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Development of an Improved rapidly exploring random trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

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SENSORS 2021年第6期21卷 2244-2244页

作者： Yang, S. M. Lin, Y. A. Natl Cheng Kung Univ Dept Aeronaut & Astronaut Tainan 70101 Taiwan

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the rapidly exploring random trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a "near"-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional-integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

关键词： autonomous vehicle obstacle avoidance path planning rapidly exploring random trees

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Hybridizing rapidly exploring random trees and Basin Hopping Yields an Improved Exploration of Energy Landscapes

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2016年第8期37卷 739-752页

作者： Roth, Christine-Andrea Dreyfus, Tom Robert, Charles H. Cazals, Federic Univ Paris Diderot Sorbonne Paris Cite CNRS Lab Biochim TheorUPR 9080 13 Rue Pierre & Marie Curie F-75005 Paris France

The number of local minima of the potential energy landscape (PEL) of molecular systems generally grows exponentially with the number of degrees of freedom, so that a crucial property of PEL exploration algorithms is their ability to identify local minima, which are low lying and diverse. In this work, we present a new exploration algorithm, retaining the ability of basin hopping (BH) to identify local minima, and that of transition based rapidly exploring random trees (T-RRT) to foster the exploration of yet unexplored regions. This ability is obtained by interleaving calls to the extension procedures of BH and T-RRT, and we show tuning the balance between these two types of calls allows the algorithm to focus on low lying regions. Computational efficiency is obtained using state-of-the art data structures, in particular for searching approximate nearest neighbors in metric spaces. We present results for the BLN69, a protein model whose conformational space has dimension 207 and whose PEL has been studied exhaustively. On this system, we show that the propensity of our algorithm to explore low lying regions of the landscape significantly outperforms those of BH and T-RRT. (c) 2015 Wiley Periodicals, Inc.

关键词： energy landscape exploration sampling local minima enumeration basin hopping rapidly exploring random trees high dimensional spaces exploration

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Planning with ants: Efficient path planning with rapidly exploring random trees and ant colony optimization

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2016年第5期13卷 1-16页

作者： Viseras, Alberto Losada, Rafael Ortiz Merino, Luis German Aerosp Ctr DLR Inst Commun & Nav Oberpfaffenhofen Germany Univ Pablo de Olavide Seville Spain

rapidly exploring random trees (RRTs) have been proven to be efficient for planning in environments populated with obstacles. These methods perform a uniform sampling of the state space, which is needed to guarantee the algorithm's completeness but does not necessarily lead to the most efficient solution. In previous works it has been shown that the use of heuristics tomodify the sampling strategy could incur an improvement in the algorithm performance. However, these heuristics only apply to solve the shortest path-planning problem. Here we propose a framework that allows us to incorporate arbitrary heuristics tomodify the sampling strategy according to the user requirements. This framework is based on 'learning from experience'. Specifically, we introduce a utility function that takes the contribution of the samples to the tree construction into account;sampling at locations of increased utility then becomes more frequent. The idea is realized by introducing an ant colony optimization concept in the RRT/RRT* algorithm and defining a novel utility function that permits trading off exploitation versus exploration of the state space. We also extend the algorithm to allow an anytime implementation. The scheme is validated with three scenarios: one populated with multiple rectangular obstacles, one consisting of a single narrow passage and a maze-like environment. We evaluate its performance in terms of the cost and time to find the first path, and in terms of the evolution of the path quality with the number of iterations. It is shown that the proposed algorithm greatly outperforms state-of-the-artRRT and RRT* algorithms.

关键词： Mobile robots autonomous agents motion and path planning rapidly exploring random trees ant colony optimization bio-inspired robotics

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rapidly exploring random trees-based Initialization of MPC Technique Designed for Formations of MAVs

Rapidly Exploring Random Trees-based Initialization of MPC T...

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International Conference on Informatics in Control, Automation and Robotics

作者： Zdenek Kasl Martin Saska Libor Preucil Department of Cybernetics Czech Technical University

ISBN: (纸本)9781479979202

Motion planning techniques suited for initialization of Model Predictive Control based methodology applied for complex maneuvering and stabilization of formations of Micro Aerial Vehicles are proposed in this paper. Two approaches to initialization of the formation driving method will be described, experimentally verified, evaluated and compared. The first proposed method is based on multiobjective optimization of the trajectory guess obtained by a rapidly exploring random trees technique. It represents an easy to implement and robust method suited for off-line initialization of the formation driving algorithm. The second proposed method is based on sequential processing of parts of the obtained trajectory. This method is well scalable and thus applicable in large workspaces with complex obstacles. In addition, the second method enables a significant reduction of computational time as is shown by comparison of series of simulations in different environments.

关键词： Trajectory Planning Model Predictive Control Micro Aerial Vehicles rapidly exploring random trees

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Comparative analysis of popular mobile robot roadmap path-planning methods

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ROBOTICA 2025年

作者： Ayawli, Ben Beklisi Kwame Dawson, John Kwao Badu, Esther Ayawli, Irene Esinam Beklisi Lamusah, Dawda Sunyani Tech Univ Dept Comp Sci Sunyani Ghana Sunyani Tech Univ ICT Directorate Sunyani Ghana Sunyani Tech Univ Dept Pharm Sunyani Ghana

Global path planning using roadmap (RM) path-planning methods including Voronoi diagram (VD), rapidly exploring random trees (RRT), and probabilistic roadmap (PRM) has gained popularity over the years in robotics. These global path-planning methods are usually combined with other path-planning techniques to achieve collision-free robot control to a specified destination. However, it is unclear which of these methods is the best choice to compute the efficient path in terms of path length, computation time, path safety, and consistency of path computation. This article reviewed and adopted a comparative research methodology to perform a comparative analysis to determine the efficiency of these methods in terms of path optimality, safety, consistency, and computation time. A hundred maps of different complexities with obstacle occupancy rates ranging from 50.95% to 78.42% were used to evaluate the performance of the RM path-planning methods. Each method demonstrated unique strengths and limitations. The study provides critical insights into their relative performance, highlighting application-specific recommendations for selecting the most suitable RM method. These findings contribute to advancing robot path-planning techniques by offering a detailed evaluation of widely adopted methods.

关键词： voronoi diagram rapidly exploring random trees probabilistic roadmap roadmap mobile robot path planning

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Automated mission planning for aerial large-scale power plant thermal inspection

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JOURNAL OF FIELD ROBOTICS 2024年第5期41卷 1313-1348页

作者： Romanov, Alexey M. Gyrichidi, Ntmitrii Volkova, Maria A. Eroshenko, Stanislav A. Matrenin, Pavel V. Khalyasmaa, Alexandra I. MIREA Russian Technol Univ Inst Artificial Intelligent Moscow 119454 Russia Ural Fed Univ Ural Power Engn Inst Ekaterinburg Russia

Close-range aerial inspection of large-scale industrial facilities such as power plants is complex. It is characterized by a few areas suitable for safe landing, many obstacles, complicated radio communication, and so forth. The paper presented a new offline approach for automated mission planning in such conditions. It is based on novel concepts of pi ${\rm{\Pi }}$-shaped flight profile and safe altitude map. These concepts reduce the mission planning task to a multiple traveling salesmen problem on a two-dimensional map, which is solved using rapidly exploring random tree and genetic algorithms. A computational fluid dynamics-based local turbulence map concept was proposed to avoid turbulence zones during inspection flights. As an advantage, this map can be generated at least 6.8x faster than the known approaches. Our new multicopter flight time prediction model provides an error of less than 10% in most cases and significantly outperforms those implemented in commercial mission planning software. All the proposed solutions were verified during a 2400-MW power plant inspection. Our algorithms managed to plan to visit all the inspection points in six routes, approximately 15 min long each. This mission set has a comparable overall duration but better safety and even battery usage compared with the routes planned by a qualified operator. Moreover, the designed aerial inspection system provides higher quality images than manually from the ground using a thermal camera with two times higher sensitivity and equipped with more advanced optics. To our knowledge, this research is the first to report the successful implementation of autonomous unmanned aerial vehicle-based power equipment diagnostics on a large-scale thermal power plant.

关键词： computational fluid dynamics drones energy mission planning rapidly exploring random trees turbulence unmanned aerial vehicles

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Design and Implementation of a Hybrid Path Planning Approach for Autonomous Lane Change Manoeuvre

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2024年第1期25卷 83-95页

作者： Skackauskas, Paulius Karpenko, Mykola Prentkovskis, Olegas Vilnius Gediminas Tech Univ Fac Transport Engn Dept Mobile Machinery & Railway Transport LT-10105 Vilnius Lithuania

Autonomous vehicles are currently on their way and they have already started to gradually alter the fundamentals of the entire transport system. However, while in relatively near future, seeking to use their full potential during various urban traffic scenarios, further developments must be done in such fields as the sensing and perception, path planning and control. This research work focuses on the path planning problems and proposes a novel hybrid path planning approach, specifically designed for an important and complex urban scenario-the lane change manoeuvre. The proposed hybrid path planner consists of two classical path planning approaches: the Bezier curves approach, which served as a primary planner, and the rapidly exploring random trees approach, which was used as a supplementary planner. The entire development process of the hybrid path planner is described systematically and in detail. To verify the efficiency of the proposed hybrid path planner, experimental test drives, while using an autonomous research vehicle, were performed. The experimental results indicated that the real-time performance of the proposed hybrid path planning approach is effective and reliable. The path generated by the proposed hybrid approach is feasible, continuous, and can be executed by the autonomous vehicle.

关键词： Autonomous driving Path planning Hybrid Lane change Bezier curve rapidly exploring random trees

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A novel directional sampling-based path planning algorithm for ambient intelligence navigation scheme in autonomous mobile robots

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2023年第3期15卷 269-284页

作者： Ganesan, Sivasankar Natarajan, Senthil Kumar Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India

Path planning algorithms determine the performance of the ambient intelligence navigation schemes in autonomous mobile robots. Sampling-based path planning algorithms are widely employed in autonomous mobile robot applications. RRT*, or Optimal rapidly exploring random trees, is a very effective sampling-based path planning algorithm. However, the RRT* solution converges slowly. This study proposes a directional random sampling-based RRT* path planning algorithm known as DR-RRT* to address the slow convergence issue. The novelty of the proposed method is that it reduces the search space by combining directional non-uniform sampling with uniform sampling. It employs a random selection approach to combine the non-uniform directional sampling method with uniform sampling. The proposed path planning algorithm is validated in three different environments with a map size of 384*384, and its performance is compared to two existing algorithms: RRT* and Informed RRT*. Validation is carried out utilizing a TurtleBot3 robot with the Gazebo Simulator and the Robotics Operating System (ROS) Melodic. The proposed DR-RRT* path planning algorithm is better than both RRT* and Informed RRT* in four performance measures: the number of nodes visited, the length of the path, the amount of time it takes, and the rate at which the path converges. The proposed DR-RRT* global path planning algorithm achieves a success rate of 100% in all three environments, and it is suited for use in all kinds of environments.

关键词： Ambient intelligence navigation scheme directional sampling path planning algorithms rapidly exploring random trees autonomous mobile robot

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Intermediary RRT*-PSO:A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

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Computers, Materials & Continua 2023年第8期76卷 2281-2300页

作者： Loc Q.Huynh Ly V.Tran Phuc N.K.Phan Zhiqiu Yu Son V.T.Dao School of Industrial Engineering and Management International UniversityVietnam National University HCMCHo Chi Minh City700000Vietnam Department of Industrial Management National Taiwan University of Science and TechnologyTaipei City106335Taiwan

Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with *** this paper,we propose a novel path planning algorithm-Intermediary RRT*-PSO-by utilizing the exploring speed advantages of rapidly exploring random trees and using its solution to feed to a metaheuristic-based optimizer,Particle swarm optimization(PSO),for fine-tuning and *** Phase 1,the start and goal trees are initialized at the starting and goal positions,respectively,and the intermediary tree is initialized at a random unexplored region of the search *** trees were grown until one met the other and then merged and re-initialized in other unexplored *** the start and goal trees merge,the first solution is found and passed through a minimization process to reduce unnecessary *** 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the *** simulating two special benchmark configurations and six practice configurations with special cases,the results of the study concluded that the proposed method is capable of handling small to large,simple to complex continuous environments,whereas it was very tedious for the previous method to achieve.

关键词： Motion planning global path planning rapidly exploring random trees particle swarm optimization

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Target-biased informed trees: sampling-based method for optimal motion planning in complex environments

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第2期9卷 755-771页

作者： Wang, Xianpeng Ma, Xinglu Li, Xiaoxu Ma, Xiaoyu Li, Chunxu Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China Hohai Univ Coll Mech & Elect Engn Changzhou 213000 Jiangsu Peoples R China

Aiming at the problem that the progressively optimized rapidly-exploring random trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm;after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.

关键词： path planning rapidly exploring random trees improved RRT target bias heuristic

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