Probabilistic Roadmap Research Articles

Adaptive path planning for the gantry welding robot system

Group welding is one of the crucial components with difficulties in shipbuilding with irregular distribution and a great variety of welding seams. The traditional manual teaching method has drawbacks of the long development period, low efficiency, and poor welding accuracy in dealing with such weld seams in the welding process. In order to realize automation planning and welding for arc welding robot, a gantry welding robot system with adaptive path planning ability is proposed to solve the problem with various welding workpieces and position. The 3D laser scanning sensor is first adopted to identify the type and position of the workpiece. Then, the discrete genetic algorithm based on inverted pyramid structure (GA-IPS) is applied for welding sequence planning, the discrete two-stage adaptive reference direction guided evolutionary algorithm with modified dominance relation for many-objective optimization (AREA-APD) is proposed for dual-objective welding sequence planning. Furthermore, the lazy probabilistic roadmap method with transition points (Lazy PRM-TP) is applied to local trajectory planning. And the laser vision system is used to realize the precise location and tracking of the welding seam. Besides, the computer system is designed to realize human-computer interaction and process monitoring. The simulation and experiments show the superior performance of the proposed strategy.

Real-Time Path Planning for Robot Using OP-PRM in Complex Dynamic Environment.

During task execution, the autonomous robots would likely pass through many narrow corridors along with mobile obstacles in dynamically complex environments. In this case, the off-line path planning algorithm is rather difficult to be directly implemented to acquire the available path in real-time. Hence, this article proposes a probabilistic roadmap algorithm based on the obstacle potential field sampling strategy to tackle the online path planning, called Obstacle Potential field-Probabilistic Roadmap Method (OP-PRM). The obstacle potential field is introduced to determine the obstacle area so as to construct the potential linked roadmap. Then the specific range around the obstacle boundary is justified as the target sampling area. Based on this obstacle localization, the effectiveness of the sampling points falling into the narrow corridors can be increased greatly for feasible roadmap construction. Furthermore, an incremental heuristic D* Lite algorithm is applied to search the shortest paths between the starting point and the target point on the roadmap. Simulation experiments demonstrate that the OP-PRM path planning algorithm can enable robots to search the optimal path fast from the starting point to the destination and effectively cross narrow corridors in complex dynamic environments.

Helical5AM: Five-axis parametrized helical additive manufacturing

Additive manufacturing (AM) commonly utilizes slicing techniques to create layers of a model, in which material is deposited layer-by-layer. However, slicing method directly affects the mechanical properties of the printed parts. This paper introduces a new AM technique (named as Helical5AM ), which employs print-paths having helical geometry for five-axis AM. Given an object to be printed, a base (supporting helical print-paths) with a center curve and helix parameters (such as lead angle and turn direction), a complete volumetric coverage using helical print-paths is obtained. A collision-free tool orientation is then generated using a probabilistic roadmap algorithm for depositing material along the helical print-paths by avoiding tool interference with obstacles. As a proof of concept, print-paths (of models) with orientation information obtained using the proposed algorithms are simulated using a five-axis AM simulation software, and material deposition process in Helical5AM is demonstrated using a five-axis AM machine. Furthermore, compression tests are performed on the printed parts for evaluating the effects of helix lead angles of the helical print-paths on mechanical properties of the printed parts. It has been confirmed that mechanical behavior of a printed part is predictable and tunable according to the helix lead angles of the print-paths. Helical5AM can potentially empower engineers to obtain AM parts with desirable mechanical properties.

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route.

Task Roadmaps: Speeding up Task Replanning.

Modern industrial robots are increasingly deployed in dynamic environments, where unpredictable events are expected to impact the robot’s operation. Under these conditions, runtime task replanning is required to avoid failures and unnecessary stops, while keeping up productivity. Task replanning is a long-sighted complement to path replanning, which is mostly concerned with avoiding unexpected obstacles that can lead to potentially unsafe situations. This paper focuses on task replanning as a way to dynamically adjust the robot behaviour to the continuously evolving environment in which it is deployed. Analogously to probabilistic roadmaps used in path planning, we propose the concept of Task roadmaps as a method to replan tasks by leveraging an offline generated search space. A graph-based model of the robot application is converted to a task scheduling problem to be solved by a proposed Branch and Bound (B&B) approach and two benchmark approaches: Mixed Integer Linear Programming (MILP) and Planning Domain Definition Language (PDDL). The B&B approach is proposed to compute the task roadmap, which is then reused to replan for unforeseeable events. The optimality and efficiency of this replanning approach are demonstrated in a simulation-based experiment with a mobile manipulator in a kitting application. In this study, the proposed B&B Task Roadmap replanning approach is significantly faster than a MILP solver and a PDDL based planner.

DTED 맵에서 무인기 경로 생성을 위한 Probabilistic RoadMap 병렬화

본 논문에서는 무인기의 경로 계획을 위한 산악 지형, 레이더 그리고 방공망 등을 3차원 환경으로 구현하고, Sampling 기반의 경로 계획 알고리즘인 PRM 알고리즘을 사용하여 경로 계획 및 재계획을 수행하는 방안에 대해 서술한다. 기존의 PRM 알고리즘의 경우 생성된 노드 사이에 장애물 존재 여부를 확인하기 위한 계산이 노드 간 1:1로 이루어지고 연속적으로 수행되어 노드 수나 노드를 연결하는 거리에 계산량이 크게 영향을 받는다. 이러한 부분을 개선하기 위해 제안하는 LineGridMask 기법을 통해 장애물 존재 여부 확인 방식을 단순화하고, 병렬화를 통해 경로 계획의 계산 시간을 감소시킨다. 마지막으로 기존 PRM 알고리즘과의 성능을 비교한 결과, 경로 계획에서는 최대 88%, 재계획의 경우 최대 94%까지 계산 시간이 감소하였음을 확인하였다.

Modeling of a wheeled humanoid robot and hybrid algorithm-based path planning of wheel base for the dynamic obstacles avoidance

PurposeMost of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.Design/methodology/approachThe kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.FindingsThe path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.Originality/valueModeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

An Efficient Sampling-Based Path Planning for the Lunar Rover with Autonomous Target Seeking

This paper presents an efficient path planning method for the lunar rover to improve the autonomy and exploration ability in the complex and unstructured lunar surface environment. Firstly, the safe zone for the rover’s motion is defined, based on which a detecting point selection strategy is proposed to choose target positions that meet the rover’s constraints. Secondly, an improved sampling-based path planning method is proposed to get a safe path for the rover efficiently. Thirdly, a map extension strategy for the unstructured and continually varying environment is included to update the roadmap, which takes advantage of the historical planning information. Finally, the proposed method is tested in a complex lunar surface environment. Numerical results show that the appropriate detecting positions can be selected autonomously, while a safe path to the selected detecting position can be obtained with high efficiency and quality compared with the Probabilistic Roadmap (PRM) and A* search algorithm.

The path-planning in radioactive environment based on HIOSD-PRM method

To ensure the safety of workers in the radioactive environment of nuclear facilities, it is necessary to develop an effective method to plan the walking path with a relatively small dose based on the information of the radiation field. In this paper, a stable and effective path planning method in the complex static radiation field is proposed based on the high-index optimization-based shrinking dimer (HIOSD) method and probabilistic roadmap (PRM) method. In a two-dimensional radiation field, local paths consist of plenty of dimers connecting saddle points to local minima can be dynamically built by the HIOSD method. With these local paths, the PRM algorithm combined with the A* algorithm is applied to optimize the whole walking path from the start point to the goal point based on the dose calculation model. To verify the effectiveness of our method, two application cases in hypothetical radiation fields are shown and we compared the results of the HIOSD-PRM method and the traditional PRM method. The results show that the average cumulative dose of the former in both cases is lower than those of the PRM method and it is worth mentioning that in case 2 with more sources, the HIOSD-PRM method shows very good stability. The built local paths which have much potential can be used multiple times and they can reduce the uncertainty of heuristic algorithms. Overall, this method is suitable for static energy fields with multiple radioactive sources and can reduce the radiation exposure to the workers in the radioactive environment of nuclear facilities.

Gesture- and vision-based automatic grasping and flexible placement in teleoperation

The teleoperation system has attracted a lot of attention because of its advantages in dangerous or unknown environments. It is very difficult to develop an operating system that can complete complex tasks in a completely autonomous. This paper proposes a robot arm control strategy based on gestures and visual perception. The strategy combines the advantages of humans and robots to obtain a convenient and flexible interaction model. The hand’s data were obtained by Leap-Motion. Then a neural network algorithm was used to classify the nine gestures, which were used for robots control through a finite state machine. The control mode switched between indicative control and mapping control. The robot acquired an autonomous grasp ability by incorporating YOLO 6D, depth data and a probabilistic roadmap planner algorithm. The robot completed most of the trajectory independently, while a few flexible trajectories required users to do mapping actions. This interactive mode reduces the burden of the users to a certain extent that makes up for the shortcomings of traditional teleoperation.

Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Trajectory optimization is a promising method for planning trajectories of robotic manipulators. With the increasing success of collaborative robots in dynamic environments, the demand for online planning methods grows and offers new opportunities as well as challenges for trajectory optimization. Special requirements in terms of real-time capabilities are one of the greatest difficulties. Optimizing a short planning horizon instead of an entire trajectory is one approach to reduce computation time, which nonetheless separates the optimality of local and global solutions. This contribution introduces, on the one hand, Extended Initialization as a new approach that reduces the risk of local minima and aims at improving the quality of the global trajectory. On the other hand, the particularly critical cases in which local solutions lead to standstills are mitigated by globally guiding local solutions. The evaluation performs four experiments with comparisons to Stochastic Trajectory Optimization for Motion Planning (STOMP) or Probabilistic Roadmap Method (PRM*) and demonstrates the effectiveness of both approaches.

Robotic mushroom harvesting by employing probabilisticroad map and inverse kinematics

collision-free path to a destination position in a random farm is determined using a probabilistic roadmap thatcan manage static and dynamic obstacles. The position of ripening mushrooms is a result of picture processing.A mushroom harvesting robot is explored that uses inverse kinematics at the target position to compute the stateof a robotic hand for grasping a ripening mushroom and plucking it. The Denavit–Hartenberg approach was usedto create a kinematic model of a two-finger dexterous hand with three degrees of freedom for mushroom picking.Unlike prior experiments in mushroom harvesting, mushrooms are not planted in a grid or design but are randomlyscattered. At any point throughout the harvesting process, no human interaction is necessary

A Comparison of the Effectiveness of the RRT, PRM, and Novel Hybrid RRT-PRM Path Planners

Abstract: Sampling-based path planners develop paths for robots to journey to their destinations. The two main types of sampling-based techniques are the probabilistic roadmap (PRM) and the Rapidly Exploring Random Tree (RRT). PRMs are multi-query methods that construct roadmaps to find routes, while RRTs are single-query techniques that grow search trees to find paths. This investigation evaluated the effectiveness of the PRM, the RRT, and the novel Hybrid RRT-PRM methods. This novel path planner was developed to improve the performance of the RRT and PRM techniques. It is a fusion of the RRT and PRM methods, and its goal is to reduce the path length. Experiments were conducted to evaluate the effectiveness of these path planners. The performance metrics included the path length, runtime, number of nodes in the path, number of nodes in the search tree or roadmap, and the number of iterations required to obtain the path. Results showed that the Hybrid RRT-PRM method was more effective than the PRM and RRT techniques because of the shorter path length. This new technique searched for a path in the convex hull region, which is a subset of the search area near to the start and end locations. The roadmap for the Hybrid RRT-PRM could also be re-used to find pathways for other sets of initial and final positions. Keywords: Path Planning, Sampling-based algorithms, search tree, roadmap, single-query planners, multi-query planners, Rapidly Exploring Random Tree (RRT), Probabilistic Roadmap (PRM), Hybrid RRT-PRM

Optimal Path Planning using Informed Probabilistic Road Map Algorithm

This study aims to propose a new path planning algorithm that can guarantee the optimal path solution. The method used is to hybridize the Probabilistic Road Map (PRM) algorithm with the Information Search Algorithm. This hybridization algorithm is called the Informed-PRM algorithm. There are two informed search methods used. The first method is the informed sampling through an ellipsoid subset whose eccentricity is dependent on the length of the shortest current solution that is successfully planned in that iteration. The second method is to use a local search algorithm. The basic PRM algorithm will be run in the first iteration. Since the second iteration, the generation of sample points in the PRM algorithm will be carried out based on information. The informed sampling method will be used to generate 50% of the sampling points. Meanwhile, the remaining number of sample points will be generated using a local search algorithm. Using several benchmark cases, we compared the performance of the Informed-PRM algorithm with the Rapidly Exploring Random Tree* (RRT*) and informed RRT* algorithm. The test results show that the Informed-PRM algorithm successfully constructs the nearly optimal path for all given cases. In producing the path, the time and path cost of the Informed-PRM algorithm is better than the RRT* and Informed RRT* algorithm. The Friedman test was then performed to check for the significant difference in performance between Informed-PRM with RRT* and Informed RRT*. Thus, the Informed-PRM algorithm can be implemented in various systems that require an optimal path planning algorithm, such as in the case of medical robotic surgery or autonomous vehicle systems.

Path planning for manipulators based on an improved probabilistic roadmap method

An indispensable feature of an intelligent manipulator is its capability to quickly plan a short and safe path in the presence of obstacles in its workspace. Among the path planning methods, the probabilistic roadmap (PRM) method has been widely applied in path planning for a high-dimensional manipulator to avoid obstacles. However, its efficiency remains disappointing when the free space of manipulators contains narrow passages. To solve this problem, an improved PRM method is proposed in this paper. Based on a virtual force field, a new sampling strategy of PRM is presented to generate configurations more appropriate for practical application in the free space. Correspondingly, in order to interconnect these configurations to form a roadmap, a new connection strategy is designed, which consists of three stages and can gradually improve the connectivity of the roadmap. The contributions of this paper are as follows. The new sampling strategy can increase the sampling density at the narrow passages of the free space and reduce the redundancy of the samples in the wide-open regions of the free space; the three-stage connection strategy for interconnecting samples can ensure a high-connectivity roadmap; through synthesizing the above strategies, the improved PRM method is more suitable for path planning of manipulators to avoid obstacles efficiently in a cluttered environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available for manipulator of any degrees of freedom.

Large-Scale Mapping and Navigation in VirtualWorlds: Thesis Summary

Virtual worlds present a challenge for intelligent mobile agents. They are required to generate maps of very large scale, dynamic and unstructured environments in a short amount of time. We investigate how to represent maps of ever growing virtual environments, how the agent can build, update and use these maps to navigate between points in the environment. We look at trails, the movement of other people and agents in the environment as a new information source. We can use trails to improve the generation of probabilistic roadmaps in these environments and enable the agent to segment space intelligently. Our future plans are to extend this to look at dynamic environments, where the agent will have to recognise change and update the map and how this will affect the map representation.

Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM

Autonomous, flexible, and human–robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform.

Representing and Generating Maps of Large-Scale Virtual Environments for Intelligent Mobile Agents

This paper looks at the challenges and solutions for intelligent mobile agents existing in virtual environments. Representing and using a map of these very large-scale, dynamic environments is a key challenge in providing an autonomous agent for large, online worlds. We look at a method for improving the generation of probabilistic roadmaps by observing and using the movements of other avatars in an environment. We also look at methods for segmenting and scaling the map in very large environments. Finally we extend this to look at generating and updating maps in dynamic environments.

Human–Robot Collaborative Assembly Based on Eye-Hand and a Finite State Machine in a Virtual Environment

With the development of the global economy, the demand for manufacturing is increasing. Accordingly, human–robot collaborative assembly has become a research hotspot. This paper aims to solve the efficiency problems inherent in traditional human-machine collaboration. Based on eye–hand and finite state machines, a collaborative assembly method is proposed. The method determines the human’s intention by collecting posture and eye data, which can control a robot to grasp an object, move it, and perform co-assembly. The robot’s automatic path planning is based on a probabilistic roadmap planner. Virtual reality tests show that the proposed method is more efficient than traditional methods.

Optimized neural network based path planning for searching indoor pollution source

Traditional path planning methods, such as A* and probabilistic roadmap, are seriously limited by the resolution and the number of samples that cannot meet the needs of complex indoor environments. Therefore, artificial neural network-based methods have become the mainstream due to the strong learning ability and robustness. In this paper, the neural network is used to control the agent according to the concentration and distance information, and a novel approach called adaptive neural evolution of augmenting topologies is proposed to optimize the neural network to improve its performance. Besides, the objective function is constructed by combining the pollution concentration and the residual energy of the agent to save energy and search time. Experiments show that our approach successfully finds an optimal path to pollution sources in different indoor environments while achieving energy saving and obstacle avoidance.