Rapidly-exploring Random Tree Star Research Articles

Rapid motion planning of manipulator in three-dimensional space under multiple scenes

Most working scenes of industrial robots are static scenes and from a previous static scene to a current static scene is a multi-scene. Finding optimal paths with limited time is difficult for motion planning in a high-dimensional space or in multiple scenes. The low efficiency of motion planning of rapidly-exploring random tree star in high-dimensional spaces, low adaptability of global replanning to multiple scenes are addressed by proposing the local replanning based on goal dynamically-guiding rapidly-exploring random tree star (LR-GD-RRT*). The algorithm contributes to fast path tree exploration and multi-scene motion planning. For path tree exploration, sampling points heuristically generating and new nodes growth by goal dynamically-guiding are proposed to reduce the blind and ineffective searches. Moreover, dynamic adjustment of size of new node neighborhood according to density of the obstacles is proposed to search for more neighbor nodes to optimize the path and also to reduce ineffective computation to improve efficiency. For multi-scene, three steps of trimming, re-exploration, and reconnection for local replanning are proposed. For path tree exploration, simulations in 2D plane, 3D space, and the manipulator show that GD-RRT* improves convergence speed, shortens path length and search time, compared with RRT*. For multi-scene, simulations in 3D space and with the manipulator show that the local replanning of the current scene has both lower path cost and higher planning efficiency compared with the global replanning of the previous scene. Motion of the six-degree-of-freedom robot end in a real scene also verifies the effectiveness of the LR-GD-RRT*.

A study on path-planning algorithm for a multi-section continuum robot in confined multi-obstacle environments

Abstract In confined multi-obstacle environments, generating feasible paths for continuum robots is challenging due to the need to avoid obstacles while considering the kinematic limitations of the robot. This paper deals with the path-planning algorithm for continuum robots in confined multi-obstacle environments to prevent their over-deformation. By modifying the tree expansion process of the Rapidly-exploring Random Tree Star (RRT*) algorithm, a path-planning algorithm called the continuum-RRT* algorithm herein is proposed to achieve fewer iterations and faster convergence as well as generating desired paths that adhere to the kinematic limitations of the continuum robots. Then path planning and path tracking are implemented on a tendon-driven four-section continuum robot to validate the effectiveness of the path-planning algorithm. The path-planning results show that the path generated by the algorithm indeed has fewer transitions, and the path generated by the algorithm is closer to the optimal path that satisfies the kinematic limitations of the continuum robot. Furthermore, path-tracking experiments validate the successful navigation of the continuum robot along the algorithm-generated path, exhibiting an error range of 2.51%–3.91%. This attests to the effectiveness of the proposed algorithm in meeting the navigation requirements of continuum robots.

IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality

Due to its asymptotic optimality, the Rapidly-exploring Random Tree star (RRT*) algorithm is widely used for robotic operations in complex environments. However, the RRT* algorithm suffers from poor path quality, slow convergence, and unstable generation of high-quality paths in the path planning process. This paper proposes an Improved Bi-Tree Obstacle Edge Search Artificial Potential Field RRT* algorithm (IBPF-RRT*) to address these issues. First, based on the RRT* algorithm, this paper proposes a new obstacle edge search artificial potential field strategy (ESAPF), which speeds up the path search and improves the path quality simultaneously. Second, a bi-directional pruning strategy is designed to optimize the bi-directional search tree branch nodes and combine the bi-directional search strategy to reduce the number of iterations for convergence speed significantly. Third, a novel path optimization strategy is proposed, which enables high-quality paths to be generated stably by creating an entirely new node between two path nodes and then optimizing the paths using a pruning strategy based on triangular inequalities. Experimental results in three different scenarios show that the proposed IBPF-RRT* algorithm outperforms other methods in terms of optimal path quality, algorithm stability, and the number of iterations when compared to the RRT*, Q-RRT*, PQ-RRT*, F-RRT* and CCPF-RRT* algorithms, and proves the effectiveness of the proposed three strategies.

Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL) for mobile robot

Path planning is an important task for mobileF service robots. Most of the available path-planning algorithms are applicable only in static environments. Achieving path planning becomes a difficult task in an unknown, dynamic environment. To solve the path planning problem in an unknown dynamic environment, this paper proposes a Bidirectional Rapidly-exploring Random Tree Star-Dynamic Window Approach (BRRT*-DWA) algorithm with Adaptive Monte Carlo Localization (AMCL). Bidirectional Rapidly-exploring Random Tree Star(BRRT*) is used to generate an optimal global path plan, Dynamic Window Approach(DWA) is a local planner and Adaptive Monte Carlo Localization(AMCL) is used as a localization technique. The robot can navigate using the map file of the unknown environment created by Simultaneous Localization And Mapping (SLAM) and the data from the Light Detection and Ranging (LiDAR) sensor while avoiding dynamic and static obstacles. In addition, the object identification algorithm You Only Look Once (YOLO) was adopted, trained, and used for the robot to recognize objects and people. Results obtained from both simulation and experiment show the proposed method can achieve better performance in a dynamic environment compared with other state-of-the-art algorithms.

Robot learning-enhanced tree-based algorithms for kinodynamic motion planning: A comparative analysis

Kinodynamic motion planning is pivotal in advancing robotics, en- abling autonomous systems to navigate dynamic environments effectively while adhering to both kinematic and dynamic constraints. This study delves into the efficacy of tree sampling-based planners, namely the Rapidly- exploring Random Tree (RRT), Rapidly-exploring Random Tree Star (RRT*), and Dominance Informed Region Trees (DIRT), in kinodynamic motion planning. Through a comparative analysis focusing on both fully informed and uninformed versions of these algorithms, I explore their performance in environments with dynamic constraints. Special emphasis is placed on the integration of learned controls, aiming to enhance maneuver planning. My research reveals significant differences in success rates, iterations, and path costs among the algorithms, underscoring DIRTs superiority under certain conditions and the beneficial impact of learned controls. These findings contribute valuable insights into the selection and optimization of motion planning algorithms, paving the way for more efficient and adapt- able autonomous systems.

More Quickly-RRT*: Improved Quick Rapidly-exploring Random Tree Star algorithm based on optimized sampling point with better initial solution and convergence rate

RRT* (Rapidly-exploring Random Tree Star), as a variant of RRT (Rapidly-exploring Random Tree), is widely used to solve path planning problems because of its asymptotic optimality. However, the algorithm is inefficient due to the high initial path cost and the slow convergence rate. In this paper, we propose a More Quickly-RRT* (MQ-RRT*) path planning algorithm based on optimized sampling points to solve the problems. A sparse sampling mechanism is proposed in MQ-RRT* to improve the global search efficiency by reducing repetitive sampling. To make the random tree oriented when expanding, a dynamic goal-biased strategy is proposed, which can reduce the sampling time. Like Q-RRT* (Quick-RRT*), MQ-RRT* expands the set of possible parent nodes in the ChooseParent and Rewire phases, which reduces the path cost. On this basis, a method for creating a new parent node close to the obstacle is proposed. The creation process can be divided into two steps: Remove-tips and CreateNodes, which further reduces the cost of path generation and makes the path smoother by using the triangle inequality principle. Finally, numerical simulations are used to compare the proposed algorithm with RRT*, Q-RRT*, GuILD (Guided Incremental Local Densification), and F-RRT* (Fast-RRT*), which verifies that the proposed algorithm has certain advantages in path cost, convergence rate, and path smoothness.

GAO-RRT*: A path planning algorithm for mobile robot with low path cost and fast convergence

<abstract><p>Path planning is an essential research topic in the navigation of mobile robots. Currently, rapidly-exploring random tree star (RRT*) and its variants are known for their probabilistic completeness and asymptotic optimality, making them effective in finding solutions for many path planning problems. However, slow convergence rate of the RRT* limits its practical efficiency. To address this problem, this paper proposed an enhanced RRT* algorithm by refining the extension process of the exploring tree. This enhancement aims to guide the tree approaching to obstacles (GAO) while exploring toward the target point. First, GAO-RRT* employed a dual-weighted sample strategy instead of random sample to guide search direction of the exploring tree. Second, a variable step size extension strategy was adopted to increase the efficiency of node generation, balancing searching time and path safety in regions with different obstacles densities. Third, growth status of new nodes was monitored in real-time, and a reverse growth strategy was proposed to guide the exploring tree to escape local optima. In addition, parent node creation procedure for new nodes was used to produce a better initial path. Finally, the proposed GAO-RRT* was compared with three state of the art algorithms on 16 different instances of four representative environments. Compared to RRT*, Quick-RRT* (Q-RRT*), and Fast-RRT* (F-RRT*), the results showed that (1) the average path cost of initial solutions obtained by GAO-RRT* decreased by 38.32%, 29.69%, and 20.44%, respectively; and (2) the average convergence time of solution obtained by GAO-RRT* to suboptimal (1.05*$ C_{best} $) was reduced by 71.22%, 69.69%, and 58.37%, respectively. Simulation results indicated that GAO-RRT* outperforms the compared algorithms in terms of path cost and convergence speed.</p></abstract>

Automatic Navigation of a Microrobotic Swarm for Tracking Multiple Mobile Targets

Microrobotic swarms actuated by magnetic fields have attracted considerable attention in the field of automatic control. Developing effective control schemes for microrobotic swarms to track multiple mobile targets is still a major challenge. In this work, we propose an optimized rapidly-exploring random tree star (ORRT*) algorithm to generate desired paths connecting the swarm and the targets. A path selection strategy is developed to select the shortest path, and subsequently the immediate target to track is determined. The sliding mode (SM) motion controller is then presented to achieve the tracking of multiple mobile targets using microrobotic swarms. Comparisons are performed to validate the effectiveness of the control scheme, including the path planner and the motion controller. Finally, simulations and experiments of tracking multiple mobile targets in a virtual micromaze are conducted.

A Path Planning Algorithm of Inspection Robots for Solar Power Plants Based on Improved RRT*

In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is proposed. Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants.

Dynamic Path Planning and Motion Control of Microrobotic Swarms for Mobile Target Tracking

Magnetic field-driven microrobotic swarms have drawn extensive attention, especially in the field of automatic control. Realizing dynamic path planning and motion control of microrobotic swarms for mobile target tracking is one of the important tasks that still remains unsolved. In this paper, we firstly present an enhanced bidirectional rapidly-exploring random tree star (EB-RRT*) algorithm considering the physical size of the swarm to dynamically plan the optimal path for obstacle avoidance. An image-guided motion controller, which consists of a direction controller and a Genetic Algorithm based Linear Quadratic Regulator (GA-LQR) velocity controller, is then proposed to realize mobile target tracking using microrobotic swarms. Targeted bursting algorithm is subsequently developed to meet the requirement of tracking high-speed ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m/s$</tex-math> </inline-formula> ) mobile targets. Simulations are performed to validate the proposed methods and obtain the proper ranges of the input parameters for the controllers. Finally, the control effectiveness of mobile target tracking in different conditions and environments is validated by experimental results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to develop an effective control scheme for mobile target tracking using microrobotic swarms. Conventional control schemes mainly focus on the control of single microrobots to reach static targets, and thus the desired path is fixed once planned. In addition, the motion of single monolithic microrobots can be modelled precisely. However, in mobile target tracking using microrobotic swarms, dynamic planning algorithms are demanded to frequently update the desired path. Swarms consisting of millions of micro-agents are also difficult to be modelled due to the complex agent-agent interactions. In this work, an effective control scheme consisting of a dynamic path planner, a motion controller and a targeted bursting unit is developed. Real-time dynamic paths will be planned even though the positions of the swarm and the target change rapidly. The precise control of the swarm direction and velocity are achieved, and moreover, using the targeted bursting algorithm, the swarm can be accelerated to approach mobile targets accurately with higher efficiency. Experimental results validates the proposed tracking strategy in different environments with virtual obstacles. The proposed control scheme paves the way for a better understanding of advanced motion control methods for microrobotic swarms.

Real-Time Multi-Agent Mobile Robot based on A Client-Server Model

A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm. Index Terms— Real-time computer control, Mobile robot, Multi-agent system, Path planning algorithms.

Automatic parking trajectory planning based on random sampling and nonlinear optimization

The current parking trajectory planning algorithms based on geometric connections or formulation of optimization problems in automatic parking systems have strict requirements on the starting position, lower planning efficiency and discontinuous curvature of the reference trajectory. In order to solve these problems, a hierarchical planning algorithm which is combined with nonlinear optimization and the improved RRT* algorithm (Rapidly-exploring Random Tree Star) with Reeds-Shepp curve is proposed in this paper. First, the improved RRT*RS algorithm with the rapid repulsion-straddle experiment is designed for enhancing the efficiency of path planning. Second, because of the shortcomings of the Reeds-Shepp curve that can meet the minimum turning radius but not realize the continuous curvature of the path, a nonlinear optimization problem based on convex-set obstacle constraints is formulated and solved. Finally, simulation results show that the proposed parking trajectory planning algorithm in this paper can plan an effective parking trajectory with continuous curvature in different starting positions and multiple parking scenarios.

An Autonomous Parking System using the Hybridization of the Rapidly-Exploring Random Trees Star and Ant Colony System

One key autonomous driving car application that can be utilized to address the problem of parking spaces is autonomous parking. The goal of this study is to develop a path planning algorithm that can swiftly create a path for autonomous parking in a variety of car parking settings. The method employed is a mix of Rapidly Exploring Random Tree Star and Ant Colony Systems (RRT-ACS). The RRT-ACS method can quickly provide an ideal path. The reed sheep planner technique is also used to provide a smooth curving path that non-holonomic vehicles can follow. The performance of the RRT-ACS algorithm-based autonomous parking system is compared to that of the RRT*-connect and informed RRT*-connect algorithms. The simulation tests were performed in common parking settings such as parallel parking and vertical parking. The test results reveal that the suggested autonomous parking system outperforms other comparative algorithms. It is possible to infer that a route planning algorithm capable of rapidly designing a path for automatic parking in a variety of car parking circumstances has been successfully built. These findings may have ramifications for self-driving cars equipped with advanced driver support systems.

CCPF-RRT*: An improved path planning algorithm with consideration of congestion

Path planning is essential for robots to efficiently execute jobs in challenging settings. In this paper, we propose a new algorithm, called potential function-based sampling heuristic optimal path planning considering congestion (CCPF-RRT*), extending the standard Rapidly-exploring Random Tree star (RRT*) to address the issues of slow convergence, high path cost, and how to plan suitable paths in the presence of congested regions in the environment. First, the CCPF-RRT* algorithm adds the artificial potential field to RRT*, which can drastically cut down on the number of iterations and speed up convergence. Second, a movement cost function that takes congestion intensity and path length into account when evaluating a connection between two path nodes is created. This function is then used to direct the growth of new nodes and the updating of parent nodes so that the algorithm takes congestion intensity and path length into account when generating paths. Third, in order to reduce the path cost, the algorithm establishes parent nodes for random nodes by combining the benefits of the F-RRT* algorithm. The movement cost function is added to the process of establishing parent nodes to optimize the path cost in order to prevent the creation of parent nodes that force the path to traverse through crowded areas. In experimental simulation results, the algorithm outperforms RRT*, Q-RRT*, PQ-RRT*, and F-RRT* not only in terms of initial solution and quick convergence speed, but is also able to design an ideal path with the lowest movement cost in a crowded environment using the movement cost function.

Path Planning Method With Constant Bending Angle Constraint for Soft Growing Robot Using Heat Welding Mechanism

Soft growing robots, a new soft mobile robot, have recently attracted considerable interest. There are many soft growing robots, some of which have irreversible growing and bending motion mechanisms. For such robots, path planning methods that provide information on bending timing improve operation efficiency. Although various path planning methods have already been developed, they cannot be applied to our growing robot that uses a heat welding mechanism because it has a constraint that all bending angles are the constant value. This article proposes a novel path planning method with constant bending angle constraint. The proposed algorithm was developed based on the rapidly-exploring random tree star (RRT*) algorithm. The method incorporates an algorithm for reducing unnecessary nodes from obtained paths keeping bending angles constant and improving path optimality. We confirmed that the proposed method generates paths whose bending angles are constant. In addition, we experimented with moving our robot along the path in the field with some obstacles. The result showed that the proposed method enabled the robot to reach to target place, avoiding obstacles. The proposed method improves the operating efficiency of our soft growing robot.

ATS-RRT*: an improved RRT* algorithm based on alternative paths and triangular area sampling

The Rapidly Exploring Random Tree Star (RRT*) is a probabilistically complete algorithm. It is recognized as a better path planning algorithm, but its path quality and path planning speed still have room for improvement. This paper proposes an improved RRT* algorithm based on alternative paths and triangular area sampling (ATS-RRT*). The alternative paths strategy generates multiple initial paths based on whether the sample points can communicate with the target points and set the path with the smallest cost as the final initial path, which can speed up the initial path planning and improve the initial path finding rate. The triangular area sampling strategy combines every three adjacent nodes to generate some triangle areas and corresponding half-triangle areas. The path quality can be improved quickly by limiting the sampling in these triangle areas. In addition, the direct connection strategy with triangle nodes and the tabu table using in the Rewire process also speeds up the algorithm. Experiments show that the speed of finding the initial path and the success rate of finding the suboptimal path are improved by 2.3 and 1.45 times respectively compared with RRT*, Quick-RRT*, and Informed + Quick-RRT*.

Design of constrained dynamic path planning algorithms in large-scale 3D point cloud maps for UAVs

The technology of unmanned aerial vehicles (UAVs) has been growing rapidly in recent years. To achieve highly autonomous flight capability, collision-free path generation becomes a key research topic. However, for a large-scale terrain map, an efficient path searching algorithm is always a challenging problem. Based on the Goal-bias Rapidly-exploring Random Tree Star (GB-RRT*) together with the aid of a bidirectional search technique, in this paper, a new constrained path planning algorithm, called Parent Revisited Goal and Parent Bias RRT* (PRGPB-RRT*), is proposed. Three major objectives are considered as the development benchmark: 1). shorter total distance, 2). smoother flight path, and 3). high computational efficiency. To meet different task requirements, a dynamic path planning method is investigated to diminish the high costs due to large-scale environments and keep track of the changing destination by finding local target points. In addition, unknown object avoidance is an essential part of flight path planning. As a consequence, the Fitting-Octree is presented to partition the point clouds into multiple cubes to represent the obstacles precisely. Moreover, the method of grouping point clouds into clusters effectively increases the efficiency of the implementation under a large number of obstacles. Finally, the feasibility of the proposed method is verified by a real-world 3D large-scale point cloud map. Experiments show that the proposed method successfully enhances the path generation efficiency as well as the trajectory smoothness. Moreover, the presented PRGPB-RRT* can be applied to different intelligent UAVs subject to flight kinematics constrain.

An improved RRT* algorithm for robot path planning based on path expansion heuristic sampling

Rapidly-exploring Random Tree Star (RRT*) algorithm and its variants based on random sampling can provide a collision-free and asymptotic optimal solution for many path planning problems. However, many RRT* based variants have low sampling efficiency and slow convergence rate in the environment which consists of long corridors, due to a large number of iterations are required in sampling critical nodes. To overcome this problem, the paper proposes the Expanding Path RRT* (EP-RRT*) based on heuristic sampling in path expansion area. By combining the greedy heuristic of Rapidly exploring Random Tree (RRT)-Connect, EP-RRT* quickly explores the environment in order to find a feasible path, and then expands it to obtain the heuristic sampling area. It iteratively searches in the heuristic sampling area which also changes with the continuous optimization of the path, and finally obtains an optimal or suboptimal path connecting starting point and target point. Comparisons of EP-RRT* with RRT* and Informed RRT* in four simulation environments verify that EP-RRT* improves the node utilization, accelerates the convergence rate, and obtains a better path for the same number of iterations.

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

This study presents an efficient path planning method for mobile robots in a dynamic environment. The method is based on the rapidly-exploring random tree (RRT) algorithm. The two primary processes in mobile robot path planning in a dynamic environment are initial path planning and path re-planning. In order to generate a feasible initial path with fast convergence speed, we used a hybridization of rapidly-exploring random tree star and ant colony systems (RRT-ACS). When an obstacle obstructs the initial path, the path re-planner must be executed. In addition to the RRT-ACS algorithm, we proposed using a rule-template set based on the mobile robot in dynamic environment scenes during the path re-planner process. This novel algorithm is called RRT-ACS with Rule-Template Sets (RRT-ACS+RT). We conducted many benchmark simulations to validate the proposed method in a real dynamic environment. The performance of the proposed method is compared to the state-of-the-art path planning algorithms: RRT*FND and MOD-RRT*. Numerous experimental results demonstrate that the proposed method outperforms other comparison algorithms. The results show that the proposed method is suitable for the use on robots that need to navigate in a dynamic environment, such as self-driving cars.

Feedback RRT* algorithm for UAV path planning in a hostile environment

Unmanned aerial vehicle (UAV) path planning in a hostile environment (UPPHE) considers the features and information of defense systems to plan a risk-constrained shortest path for a UAV from the initial position to target. When the characteristics of defense systems cannot be accurately expressed manually, it will be brutal to effectively or efficiently implement the classical planning methods because of the lack of risk information to constrain and guide planning. We present a data-driven approach for UPPHE, the feedback rapidly-exploring random tree star algorithm (FRRT*), with a data-driven risk network and feedback module. FRRT* can use information extracted from situation data to constrain the growth of the random tree and make biased adjustments to improve planning efficiency. Simulation experiments verify the effectiveness of the risk network in guiding planning, and the improved efficiency brought by the feedback module.