RRT Algorithm Research Articles

Erratum: A Novel Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Improved APF and RRT Algorithm

Erratum: A Novel Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Improved APF and RRT Algorithm

Research and Application of Euler Cutting Path Based Optimisation Algorithm with Greedy-RRT Algorithm

With the development of social economy, mould processing enterprises have an increasingly strong demand for reducing costs and improving economic efficiency. The optimisation of steel plate cutting path is one of the key links to improve production efficiency. In this paper, the optimal cutting path problem of steel plate under different cutting tasks is investigated, and the optimal cutting path under complex geometries is found through various optimisation algorithms. For cutting task N3, the cutting path is divided into outer contour cutting, inner hole cutting and inner hole part cutting. According to the problem, all rectangular parts inside the ellipse need to be cut before the ellipse. Through the Eulerian cutting path optimisation algorithm, we solve the air-travel problem between the inner holes and the outer contour as well as the air-travel problem between the inner holes of the ellipse and the parts inside the ellipse. The total length of the finally determined optimal air-travel path is 97 units. By combining the greedy algorithm and the RRT algorithm, we determine the number and location of the bridges and design the optimal cutting path starting from the lower right corner of the steel plate. The final clearance of the bridges is 4 units, the total length of the combined optimal path clearance is 35.56 units, and the number of bridges is 8, which are located between adjacent rectangles.

A Novel Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Improved APF and RRT Algorithm

A Novel Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Improved APF and RRT Algorithm

Research Process of Path Planning based on RRT Algorithm and Its Improvements

Research Process of Path Planning based on RRT Algorithm and Its Improvements

Research on multi-vehicle formation control based on improved artificial potential field method

Multi-vehicle formation can perform various special tasks in unstructured environment. How to take into account the safety of vehicles in avoiding obstacles and the ability to maintain formation has a certain research value. In this paper, the four-circle model of vehicle is established first, and the circle radius is adjusted according to the state of vehicle, so as to describe the safety boundary of vehicle. The improved RRT algorithm is used for the whole route planning, and the discrete path points are used as vehicle guidance. Then the artificial potential field is constructed, and the formation coordination potential field is proposed, so that the vehicles can cooperate with other vehicles to keep the preset formation as far as possible when avoiding obstacles. Then the control quantity of the vehicle is calculated according to the force condition of the vehicle in the potential field by the double exponential sliding mode control method. Finally, the effectiveness of the method is verified by the simulation experiments of triangle formation and circular formation under different working conditions, and the formation error is reduced by about 20%.

Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm

Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm

Ground vehicle path planning on Uneven terrain Using UAV Measurement point clouds

Abstract. The objective of this study is to develop a system to support rapid ground vehicle activities by planning safe travel routes for ground vehicles from point clouds of wide-area uneven terrain environments measured using UAVs. However, fast path planning is difficult in complex environments such as large, uneven terrain environments. Therefore, this paper proposes a new RRT method based on the RRT algorithm that can perform fast path planning, even in complex environments. In the proposed method, narrow areas that are difficult to be explored by ordinary RRTs are first identified in advance, and nodes are placed in these areas to guide the search. When searching with RRTs, the tree is extended via these guide nodes to efficiently traverse the narrow area. In the validation of the proposed method, a comparison was made with RRT and RRT-Connect in two environments, including narrow areas. The results show that the proposed method has a higher route discovery capability, at least two times fewer search nodes and five times faster path planning capability than other RRTs.

Research on autonomous navigation system of greenhouse electric crawler tractor based on LiDAR.

The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor's information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6cm, and the maximum longitudinal deviation is 16cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13cm, the average lateral deviation is less than 7cm, and the standard lateral deviation is less than 8cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.

HB-RRT:A path planning algorithm for mobile robots using Halton sequence-based rapidly-exploring random tree

Path planning remains crucial for efficient robot operation. A Halton Biased Rapidly-exploring Random Tree (HB-RRT) path planning algorithm is introduced in this study. The Halton sequence, known for its uniform distribution and low discrepancy, is employed for sampling. Issues arising from the pseudo-random sequence in the standard RRT algorithm, leading to uneven distribution of sampling points, are addressed. A mouse-inspired goal-oriented strategy and a candidate sampling pool strategy are incorporated to enhance the sampling point quality, thereby addressing the challenge of insufficient memory during node expansion. Path optimization is further achieved through a multi-level planning approach, which aims to minimize redundancy. A subsequent smoothing of the path is conducted using a cubic B-spline method. Comparisons with the RRT, Bionic Target Bias-RRT, and Informed-RRT* algorithms, through both numerical simulations and real-world testing, confirm the superiority of the HB-RRT algorithm in terms of planning time, path length, and overall path quality.

Multi-objective continuous picking path planning for citrus based on TSP_RRT algorithm

Multi-objective continuous picking path planning for citrus based on TSP_RRT algorithm

RRT algorithm learning and optimization

LWith the increasing maturity of RRT algorithm, more and more fields are starting to use this algorithm. For example, in path planning problems, this algorithm has been well applied because it has good performance and real-time performance. The RRT algorithm is a path planning algorithm based on tree structure. It continuously explores unknown regions, finds feasible paths, and ultimately connects the starting point and target point by randomly expanding the nodes of the tree. The RRT algorithm has good fast exploration ability and low computational complexity, making it suitable for path planning problems in various environments. This article focuses on studying the parameters in various RRT algorithms. Through analysis and comparison, more reasonable parameters were ultimately found. This article also involves optimizing and improving the RRT algorithm using the RRT * algorithm. The research in this article can further understand the application scenarios of the RRT algorithm. It is expected that this algorithm will be better applied in the field of autonomous driving in the future.

A Motion Planning Method for 6-DOF Mechanical Arm based on Improved RRT Algorithm

A Motion Planning Method for 6-DOF Mechanical Arm based on Improved RRT Algorithm

Path planning algorithms of sweeping robots

Different categories of path planning algorithms for sweeping robot are introduced, including Dijkstra algorithm and A*Algorithm in Traditional path-planning Algorithm, PRM Algorithm and RRT Algorithm in sampling algorithm, and Ant Colony Optimization Algorithms and Genetic algorithms in Intelligent bionic algorithm. Each algorithm has its principles and features introduced. At the same time, several algorithms are compared, and summarized, each algorithm has its advantages and disadvantages, in the future development should be combined with their strengths to optimize the path planning algorithm of the sweeping robot.

Multigene and Improved Anti-Collision RRT* Algorithms for Unmanned Aerial Vehicle Task Allocation and Route Planning in an Urban Air Mobility Scenario.

Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks' completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed.

Review of Intelligent Ship Path Planning Algorithms

Intelligent ship route planning has become an important research direction in the field of shipping in recent years. This paper first provides an overview of the basic theory of intelligent ship route planning. Secondly, various intelligent ship route planning algorithms are introduced, including methods based on A* algorithm, artificial potential field algorithm, RRT algorithm, and reinforcement learning. These algorithms analyze information such as ocean environment, predict sea conditions and traffic conditions, and consider ship dynamics and navigation safety constraints to provide efficient and safe navigation routes for ships. Finally, this paper points out the key issues and future development directions in intelligent ship route planning. The continuous innovation and application of intelligent ship route planning algorithms will provide more intelligent and efficient ship transportation services for the shipping industry, promoting the sustainable development of the shipping industry.

An Intelligent Obstacle Avoidance Method for Six Degree of Freedom Robotic Arm Based on Assembly Sequence Planning

Abstract Autonomous operation planning of robotic arms under complex task constraints is still a difficult problem in the current research in this direction, and the existing operation planning methods are facing challenges and urgently need to break through the technical bottleneck. This paper proposes an improved RRT algorithm for six-degree-of-freedom robotic arms, which can be used for obstacle avoidance path planning. Based on the assembly sequence planning method, combining the advantages of RRT and RRT-connect algorithm, applying the idea of target sampling to strengthen the convergence of the algorithm to the target point search, introducing the greedy algorithm to improve the efficiency of the algorithm, combining with the five times of the B-spline interpolation on the path of the smoothing optimization, to shorten the planning path time and length. The Robotics Toolbox toolkit in MATLAB is used to count the time, number of samples, and path length required for the planning of RRT and the improved RRT algorithm, and the dynamic planning library is used to configure the algorithm and verify the path planning effect of the improved algorithm. Compared with the traditional RRT algorithm, using the proposed SDPS-RRT Connect algorithm for six-degree-of-freedom robotic arm path planning, the minimum distance of the connecting rod from the obstacle is 51.4948cm, which is greater than the standard safety distance of 45cm, which verifies the feasibility of the algorithm in practical applications.

Automatic Dual Crane Cooperative Path Planning Based on Multiple RRT Algorithm for Narrow Path Finding Scenario

Automatic Dual Crane Cooperative Path Planning Based on Multiple RRT Algorithm for Narrow Path Finding Scenario

Path Planning for Robotic Arms Based on an Improved RRT Algorithm

Path Planning for Robotic Arms Based on an Improved RRT Algorithm

Method for planning the way of UGV using a modification of dynamic bi-directional RRT algorithm.

The path planning problem of unmanned autonomous ground vehicles has always been an acute problem in the field of autonomous ground robotic systems research. From the point of view of the complexity of the tasks during the conduct of combat operations in the urbanized space of the densely built-up city with a constantly changing landscape, the tasks entrusted to the UGV are constantly becoming more difficult, and the scenarios of the use of the UGV demonstrate a diversified trend in the development of new management and control systems. The rapidity of conducting modern military operations in an urbanized space appears as a complex and multifaceted task. Considering the complexity of the UGV movement process, at the current stage of the development of robotic systems, the general trend is to abandon remote control of robotic complexes with the transition to automatic modes, which requires the development and implementation of algorithms for automatic interaction and movement of military mobile robotic systems. To solve the problems of the Rapidly-Exploring Random Tree *Fixed Nodes algorithm regarding its low speed for obtaining track junctions and the impossibility of using it in a dynamic environment when planning a UGV path. To solve the problem of accelerating the acquisition of collision-free paths in real time in two-dimensional space, it is proposed to apply a modified dynamic bidirectional RRT* algorithm with reference nodes. The algorithm is a modification of Rapidly-Exploring Random Tree *Fixed Nodes using a bidirectional greedy search method to speed up and solve the problem of the unidirectional Rapidly-Exploring Random Tree algorithm regarding its slow search speed, as well as the difficulties of decision making in a narrow environment caused by blind random sampling. In the case of dynamic disturbance movement, taking advantage that reference nodes do not require much calculation in planning, in the process of iterative path optimization, the proposed algorithm updates the map information in real time and repairs the damaged output path to complete the dynamic path planning.

Researches advanced in path planning to indoor fire escape and rescue based on SLAM

Helping trapped people understand the external situation and provide navigation to escape the fire is the key to reducing fire casualties. Thanks to the rapid development of artificial intelligence technology, the combination of simultaneous localization and mapping (SLAM) and path planning technology has gradually become a new research hotspot, which can help provide on-site fire information, maps and navigation for trapped people and firefighters. However, improving the accuracy of SLAM techniques under harsh conditions (e.g., thick smoke, high temperature) is still an open topic. Focusing on SLAM noise reduction and path planning, in this paper, we detail the latest research progress of SLAM technology in fire escape assistance. Specifically, we first introduce the current development and application frontiers of SLAM technology, and then analyze and compare the application of SLAM technology in fire scenarios. In addition, the performance changes of the two continuous A* algorithms and the RRT algorithm during global path planning for fire scenarios are compared. Finally, we discuss the development trend of SLAM in future fire escape and rescue.