Rapidly-exploring Random Tree Research Articles

A SAC-Bi-RRT Two-Layer Real-Time Motion Planning Approach for Robot Assembly Tasks in Unstructured Environments

Due to the uncertainty and complexity of the assembly process, the trajectory planning of a robot needs to consider the real-time obstacle avoidance problem when it completes the assembly in the unstructured workspace. To realize the safe assembly of assembly robots in dynamic and complex environments, a dynamic obstacle avoidance trajectory planning method for robots combining traditional planning algorithms and deep reinforcement learning algorithms is proposed to improve the robot’s agent and obstacle avoidance ability in dynamic and complex environments. The Bidirectional Rapidly-exploring Random Tree (Bi-RRT) method is utilized as a global planner to plan the global optimal path quickly; considering the real-time nature of the assembly process, the Soft Actor-Critic (SAC) is used as a local obstacle avoider to avoid obstacles more accurately and to find the nearest node generated by the Bi-RRT during the planning process, which is regarded as the goal during the local obstacle avoidance to reduce the model’s complexity. By training and testing in the simulation engine and comparing with SAC, DDPG and DQN algorithms, the method can avoid obstacles in dynamic and complex environments more efficiently, which verifies that the proposed hybrid method can accomplish the high-precision planning task with a high success rate.

Dynamic Target Hunting Under Autonomous Underwater Vehicle (AUV) Motion Planning Based on Improved Dynamic Window Approach (DWA)

A dynamic distributed target hunting method is proposed for the problem of distributed moving target hunting by multiple Autonomous Underwater Vehicles (AUVs). By integrating the improved Dynamic Window Approach (DWA) with the Rapidly-exploring Random Tree (RRT) algorithm and incorporating collision avoidance rules between AUVs into the evaluation system of the DWA, the collision avoidance rules are quantified, and corresponding evaluation functions are established. This allows for the selection of motion trajectories that comply with the collision avoidance rules from the predicted trajectory set, improving the obstacle avoidance capability during AUV motion planning and enhancing the reliability of the target hunting task. The introduction of a consistency algorithm maintains the consistency of the group task information and ensures that the hunting strategy can be adjusted promptly in the event of an AUV failure, allowing the target hunting task to continue. Polynomial regression algorithms are used to predict the moving target’s trajectory. Based on a polygonal hunting formation, the hunting potential points are dynamically allocated, and, finally, each AUV executes distributed motion planning towards the hunting potential points to form the hunting formation. Simulation results show that the proposed method achieves efficient multi-AUV-distributed dynamic target hunting.

A Comparative Study of Rapidly-exploring Random Tree Algorithms Applied to Ship Trajectory Planning and Behavior Generation

Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query nonholonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators. Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch’s t-test at a significance level of α=0.05, indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.

Time-optimal trajectory planning for a six-degree-of-freedom manipulator: a method integrating RRT and chaotic PSO

This study proposes an innovative algorithm based on a hybrid optimization strategy, integrating the rapidly-exploring random tree (RRT) and an improved particle swarm optimization (PSO) method to address the time-optimal trajectory planning problem for six-degree-of-freedom robotic arms. The proposed approach emphasizes obstacle avoidance and motion smoothness. RRT is utilized within a dynamic three-dimensional environment to rapidly generate an initial collision-free path. Subsequently, improved PSO enhances global search performance by introducing a multi-source chaotic mapping-based population initialization strategy, dynamically adjusted inertia weights, and nonlinear learning factors. These enhancements effectively mitigate the limitations of traditional PSO methods, which are prone to premature convergence in complex optimization problems. Furthermore, the proposed 3-5-3 polynomial interpolation method significantly smooths the trajectory, reducing fluctuations in velocity and acceleration, and thereby improving the precision and energy efficiency of trajectory planning. Experimental results demonstrate that the proposed algorithm outperforms existing methods, such as the improved RRT and improved non-dominated sorting genetic algorithm, across multiple metrics. Notable achievements include a reduction of total motion time by approximately 21%, improved stability in robotic arm motion, and enhanced adaptability to dynamic environments. Particularly, the method achieves superior trajectory diversity and uniformity through joint optimization of multiple chaotic sources, overcoming the inherent limitations of single chaotic mappings. This research expands the application scenarios of RRT and PSO and provides a novel solution for intelligent control and real-time planning of high-degree-of-freedom robotic arms.

A Smooth Global Path Planning Method for Unmanned Surface Vehicles Using a Novel Combination of Rapidly Exploring Random Tree and Bézier Curves.

Developing autonomous navigation techniques for surface vehicles remains an important research area, and accurate global path planning is essential. For mobile robots-particularly for Unmanned Surface Vehicles (USVs)-a key challenge is ensuring that sharp turns and sharp breaks are avoided. Therefore, global path planning must not only calculate the shortest path but also provide smoothness. Bézier Curves are one of the main methods used for smoothing paths in the literature. Some studies have focused on turns alone; however, continuous path smoothness across the entire trajectory enhances navigational quality. Contrary to similar studies, we applied Bézier Curves whose control polygon is defined by an RRT path and thus avoided a multi-objective formulation. In the final stage of our approach, we proposed a control point reduction method in order to decrease the time complexity without affecting the feasibility of the path. Our experimental results suggest significant improvements for multiple map sizes, in terms of path smoothness.

RSP-UV: real-time sampling-based path planning method for unmanned vehicles

Abstract The Rapidly-exploring Random Trees Star (RRT*) is widely used in unmanned vehicle path planning. However, it suffers from excessive sampling randomness, long search time and not capable of dynamic obstacle avoidance. This is not only fatal to safety, but also detrimental to smooth driving. We propose a real-time sampling-based path planning method for unmanned vehicles (RSP-UV) that systematically solves these problems. To address the problem of excessive sampling randomness, we formulate a partitioned sampling strategy and limit the density of sampling points. It reduces the sampling time to a certain extent. Additionally, we design a fuzzy controller to adjust the scale factor of the Dynamic Window Approach (DWA) evaluation function to optimise the local path and improve the dynamic obstacle avoidance efficiency. Fusing the improved RRT* and the improved DWA, the global path output from the improved RRT* is used as the bootstrap path for the improved DWA. Besides, the return path set rule is made to avoid the backtracking phenomenon. This improves the reliability and asymptotic optimality of the final path. Simulation experiments show that the trajectories planned by RSP-UV outperform the three compared methods, the similar method, ORRT*-DWA and MTAR. Real experiments show that the average path length of RSP-UV is reduced by 2.26%, 3.03%, and 3.33% compared to the other three methods, respectively. The average planning time of RSP-UV is reduced by 7.16%, 2.15% and 8.78% compared to the other three methods, respectively. RSP-UV not only ensures the success of path planning but also excels in real-time and adaptability. The paths formed by RSP-UV are cheaper and safer.

The Optimization and Application Research of the RRT-APF-Based Path Planning Algorithm

To address the shortcomings of the original rapidly-exploring random tree (RRT) algorithm, such as long and non-smooth paths, slow convergence to the goal region, and limited adaptability in dynamic environments, this paper proposes a hybrid path planning method combining the artificial potential field (APF) approach with the RRT algorithm. This integrated approach leverages the strengths of both methods to achieve efficient, collision-free path planning in both two-dimensional and three-dimensional environments. The algorithm overcomes the local minima problem inherent in APF while maintaining the RRT’s efficiency in high-dimensional spaces and complex environments. A dynamic adjustment strategy is introduced to adapt to specific application scenarios and varying environmental complexity. Additionally, Bezier curve fitting is applied to smooth the resulting paths. Simulations conducted in various environments demonstrate the effectiveness of the proposed method, highlighting its efficiency and robustness in generating collision-free paths. Compared to the original RRT algorithm, the proposed method reduces path length by 13.4% to 24.9% and decreases search time by 9.8% to 56.5%, improving both path quality and planning efficiency.

A digital twin-driven pre-grasp path planning method for robotic deep-frame grasping of disordered workpieces

In a sheet metal parts welding shop, traditional robot path-planning methods prove inadequate for achieving automatic robot loading and unloading of disordered workpieces in the deep material frames. Therefore, this paper proposes a robotic motion planning framework in Deep-frame Disordered Workpiece Grasping (DDWG), ensuring efficient automatic loading and unloading tasks with high efficiency and collision-free operations. In conjunction with the actual DDWG scenario, an Oriented Bounding Boxes (OBB) based hierarchical enclosing box algorithm is used for collision detection. Further, this paper proposes a two-segment Middle Pre-Grasp Rapidly-exploring Random Tree (MPG-RRT) algorithm for path planning, utilizing pre-grasp point (middle point) guidance. In the DDWG process, a pre-adjustable pre-grasp point is introduced to expand the two random trees toward the intermediate points. Additionally, a probabilistic goal bias and a goal gravity strategy are incorporated with an adaptive gravity step size at each exploring stage to guide the expansion of random trees toward the goal point direction and to optimize paths by removing redundant nodes. Finally, an existing digital twins-driven robotic production line is reconfigured to simulate motion planning in two DDWG scenarios, which validates the effectiveness and superiority of the proposed method.

Improved RRT* Path-Planning Algorithm Based on the Clothoid Curve for a Mobile Robot Under Kinematic Constraints.

In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorithm are as follows: First, we introduce a bidirectional expansion strategy that quickly identifies a direct path to the goal point in a short time. Second, a node reconnection strategy is used to eliminate unnecessary nodes, thereby reducing the path length and saving memory. Third, a path deformation strategy based on the Clothoid curve is devised to enhance obstacle avoidance and path-planning capability, ensuring collision-free paths that comply with the kinematic constraints of mobile robots. Simulation results demonstrate that our algorithm is simpler, more computationally efficient, expedites pathfinding, achieves higher success rates, and produces smoother paths compared to existing algorithms.

Performance comparison of rapidly-exploring random tree algorithms for path planning of autonomous underwater vehicles in complex environments

Performance comparison of rapidly-exploring random tree algorithms for path planning of autonomous underwater vehicles in complex environments

Fusion of improved RRT and ant colony optimization for robot path planning

Abstract To address the issues of poor guidance at the beginning of the Ant Colony Optimization (ACO) algorithm, non-smooth paths, and its tendency to fall into local optima, this paper proposes a path planning approach based on the Rapidly-exploring Random Tree (RRT) and Ant Colony Optimization (ACO). Firstly, obstacles are inflated to set a safety distance, and a differentiated pheromone distribution is created using the sub-optimal trajectory produced by the improved RRT, guiding the initial direction of the ant colony. Secondly, dynamic strategies are introduced into the evaporation coefficient and heuristic factor, adjusting their weights according to the number of iterations to enhance the attraction of the target point to the ants. Then, a reward-punishment mechanism is used to update the pheromone, solving the problem of local optima. Finally, a pruning optimization strategy based on the maximum turning angle is employed to remove redundant nodes, making the path smoother. Multiple simulation results confirm that the algorithm possesses good global search capabilities and robustness under various conditions.

Optimization of Motion Planning for Quadrotor-Equipped Robotic Manipulators

The focus of the work is on optimizing motion planning for quadcopter-equipped robotic manipulators by integrating quadcopter design, modeling, and path planning for a robotic arm mounted on a quadcopter through MATLAB. Recently, quadcopters have also been used in many application fields with increased viability. This paper will attempt at bringing a complete framework that enhances the operational capabilities of quadcopters through optimized path planning algorithms. The approach will rely on obstacle avoidance techniques for safe operation with satisfactory efficiency in unstructured environments. Advanced algorithms such as Rapidly-exploring Random Trees and dynamic window approaches provide solutions to this challenge of redundancy in the path followed and adaptability in its surroundings. Simulations prove that the proposed optimization methods strongly improve the accuracy and efficiency of the path planned by the robotic arm as it adheres to the dynamic constraints imposed. This work can apply practical insight into many real-world fields such as logistics, surveillance, and search-and-rescue applications aside from contributing to the theoretical understanding of quadcopter dynamics.

Escape Path Planning for Unmanned Surface Vehicle Based on Blind Navigation Rapidly Exploring Random Tree* Fusion Algorithm.

To address the design and application requirements for USVs (Unmanned Surface Vehicles) to autonomously escape from constrained environments using a minimal number of sensors, we propose a path planning algorithm based on the RRT* (Rapidly Exploring Random Tree*) method, referred to as BN-RRT* (Blind Navigation Rapidly Exploring Random Tree*). This algorithm utilizes the positioning information provided by the GPS onboard the USV and combines collision detection data from collision sensors to navigate out of the trapped space. To mitigate the inherent randomness of the RRT* algorithm, we integrate the Artificial Potential Field (APF) method to enhance directional guidance during the sampling process. Additionally, inspired by blind navigation principles, we propose an active collision mechanism that relies on continuous collisions to identify obstacles and adjust the next movement direction, thereby improving the efficiency of escape path planning. We also implement an obstacle memory mechanism to prevent exploration into erroneous areas during sampling, significantly increasing the success rate of escape and reducing the path length. We validate the proposed algorithm in a dedicated MATLAB environment, comparing its performance with existing RRT, RRT*, and APF-RRT* algorithms. Experimental results indicate that the improved algorithm achieves significant enhancements in both planning speed and path length compared to the other methods.

An improved RRT* drone three-dimensional path-planning algorithm based on point cloud maps

Given the problems of slow convergence and blind sampling of the Rapidly-exploring Random Trees (RRT*) algorithm in 3D path-planning of UAVs, this paper proposes an improved bidirectional probabilistic target bias RRT* algorithm for 3D path-planning of UAVs based on point cloud maps. Initially, 3D point cloud maps and the dynamic step size necessary for the expansion of the RRT* algorithm are derived through the implementation of 3D map reconstruction and point cloud analysis techniques. Subsequently, a double sampling mechanism of random and target sampling is combined with the expansion strategy of the Bi-RRT* and RRT*-Connect algorithms to generate a bidirectional random tree, which improves the search speed and shortens the path. Furthermore, the influence of a gravitational mechanism, generated by the goal, is considered to direct the growth of the random tree toward the target. A collision avoidance strategy based on KD-tree is also introduced to enhance collision detection efficiency and mitigate collision risks. Additionally, cubic spline optimization is employed to achieve a smoother path. Finally, in order to illustrate the viability and efficacy of the proposed algorithm, a comparative analysis is performed between the enhanced algorithm and the RRT*, P-RRT*, and Bi-RRT* algorithms across two distinct environments.

A UAV path planning method based on the framework of multi-objective jellyfish search algorithm

Multi-constraint UAV path planning problems can be viewed as many-objective optimization problems that can be solved by meta-heuristic algorithms with good self-organizing optimization capabilities. However, such algorithms mostly use random initializing methods, resulting in low-quality initial paths that reduce the efficiency of subsequent algorithmic searches. Moreover, as the number of objective functions increases, meta-heuristic algorithms face inadequate selection pressure and convergence capability, which lead to poor solution. In order to address these issues, this paper proposes a UAV path planning method based on the framework of multi-objective jellyfish search algorithm (UMOJS). Firstly, an initializing strategy based on Rapidly-exploring Random Trees (RRT) is proposed to achieve higher quality initial paths. Secondly, a jellyfish updating strategy guided by the class-optimal individual is designed to enhance the convergence ability of the algorithm. Furthermore, a set of predefined reference points is imported to obtain Pareto optimal solutions with better convergence and distribution in many-objective optimization problems. To evaluate the superiority of the proposed UMOJS algorithm, three different difficulties of simulated flight environments are constructed to verify its performance. The experimental results show that UMOJS is not only able to gain more UAV paths with shorter length, but also more evenly distributed Pareto optimal solutions compared to five meta-heuristic algorithms when the constraint conditions are satisfied.

A Combined Strategy for Path Planning of Tensegrity Manipulators Considering Structural Stability

Abstract A strategy combining an improved rapidly exploring random tree (RRT) method with the artificial potential field (APF) method is proposed for the path planning of flexible tensegrity manipulators considering typical complex constraints, such as structural stability, obstacle avoidance, and cable no-slackening. The relationship between the node displacements and the elongations of active members is established for the kinematic analysis of tensegrity manipulators. The rest lengths of active members are taken as the design variables for the generation of a random tree. The guide node is utilized for goal-biased samplings to expedite the exploration toward the final configuration, while the APF method is introduced to ensure that the obtained elongations of active members can satisfy the constraint of obstacle avoidance. The proportion of random and goal-biased samplings is adaptively adjusted based on the sampling success rate. Futile random samplings can be significantly reduced by dynamically modifying the random sampling range according to the obtained configuration nearest to the final configuration. The computational procedure of the proposed method is presented. An illustrative flexible tensegrity manipulator is employed to demonstrate the adaptability of the proposed path planning method to complex constraints, especially structural stability.

Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots.

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

An Improved RRT Path-Planning Algorithm Based on Vehicle Lane-Change Trajectory Data

The Rapidly-exploring Random Tree (RRT) algorithm faces issues in path planning, including low search efficiency, high randomness, and suboptimal path quality. To overcome these issues, this paper proposes an improved RRT planning algorithm based on vehicle lane change trajectory data. This algorithm dynamically adjusts the sampling area based on road environment and trajectory change laws, allowing the random tree to sample within an effective area, thereby improving the algorithm’s sampling efficiency. After determining the sampling area, a sampling point optimization strategy is used to enhance sampling quality, resulting in a smoother and more executable path. Finally, the vehicle is processed in a standardized manner to further improve path safety. Simulation results indicate that, compared to the original RRT algorithm, the improved version reduces nodes, planning time, and path length by 12.77%, 64.79%, and 12.87%, respectively. It also improves path smoothness and more closely aligns with actual lane-change trajectories, demonstrating the effectiveness and executability of the improved algorithm.

Efficient path planning for autonomous vehicles based on RRT* with variable probability strategy and artificial potential field approach.

With the rapid development of autonomous driving technology, path planning has gained significant attention as it holds great potential for improving safety. The Rapidly-exploring Random Tree star(RRT*) algorithm has attracted much attention because of its good adaptability and expansibility. However, how solving problems in the RRT* algorithm such as slow convergence time, significant search range randomness, and unpredictability is a challenge. Therefore, an RRT* enhancement algorithm combining variable probability goal-bias strategy and artificial potential field(APF) method(Improved A-RRT*) is proposed in this paper. Firstly, the variable probability goal-bias strategy is introduced in the sampling process to make random tree expand towards the target direction and improve the directional searchability of the random tree. Secondly, the potential field function in APF is improved to prevent falling into local optimum problems during path generation. Thirdly, improved APF is combined with RRT*, the target generates a gravitational field on random tree, and the obstacle generates a repulsive force on it, leading random tree to grow toward the target region. Finally, the proposed algorithm is compared with RRT* algorithm and its derivative algorithm. The experimental results demonstrate that the proposed algorithm has obvious optimizations in convergence speed and path quality.

Acoustic emission source location in complex structures based on artificial potential field-guided rapidly-exploring random tree* and genetic algorithm

Towards more accurate and easy-to-implement damage detection in large-scale complex structures, a novel acoustic emission (AE) source location method is developed based on artificial potential field-guided rapidly-exploring random tree* (APF-RRT*) and genetic algorithm (GA). APF-RRT*, which combines the excellent obstacle avoidance ability of RRT* with the path planning efficiency of APF, is introduced to adaptively estimate the shortest distances from the damage source to AE sensors. The shortest distances are obtained as the actual propagation distances of waves and then embedded into the modified error function, where GA is employed as an optimization scheme to evaluate the source location via iterations. Through the experiment on a full-scale high-strength bolt joint plate with a series of bolt holes, the effectiveness and superiority of the proposed method were validated. It achieved a better source location performance with lower mean absolute error and standard deviation than the time-of-arrival (TOA) method, delta-T mapping method, and machine learning-improved methods based on Gaussian process (GP) and artificial neural network (ANN), respectively. The primary contributions of the proposed method lay in abandoning the straight-wave-propagation assumption of the traditional TOA method by adaptively taking into account the geometric obstacles in complex structures, and removing the need for a large amount of training data and burdensome pencil lead break (PLB) tests required by data-driven location methods.