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Efficient and High-Quality Path Planning Guided by Map Information for Mobile Robots

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年

作者： Fan, Wenpei Wang, Yaonan Liu, Licheng Chen, Yanjie Tan, Haoran Miao, Zhiqiang Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410082 Peoples R China Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Wales

Path planning of mobile robots plays an important role in improving work efficiency in the manufacturing industry. On one hand, the sampling-based path planning methods commonly used in mobile robots require extensive and time-consuming iterative searches to find a relatively optimal path due to their stochastic nature. On the other hand, the map information obtained during planning is solely utilized for collision detection without being effectively leveraged. This article proposes a map-informed rapidly random-exploring tree (MIRRT) algorithm that combines uniform sampling, rapidly crossing obstacles (RCO) and progressive optimization. It can utilize map information to quickly guide the search tree. MIRRT involves a preprocessing step that requires segmenting concave obstacles into convex obstacles to prevent path loss. During the growth of the search tree, the RCO method guided by map information can rapidly discover high-quality ground path nodes and efficiently navigate obstacles when they block the path. Finally, simulations and physical experiments were performed to prove that MIRRT significantly outperforms existing state-of-the-art sampling-based planners designed specifically for mobile robots.

关键词： Path planning Planning Mobile robots Classification algorithms Robot sensing systems Heuristic algorithms Hands Deep learning Convergence Fans Dynamic planning map information mobile robots path planning rapidly exploring random tree

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

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2025年第2期26卷 451-461页

作者： Zhao, Shuen Leng, Yao Zhao, Maojie Wang, Kan Zeng, Jie Liu, Wanli Chongqing Jiaotong Univ Sch Mechatron & Vehicle Engn Chongqing 400074 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China China Merchants Testing Vehicle Technol Res Inst C Chongqing 401329 Peoples R China Chongqing Key Lab Ind & Informatizat Automot Act S Chongqing 401329 Peoples R China

To satisfy multi-objective requirements of the dynamic lane-changing trajectory planning (DLTP) for autonomous vehicles, a novel DLTP method based on the improved artificial potential field (APF) and rapidly exploring random tree (RRT) algorithm is proposed. The problem of lane-changing trajectory planning can be decoupled into trajectory shape planning and speed planning. First, the Frenet coordinate system is employed to transform the planning trajectory on curved roads to that on straight roads. Second, based on sinusoidal obstacle avoidance lane-changing, the potential field of virtual obstacle points at the road boundary is established by integrating information on the position and state of surrounding vehicles. The improved APF algorithm is utilized to plan the shape of the lane-changing trajectory. Then, the motion states of surrounding vehicles are mapped to the obstacle region in the space-time graph, transforming speed planning into a path-searching problem. The efficiency of the RRT algorithm is improved by increasing the heuristic information of the lane-changing endpoint and the multi-objective constraints of the random sampling region. Finally, simulation results validate that the proposed method can plan a smooth lane-changing trajectory, effectively avoid collisions with surrounding vehicles, and ensure real-time stability of the lane-changing process.

关键词： Autonomous vehicles Lane-changing Trajectory planning Artificial potential field rapidly exploring random tree

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Spatially perceptual path planning for AUVs in uncharted marine environments using rolling window strategy

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Meng, Wenlong Pu, Yanbo Li, Yujing Wang, Jinglin Gong, Ya Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Shandong Univ Marine Coll Weihai 264209 Peoples R China

Autonomous underwater vehicles (AUVs) are increasingly integral to the in-depth exploration of oceanic environments. Efficiently identifying optimal routes, particularly within uncharted marine domains, is paramount for AUVs when addressing path-planning challenges. Existing path planning algorithms relying on underwater sensing equipment frequently encounter challenges in achieving efficient exploration while generating high- quality trajectories. In this paper, we introduce an innovative method aimed at efficiently generating optimal paths for AUV navigation within the constraints of limited information. In detail, given the uncertain marine environment, we transform the global one-time path planning problem into multiple iterations of local planning through a rolling window strategy. Within each successive rolling window, utilizing sensor-detected environmental data, we present a novel variant of the RRT* algorithm denoted as circle-RRT*. This algorithm is designed to determine the high-precision sub-paths efficiently. In the circle-RRT* procedure, the integration of adaptive circle sampling and the rewiring process from traditional RRT* leads to a notable reduction in redundant sampling points and modifies the connectivity relationships of the RRT tree. Consequently, these improvements enhance the efficiency of local path planning, concurrently leading to a substantial decrease in path length. We rigorously assessed the performance of our algorithm in uncharted environments via extensive simulations and conducted comparative analyses against established state-of-the-art methods.

关键词： Path planning Autonomous undersea vehicle Uncharted marine environments rapidly exploring random tree Artificial potential field

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FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

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ROBOTICA 2024年第3期42卷 644-659页

作者： Xiao, Guangzhou Zhang, Lixian Wu, Tong Han, Yuejiang Ding, Yihang Han, Chengzhe Harbin Inst Technol Sch Astronaut Harbin Peoples R China

This paper is concerned with the problem of collision-free path planning for manipulators in multi-obstacle scenarios. Aiming at overcoming the deficiencies of existing algorithms in excessive time consumption and poor expansion quality, a path planning algorithm named Fast Bi-directional rapidly-exploring random tree (FBi-RRT) with novel heuristic node expansion is proposed, which includes a selective-expansion strategy and a vertical-exploration strategy. The selective-expansion strategy is designed to guide the selection of the nearest-neighbor node to avoid the repeated expansion failure, thereby shortening the overall planning time. Also, the vertical-exploration strategy is developed to regulate the expansion direction of the collision nodes to escape from the obstacle space with less blindness, thus improving the expansion quality and further reducing time cost. Compared with previous planning algorithms, FBi-RRT can generate a feasible path for manipulators in a drastically shorter time. To validate the effectiveness of the proposed heuristic node expansion, FBi-RRT is conducted on a 6-DOF manipulator and tested in five scenarios. The experimental results demonstrate that FBi-RRT outperforms the existing methods in time consumption and expansion quality.

关键词： path planning rapidly exploring random tree robotic manipulator sampling-based algorithm time consumption

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Path planning of a 5-dof robotic arm based on BiRRT-APF algorithm considering obstacle avoidance

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第16期236卷 9282-9292页

作者： Zhang, Ning Cui, Chuangchuang Wu, Guanglei Dalian Univ Technol Sch Mech Engn 2 Linggong RdHigh Tech Pk Dist Dalian 116024 Peoples R China

In robotic applications, an efficient path planning in a prescribed environment is the central issue, which is a challenging task for multiple degree-of-freedom articulated robotic arms. In this light, the path planning for robotic manipulators based on the combined artificial potential field method and bidirectional rapidly exploring random tree (BiRRT-APF) algorithm is proposed in this paper, aiming to handle the problems of highly possible randomness and low search efficiency. In order to improve the attraction function to speed up the efficiency of searching path, the characteristics of the Bi-RRT algorithm (i.e., bi-directional growth of random trees), is integrated to reduce the search time. Subsequently, a path construction strategy is proposed to remove the redundant nodes of the path, for the increased smoothness of the path and reduced memory storages of the algorithm. The effectiveness of the proposed path planning algorithm is illustrated with a 5-dof robotic arm, and is verified with MATLAB (TM) and CoppeliaSim (TM) co-simulation, through a comparative study among different algorithms. The results show that the proposed BiRRT-APF algorithm can decrease the randomness of node growth and reduce the number of algorithm iterations, resulting in faster convergence of a smooth path for reduced computational burden.

关键词： path planning obstacle avoidance artificial potential field rapidly exploring random tree robotic arm

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Sampling-Based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2019年第3-4期93卷 763-785页

作者： Vonasek, Vojtech Jurcik, Adam Furmanova, Katarina Kozlikova, Barbora Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 6 Czech Republic Masaryk Univ Fac Informat Bot 68a Brno 60200 Czech Republic

Proteins are involved in many biochemical processes. The behavior of proteins is highly influenced by the presence of internal void space, in literature denoted as tunnels or cavities. Tunnels are paths leading from an inner protein active site to its surface. The knowledge about tunnels and their evolution over time, captured in molecular dynamics simulations, provides an insight into important protein properties (e.g., their stability or activity). For each individual snapshot of molecular dynamics, tunnels can be detected using Voronoi diagrams and then aggregated over time to trace their behavior. However, this approach is suitable only when a given tunnel is detected in all snapshots of molecular dynamics. This is often not the case of traditionally used approaches to tunnel computation. When a tunnel becomes too narrow in a particular snapshot, the existing approaches cannot detect this case and the tunnel completely disappears from the results. On the other hand, this situation can be quite common as tunnels move, disappear and appear again, split, or merge. Therefore, in this paper we propose a method which enables to trace also tunnels in those missing snapshots. We call them dynamic tunnels and we use the sampling-based motion planning to compute them. The rapidly exploring random tree (RRT) algorithm is used to explore the void space in each frame of the protein dynamics. The void space is represented by a tree structure that is transferred to the next frame of the dynamics and updated to remove collisions and to cover newly emerged free regions of the void space. If the void space reaches the surface of the protein, a dynamic tunnel is reconstructed by tracking back in the tree towards a desired place (i.e., the active site). To efficiently sample the narrow void space inside proteins, a Voronoi diagram of the static protein frames is used. The results of the proposed method are demonstrated on an exemplary dataset obtained from the domain experts and th

关键词： Sampling-based motion planning rapidly exploring random tree Protein tunnel detection

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Virtual target point-based obstacle-avoidance method for manipulator systems in a cluttered environment

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ENGINEERING OPTIMIZATION 2020年第11期52卷 1957-1973页

作者： Long, Zhang Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China

This article presents an obstacle-avoidance method for manipulator systems working in a cluttered environment. Based on the concept of the artificial potential field, a velocity potential field method is proposed, with the attractive velocity function designed in configuration space and the repulsive velocity function designed in Cartesian space. The attractive and repulsive velocities calculated by the local information together drive the manipulator to reach the target point without colliding with obstacles. To overcome the local minima problem, virtual target points are selected based on the rapidly exploring random trees method, which can consider the global information. The manipulator reaches the virtual target points one by one until the desired target is reached. The proposed method considers both global and local information and can significantly increase the probability of achieving a feasible and continuous trajectory for manipulator systems in cluttered environments. The simulation results verify the effectiveness of the proposed method.

关键词： Manipulator system obstacle avoidance virtual target points velocity potential field rapidly exploring random tree

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randomized path planning for tasks requiring the release and regrasp of objects

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ADVANCED ROBOTICS 2016年第4期30卷 270-283页

作者： Kim, Jinkyu Ko, Inyoung Park, Frank C. Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 151742 South Korea Samsung Elect Co Ltd Global Technol Ctr Suwon 443742 South Korea

This paper presents a randomized planning algorithm for manipulation tasks that require the robot to release and regrasp an object in different robot postures. Such problems arise, for example, in robotic suturing and knot tying, and in assembly tasks where parts must be guided through complex environments. Formulating the problem as one of planning on a foliated manifold, we present a randomized planning algorithm that, unlike existing methods, involves sampling and tree propagation primarily in the task space manifold;such an approach significantly improves computational efficiency by reducing the number of projections to the constraint manifold, without incurring any significant increases in the number of release-regrasp sequences. We also propose a post-processing topological exploration algorithm and path refinement procedure for reducing the number of release-regrasp sequences in a solution path, independent of the algorithm used to generate the path. Experiments involving spatial open chains with up to 10 degrees of freedom, operating in complex obstacle-filled environments, show that our algorithm considerably outperforms existing algorithms in terms of computation time, path length, and the number of release-regrasp operations.

关键词： Motion planning sampling-based planning rapidly exploring random tree release-regrasp foliation

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A Novel Goal-oriented Sampling Method for Improving the Convergence Rate of Sampling-based Path Planning for Autonomous Mobile Robot Navigation

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DEFENCE SCIENCE JOURNAL 2023年第3期73卷 322-331页

作者： Ganesan, Sivasankar Natarajan, Senthil Kumar Thondiyath, Asokan Mepco Schlenk Engn Coll Sivakasi 626005 Tamil Nadu India Indian Inst Technol Madras Chennai 600036 Tamil Nadu India

Autonomous Mobile Robots' performance relies on intelligent motion planning algorithms. In autonomous mobile robots, sampling-based path-planning algorithms are widely used. One of the efficient sampling-based path planning algorithms is the rapidly exploring random tree (RRT). However, the solution provided by RRT is suboptimal. An RRT extension known as RRT* is optimal, but it takes time to converge. To improve the RRT* slow convergence problem, a goal-oriented sampling-based RRT* algorithm known as GS-RRT* is proposed in this paper. The focus of the proposed research work is to reduce unwanted sample exploration and solve the slow convergence problem of RRT* by taking more samples in the vicinity of the goal region. The proposed research work is validated in three different environments with a map size of 384*384 and compared to the existing algorithms: RRT, Goal-directed RRT(G-RRT), RRT*, and Informed-RRT*. The proposed research work is compared with existing algorithms using four metrics: path length, time to find the solution, the number of nodes visited, and the convergence rate. The validation is done in the Gazebo Simulation and on a TurtleBot3 mobile robot using the Robotics Operating System (ROS). The numerical findings show that the proposed research work improves the convergence rate by an average of 33 % over RRT* and 27 % over Informed RRT*, and the node exploration is 26 % better than RRT* and 20% better than Informed RRT*.

关键词： Goal-oriented sampling Mobile robot navigation Path planning algorithm rapidly exploring random tree

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Towards comfort-optimal trajectory planning and control

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VEHICLE SYSTEM DYNAMICS 2019年第8期57卷 1108-1125页

作者： Mischinger, Marlies Rudigier, Martin Wimmer, Peter Kerschbaumer, Andreas Virtual Vehicle Res Ctr Graz Austria

This paper describes a method to analyse and evaluate different trajectory planning methods and controller types for usage in automated vehicles. Its application is shown by using a novel trajectory planning approach considering comfort aspects (based on rapidly exploring random tree (RRT)), two different controllers to follow the planned path (cascade controller and flatness based controller) and a simulation method to obtain resulting lateral vehicle accelerations. The method is used to plan and drive a trajectory through a roundabout. It can be seen that the lateral accelerations of the controller-driven vehicle are in the range of the values used for planning. However, the results of both controllers show differences in lateral deviation and in smoothness of lateral accelerations. The simulation results are then compared to real-world test drives in the same roundabout. The measured lateral accelerations are in the same range as well but show a smoother progression than the two controller models.

关键词： rapidly exploring random tree trajectory planning motion primitives comfort criteria

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