Traversability-Based RRT*for Planetary Rover Path Planning in Rough Terrain with LIDAR Point Cloud Data

As a sampling based path planning method, Rapidly-exploring Random Tree (RRT) is powerful for its high efficiency in finding feasible solutions. Optimal RRT (RRT*) improves RRT by asymptotically searching the optimal solution, however, at the cost of time consuming. This paper proposes an efficient optimal planning algorithm based on RRT* with adaptive restricted sampling space, which is inspired by the tropism of plants and named as tropistic RRT*. Tropistic RRT* improves the efficiency of the path planning by restricting the sampling space to approach the goal position just like the phototropism of plants. To validate our proposed algorithm, we carry out both path planning and motion planning experiments in 2D environments. In the path planning experiments, we compare the proposed algorithm with RRT* and Informed RRT* and experimental results show that our method outperforms the other two in the convergence rate, final solution cost and robustness. Furthermore, we also implement our algorithm in solving motion planning problem for a nonholonomic mobile robot and compare the time cost in the robot navigation process. Compared results exhibit that the time cost of our algorithm is much less than that of Real-Time RRT* in both static and dynamic environments.

Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

In this paper a novel combination of data structures and algorithms is presented as a fast and efcient practical approach to path planning for unmanned vehicles. This approach utilizes an architecture that integrates effective obstacle representation using an efcient Quadtree data structure, with path planning based on a modied Rapidly-exploring Random Tree (RRT) algorithm, and a path pruning technique based on Dijkstra’s algorithm, resulting in rapid discovery of sub-optimal feasible paths. One of the computationally expensive steps of the RRT algorithm is checking RRT segments for collision with obstacles. To address this problem, an efcient obstacle representation using Quadtrees is implemented enabling fast segment checks. While the RRT approach is guaranteed to nd a feasible path through complex obstacle elds with the probability of discovery approaching one as the number of iterations increases, the resulting paths may be far from optimal as the only criterion embedded in the RRT search is feasibility. For this reason, the paths generated by the RRT algorithm are passed through Dijkstra algorithm that nds a shortest obstacle-free path among the points on the path. Dijkstra algorithm that re-samples the segments of the path and nds the shortest path among the new points is implemented resulting in paths that are very close to the optimal ones. To speed up the computation, the basic search algorithm is implemented as a dual RRT from the initial and nal points. To account for motion transients along the path, a suitable size corridor is introduced, assuring that the vehicle has enough room for maneuvering among the obstacles. This computationally efcient architecture lends itself to real-time implementation for both pre-planning with known obstacles as well as rapid reactive re-planning in the presence of new pop-up obstacles. The proposed technique is evaluated using several challenging path-planning examples characterized by a large number of non-convex constraints arising in cluttered obstacle elds with known and unknown obstacles. A modication that allows an easy way to introduce vehicle dynamics constraints or actuation failures is also implemented. Algorithms have been implemented for two as well as three dimensional path planning applications.

The online cooperative path planning problem is discussed for multi-quadrotor maneuvering in an unknown dynamic environment. Based on the related basic concepts, typical three-dimensional obstacle models, such as spherical and cubic, and their collision checking criteria are presented in this article. An improved rapidly exploring random tree (RRT) algorithm with goal bias and greed property is proposed based on the heuristic search strategy to overcome the shortcomings of the classical RRT algorithm. Not only are the kinematic constraints of the quadrotor established but the time and space coordination strategy matching with the improved RRT algorithm is also presented in this article. Furthermore, a novel online collision avoidance strategy according to the partial information of the surrounding environment is proposed. On the basis of the above work, a distributed online path planning strategy is proposed to obtain the feasible path for each quadrotor. Numerical simulation results show that the improved RRT algorithm has better search efficiency than the classical RRT algorithm. And the satisfactory path planning and path tracking results prove that the above model and related planning strategies are reasonable and effective.

PurposeThe purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.Design/methodology/approachAnother RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.FindingsIt is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.Originality/valueThe paper presents a new nested RRT algorithm for fault tolerant motion planning.

Double manipulator path planning is the basis for overhead line live working robot to complete the task. The previous dual manipulator motion path planning method has some problems, such as low success rate, low convergence speed, unstable guidance, and easy to lead to path discontinuity in complex environment. Therefore, this paper proposes a decoupling motion planning method for two manipulators of live working robot based on hybrid RRT (Rapidly-Exploring Random Trees) algorithm. The hybrid RRT algorithm combines target offset RRT algorithm, bidirectional RRT algorithm and dynamic expansion RRT algorithm. MATLAB robot toolbox is used to simulate the motion path planning of 6-DOF double manipulator of overhead line live working robot. The results show that the method proposed in this paper can realize the loose coupling and efficient collision free motion planning of double manipulator of live working robot.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm’s convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm’s enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868 , 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.

Many path planning algorithms have been introduced so far, but most are costly, in path cost and in processing time, in large-scale uncluttered 3D environments such as underground mining stopes explored by an unmanned aerial vehicle (UAV). Rapidly-exploring Random Tree (RRT) algorithms are popular because of their probabilistic completeness and rapidity in finding a feasible path in single-query problems. Many of the algorithms (e.g. Informed RRT*, RRT#) developed to improve RRT need considerable time to converge in large environments. Shortcutting an RRT is an old idea that has been proven to outperform RRT variants. This paper introduces a new method, RRT-Rope, that aims at finding a near-optimal solution in a drastically shorter amount of time. The proposed approach benefits from fast computation of a feasible path with an altered version of RRT-connect, and post-processes it quickly with a deterministic shortcutting technique, taking advantage of intermediate nodes added to each branch of the tree. This paper presents simulations and statistics carried out to show the efficiency of RRT-Rope, which gives better results in terms of path cost and computation time than other popular RRT variations and shortening techniques in all our simulation environments, and is up to 70% faster than the next best algorithm in a representative stope.

Single tree detection is important for monitoring tree plantations in order to estimate crop yield, which can be used for assessing food and biofuels. In the field of remote sensing, there have been studies regarding single tree extraction using Light Detection and Ranging (LiDAR) point cloud data. Canopy Height Model (CHM) can be simply derived from these LiDAR data where trees can be analysed using different physical characteristics such as tree crown width, and height. However, using these physical characteristics can’t be very useful for identifying which trees are within a plantation, a forest, or a single tree. This is the motivation for developing algorithms of automated differentiation of random trees from plantation trees. Object Based Image Analysis (OBIA) was used to acquire the locations of individual trees. Trees were separated from other land cover in the image using a CHM derived from LiDAR point cloud data. Using watershed algorithm in eCognition, trees with good canopy spacing was easily distinguished. A spatial point pattern analysis was done from the extracted locations of trees to differentiate regularly patterned points from randomly distributed points. Nearest neighbour statistics was done to provide measurement of the distribution of the extracted points. The Z-Score parameter from nearest neighbor was then added to the layers in eCognition as supplement to the available height derivatives such as mean, standard deviation, and different textural parameters. Supervised type of classification was used in order to classify the trees on the image. Training samples of each tree from the image were entered in Support Vector Machine (SVM) to classify the different trees. Two sets of validation were done to compare the results of using the object parameters only, and the object parameters and Z-Score. Addition of the Z-Score from the object parameters increased the obtained accuracy on both validation sets. Classification accuracy based on validation set 1 and set 2 increased by about 20% and 6%, respectively. Additional spatial pattern analysis methods and validation can further prove the use of such layers in improving classification of trees.

Large trees are important to a wide variety of wildlife, including many species of conservation concern, such as the California spotted owl (Strix occidentalis occidentalis). Light detection and ranging (LiDAR) has been successfully utilized to identify the density of large‐diameter trees, either by segmenting the LiDARpoint cloud into individual trees, or by building regression models between variables extracted from the LiDARpoint cloud and field data. Neither of these methods is easily accessible for most land managers due to the reliance on specialized software, and much available LiDARdata are being underutilized due to the steep learning curve required for advanced processing using these programs. This study derived a simple, yet effective method for estimating the density of large‐stemmed trees from the LiDARcanopy height model, a standard raster product derived from the LiDARpoint cloud that is often delivered with the LiDARand is easy to process by personnel trained in geographic information systems (GIS). Ground plots needed to be large (1 ha) to build a robust model, but the spatial accuracy of plot center was less crucial to model accuracy. We also showed that predicted large tree density is positively linked to California spotted owl nest sites.

This paper presents a method for planning a motion for a humanoid robot performing manipulation tasks in a high dimensional space such that the energy consumption of the robot is minimized. While sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT) and its variants, have been highly effective for complex path planning problems, it is still difficult to find the minimum cost path in a high dimensional space since RRT-based algorithms extend a search tree locally, requiring a large number of samples to find a good solution. This paper presents an efficient nonmyopic motion planning algorithm for finding a minimum cost path by combining RRT* and cross entropy (CE). The proposed method constructs two RRT trees: the first tree is a standard RRT tree which is used to determine the nearest node in the tree to a randomly chosen point and the second tree contains the first tree with additional long extensions. By maintaining two separate trees, we can grow the search tree non-myopically to improve efficiency while ensuring the asymptotic optimality of RRT*. We first identify and demonstrate the limitation of RRT* when it is applied to energy-efficient path planning in a high dimensional space. Results from experiments show that the proposed method consistently achieves the lowest energy consumption against other algorithms.

Rapid path planner plays an important role in autonomous ground vehicle (AGV) operation. Depending on the non-holonomic kinematics constraints of AGV, its path planning problem is discussed. Since rapidly-exploring random tree (RRT) can directly take non-holonomic constraints into consideration, it is selected to solve this problem. By applying extra constraints on the movement, the generation of new configuration in RRT algorithm is simplified and accelerated. With section collision detection method applied, collision detection within the planer becomes more accurate and efficient. Then a new path planner is developed. This method complies with the non-holonomic constraints, avoids obstacles effectively and can be rapidly carried out while the vehicle is running. Simulation shows that this path planner can complete path planning in less than 0.5 s for a 170 m×170 m area with moderate obstacle complexity.

This paper proposes a hybrid Unmanned Aerial Vehicle (UAV) path planning method that combines the Rapidly-exploring Random Tree (RRT) algorithm with Proximal Policy Optimization (PPO). The proposed method aims to enhance the efficiency and adaptability of UAV path planning in complex and dynamic environments. The RRT algorithm excels at quickly generating a feasible path from a start point to a goal point. However, the quality of its paths is often suboptimal, and it lacks adaptability in dynamic settings. In contrast, PPO, a deep reinforcement learning algorithm, optimizes paths through iterative policy updates, enabling the UAV to adapt to environmental changes. Our approach first employs RRT to generate an initial path, which is subsequently refined by PPO to improve smoothness and adaptability. Experimental results demonstrate that the RRT-PPO hybrid method performs favorably in terms of path length, computational time, and obstacle avoidance capability, effectively improving the task completion efficiency of UAVs in complex environments.