems to gracefully cope with dynamic environments. In the context of neuro-fuzzy systems, research efforts have been directed toward developing online learning methods that can update both system structure and parameters on the fly. However, the current online learning approaches often rely on heuristic methods that lack a formal statistical basis and exhibit limited scalability in the face of large data stream. In light of these issues, we develop a new Sequential Probabilistic Learning for Adaptive Fuzzy Inference System (SPLAFIS) that synergizes the Bayesian Adaptive Resonance Theory (BART) and Rule-Wise Decoupled Extended Kalman Filter (RDEKF) to generate the rule base structure and refine its parameters, respectively. The marriage of the BART and RDEKF methods, both of which are built upon the maximum a posteriori (MAP) principle rooted in the Bayes’ rule, offers a comprehensive probabilistic treatment and an efficient way for online structural and parameter learning suitable for large, dynamic data stream. To manage the model complexity without sacrificing its predictive accuracy, SPLAFIS also includes a simple procedure to prune inconsequential rules that have little contribution over time. The predictive accuracy, structural simplicity, and scalability of the proposed model have been exemplified in empirical studies using chaotic time series, stock index, and large nonlinear regression datasets.     

Optimization of layout and path planning of surgical robotic system

Positioning a surgical robot for optimal operation in a crowded operating room is a challenging task. In the robotic-assisted surgical procedures, the surgical robot’s end-effector must reach the patient’s anatomical targets because repositioning of the patient or surgical robot requires additional time and labor. This paper proposes an optimization algorithm to determine the best layout of the operating room, combined with kinematics criteria and optical constraints applied to the surgical assistant robot system. A new method is also developed for trajectory of robot’s end-effector for path planning of the robot motion. The average deviations obtained from repeatability tests for surgical robot’s layout optimization were 1.4 and 4.2 mm for x and y coordinates, respectively. The results of this study show that the proposed optimization method successfully solves the placement problem and path planning of surgical robotic system in operating room.

     

Non-nominal path planning for robust robotic assembly

In manufacturing and assembly processes it is important, in terms of time and money, to verify the feasibility of the operations at the design stage and at early production planning. To achieve that, verification in a virtual environment is often performed by using methods such as path planning and simulation of dimensional variation. Lately, these areas have gained interest both in industry and academia, however, they are almost always treated as separate activities, leading to unnecessary tight tolerances and on-line adjustments.     

A path planning algorithm for industrial robots

Instead of using the tedious process of robot teaching, an off-line path planning algorithm has been developed for industrial robots to improve their accuracy and efficiency. Collision avoidance is the primary concept to achieve such goal. By use of the distance maps, the inspection of obstacle collision is completed and transformed to the configuration space in terms of the robot joint angles. On this configuration map, the relation between the obstacles and the robot arms is obvious. By checking the interference conditions, the collision points are indicated with marks and collected into the database. The path planning is obtained based on the assigned marked number of the passable region via wave expansion method. Depth-first search method is another approach to obtain minimum sequences to pass through. The proposed algorithm is experimented on a 6-DOF industrial robot. From the simulation results, not only the algorithm can achieve the goal of collision avoidance, but also save the manipulation steps.     

基于双向搜索改进A*算法的自动导引车路径规划

在面积比较大的或划分精细的栅格地图中进行自动导引车(AGV)行驶路径规划时,经典的A*算法搜索得到的路径往往冗余节点和转折点较多,搜索路径时间较长.为了提高A*算法的实时性,提出了一种基于双向搜索路径的A*算法.首先,对于A*算法的启发函数引入父节点和Chebyshev Distance,改进启发函数;其次,引入双向路径搜索的动态窗口,同时从路径的起点和终点搜索路径,得到一条初始路径,并论述了动态窗口的大小对于双向搜索路径的影响;最后,依据关键点搜索原理,剔除初始路径中存在的冗余节点,得到最终的搜索路径.实验证明,相较于单向改进A*算法和改进人工势场算法,双向搜索改进A*算法搜索路径耗费时间分别降低了22.9％和78.4％,路径包含节点数分别降低了82.2％和99.5％,证明了算法的有效性.     

基于双向搜索改进A*算法的自动导引车路径规划

在面积比较大的或划分精细的栅格地图中进行自动导引车(AGV)行驶路径规划时,经典的A*算法搜索得到的路径往往冗余节点和转折点较多,搜索路径时间较长.为了提高A*算法的实时性,提出了一种基于双向搜索路径的A*算法.首先,对于A*算法的启发函数引入父节点和Chebyshev Distance,改进启发函数;其次,引入双向路径搜索的动态窗口,同时从路径的起点和终点搜索路径,得到一条初始路径,并论述了动态窗口的大小对于双向搜索路径的影响;最后,依据关键点搜索原理,剔除初始路径中存在的冗余节点,得到最终的搜索路径.实验证明,相较于单向改进A*算法和改进人工势场算法,双向搜索改进A*算法搜索路径耗费时间分别降低了22.9％和78.4％,路径包含节点数分别降低了82.2％和99.5％,证明了算法的有效性.     

Bezier曲线与A~*算法融合的移动机器人路径规划

移动机器人路径规划一直是移动机器人领域里的重要技术问题。A*算法在最优路径搜索上有着比较成功的运用,但在栅格环境下的A*算法也存在着折线多、转折角度大等问题。在考虑移动机器人的实际工作环境及相关运动参数后,这些问题都将大大地影响移动机器人的工作效率。在对以上问题进行分析后提出了一种基于Bezier曲线与A*算法融合的方法来实现移动机器人的路径规划,再通过MATLAB、V-REP仿真工具来实现Bezier_A*融合算法与平滑A*算法及A*算法的对比。通过Bezier_A*融合算法使得机器人在工作中的寻优能力、路径规划效率都得到较大的提高。     

改进A~*蚁群算法求解机器人路径规划问题

针对蚁群算法收敛速度慢、效率低以及易陷入局部最优的一系列问题,提出改进的A~*蚁群算法。为降低蚁群死锁、停滞的概率,先将栅格地图进行处理。其次为了提高蚁群的效率,引进A~*算法确定蚁群的初始信息素,同时改进蚁群信息素更新方式,从而提高算法的收敛速度;针对局部最优的问题,提出将蚁群中的启发函数进行改进,不仅考虑到可行栅格中的最短距离,还考虑到目标点的位置,并且引入简化算子对蚁群的路径进行优化。通过4组仿真对比,改进的A~*蚁群算法效果显著。     

A probabilistic fuzzy logic system for modeling and control

In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.     

A new potential field-based algorithm for path planning

In this paper, the path-planning problem is considered. We introduce a new potential function for path planning that has the remarkable feature that it is free from any local minima in the free space irrespective of the number of obstacles in the configuration space. The only global minimum is the goal configuration whose region of attraction extends over the whole free space. We also propose a new method for path optimization using an expanding sphere that can be used with any potential or penalty function. Simulations using a point mobile robot and smooth obstacles are presented to demonstrate the qualities of the new potential function. Finally, practical considerations are also discussed for nonpoint robots     

Machine vision based path planning for a robotic golf club head welding system

A path planning method based on machine vision techniques is constructed for a golf-club head robotic welding system. This system uses 3D machine vision techniques to recognize the weldseam and generates a welding path for the robot. The location of the weldseam is discovered by applying a Sobel mask to the captured data. A Laplace mask is also useful to filter out the noise points due to the scatter light refraction of tack-welding spots. The weldseam is then replenished and smoothed out by a B-spline curve fitting. The task frame of the weldseam is computed by finding the tangent, normal, and bi-normal of the curve. The robotic welding path is obtained by further rotations and translation along the axes of the task frame according to the requirement of the welding attitude. The developed machine vision technique and the mathematic framework pertaining to the generation of a welding task frame can readily be used for various three-dimensional welding tasks.     

A family of admissible heuristics for A* to perform inference in probabilistic classifier chains

Probabilistic classifier chains have recently gained interest in multi-label classification, due to their ability to optimally estimate the joint probability of a set of labels. The main hindrance is the excessive computational cost of performing inference in the prediction stage. This pitfall has opened the door to propose efficient inference alternatives that avoid exploring all the possible solutions. The \(\epsilon \)-approximate algorithm, beam search and Monte Carlo sampling are appropriate techniques, but only \(\epsilon \)-approximate algorithm with \(\epsilon =0\) theoretically guarantees reaching an optimal solution in terms of subset 0/1 loss. This paper offers another alternative based on heuristic search that keeps such optimality. It consists of applying the A* algorithm providing an admissible heuristic able to explore fewer nodes than the \(\epsilon \)-approximate algorithm with \(\epsilon =0\). A preliminary study has already coped with this goal, but at the expense of the high computational time of evaluating the heuristic and only for linear models. In this paper, we propose a family of heuristics defined by a parameter that controls the trade-off between the number of nodes explored and the cost of computing the heuristic. Besides, a certain value of the parameter provides a method that is also suitable for the non-linear case. The experiments reported over several benchmark datasets show that the number of nodes explored remains quite steady for different values of the parameter, although the time considerably increases for high values. Hence, low values of the parameter give heuristics that theoretically guarantee exploring fewer nodes than the \(\epsilon \)-approximate algorithm with \(\epsilon =0\) and show competitive computational time. Finally, the results exhibit the good behavior of the A* algorithm using these heuristics in complex situations such as the presence of noise.     

A new hybrid algorithm for path planning of mobile robot

The Journal of Supercomputing - The selection of algorithm is the most critical part in the mobile robot path planning. At present, the commonly used algorithms for path planning are genetic...     

A modified ant optimization algorithm for path planning of UCAV

A modified ant algorithms is presented as a fast and efficient approach for path planning of UCAV in this paper. To fleetly and reliably accomplish the air combat task, the path planning plays an extremely important role in the design of UCAV. The planned path can ensure UCAV reach the destination along the optimization path with the minimum probability of being found and the minimum energy consumed cost. Due to the big search space, the original ant algorithm can easily converge to local best solutions, and the search speed is slow. For avoiding these disadvantages, an improved ant algorithm is given and it is used to optimize path of UCAV. The modified ant algorithm can improve the speed of selection course, and decrease the probability of local best solutions. When UCAV meets the unexpected threat during its fly, it needs to revise the aforehand given path with re-planning technology. Based on the modified ant algorithm, a new method of three-dimensional real-time path re-planning is presented for UCAV. The simulation results show that this proposed path-planning scheme can obtain the optimization path which can be re-optimized when the unexpected threats appear.     

A penalized batch-Bayesian approach to informative path planning for decentralized swarm robotic search

Autonomous Robots - Swarm-robotic approaches to search and target localization, where target sources emit a spatially varying signal, promise unparalleled time efficiency and robustness. With most...     

Two-way D algorithm for path planning and replanning

Inspired by the Witkowski’s algorithm, we introduce a novel path planning and replanning algorithm — the two-way D^∗ (TWD^∗) algorithm — based on a two-dimensional occupancy grid map of the environment. Unlike the Witkowski’s algorithm, which finds optimal paths only in binary occupancy grid maps, the TWD^∗ algorithm can find optimal paths in weighted occupancy grid maps. The optimal path found by the TWD^∗ algorithm is the shortest possible path for a given occupancy grid map of the environment. This path is more natural than the path found by the standard D^∗ algorithm as it consists of straight line segments with continuous headings. The TWD^∗ algorithm is tested and compared to the D^∗ and Witkowski’s algorithms by extensive simulations and experimentally on a Pioneer 3DX mobile robot equipped with a laser range finder.     

A learning fuzzy algorithm for motion planning of mobile robots

A sensor-based fuzzy algorithm is proposed to navigate a mobile robot in a 2-dimensional unknown environment filled with stationary polygonal obstacles. When the robot is at the starting point, vertices of the obstacles that are visible from the robot are scanned by the sensors and the one with the highest priority is chosen. Here, priority is an output fuzzy variable whose value is determined by fuzzy rules. The robot is then navigated from the starting point to the chosen vertex along the line segment connecting these two points. Taking the chosen vertex as the new starting point, the next navigation decision is made. The navigation process will be repeated until the goal point is reached.In implementation of fuzzy rules, the ranges of fuzzy variables are parameters to be determined. In order to evaluate the effect of different range parameters on the navigation algorithm, the total traveling distance of the robot is defined as the performance index first. Then a learning mechanism, which is similar to the simulated annealing method in the neural network theory, is presented to find the optimal range parameters which minimize the performance index. Several simulation examples are included for illustration.