Rapidly-exploring Random Tree Algorithm Research Articles

Path planning of a 6-DOF measuring robot with a direction guidance RRT method

The path planning of a measuring robot is critical to automatic measurement, but it is hard to solve a global optimal path solution when it comes to scanning a complex body with many obstacles. To overcome this problem, a direction guidance Rapidly-exploring Random Tree algorithm (DG_RRT) is proposed. First, to improve the efficiency of the search process, the strategy of direction guidance is introduced for the initial path based on the traditional RRT algorithm. Second, invalid paths are simplified by linear processing. Third, to balance the length of the path and the planning time, generated paths are further corrected by adjusting the step size, optimizing threshold parameters, and smoothing the curve. To verify the suggested method, both numerical simulation and experimental analysis are carried out. The experimental results show that the average length of the paths (ALPs) of DG_RRT is 29.6% lower than that of the traditional RRT, 6.6% lower than that of RRT* and 43.0% lower than that of Q-learning (QL). The standard deviation (SD) of the path length of DG_RRT is 93.4% lower than that of traditional RRT, and 83.1% lower than that of RRT. The mean of planning time (MPT) of DG_RRT is 94.3% lower than that of RRT*, 95.5% lower than that of QL. The number of discrete points on the path of DG_RRT is 81.4% lower than that of traditional RRT, 86.0% lower than that of RRT* and 91.7% lower than that of QL. It demonstrates that the DG_RRT is superior to other traditional methods.

IGLOO: An Iterative Global Exploration and Local Optimization Algorithm to Find Diverse Low-Energy Conformations of Flexible Molecules

The exploration of the energy landscape of a chemical system is essential for understanding and predicting its observable properties. In most cases, this is a challenging task due to the high complexity of such landscapes, which often consist of multiple, possibly hierarchical basins that are difficult to locate and thoroughly explore. In this study, we introduce a novel method, called IGLOO (Iterative Global Exploration and Local Optimization), which aims to achieve a more efficient global exploration of the conformational space compared to existing techniques. The method utilizes a tree-based exploration inspired by the Rapidly exploring Random Tree (RRT) algorithm originating from robotics. IGLOO dynamically adjusts its exploration strategy to both homogeneously scan the landscape and focus on promising regions, avoiding redundant exploration. We evaluated IGLOO using models of two polypeptides and compared its performance to the traditional basin-hopping method and a hybrid method that also incorporates the RRT algorithm. We find that IGLOO outperforms both alternative methods in terms of efficiently and comprehensively exploring the molecular conformational space. This approach can be easily generalized to other chemical systems such as molecules on surfaces or crystalline systems.

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

NT-ARS-RRT: A novel non-threshold adaptive region sampling RRT algorithm for path planning

Rapidly-Exploring Random Tree (RRT) algorithm is a widely used path planning method. However, it suffers from low solution efficiency, no search guidance, poor quality of the obtained path and the problem of obvious reduction of search efficiency in the narrow exit environment, which greatly reduces its performance. To overcome these issues, this paper proposes a novel non-threshold adaptive region sampling RRT (NT-ARS-RRT) algorithm for path planning. First, a map preprocessing method is proposed to reduce the explore space of the random tree and improves the path search efficiency especially at narrow exits. Second, a branch-and-leaf backtracking method is proposed to optimize the selection of the nearest node. Third, a distance threshold connection constraints optimization method between the newest node and the target point is proposed to reduce the generation of unnecessary nodes and thus further improves the algorithm’s speed. Fourth, the initial path is optimized by using a rewiring method based on triangular inequality to improve the path quality. Experimental results show the effectiveness of the proposed algorithm.

CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments

In complex environments, path planning for mobile robots faces challenges such as insensitivity to the environment, low efficiency, and poor path quality with the rapidly-exploring random tree (RRT) algorithm. We propose a novel algorithm, the complex environments rapidly-exploring random tree (CERRT), to address these issues. The CERRT algorithm builds upon the RRT approach and incorporates two key components: a pre-allocated extension node method and a vertex death mechanism. These enhancements aim to improve vertex utilization and overcome the problem of becoming trapped in concave regions, a limitation of traditional algorithms. Additionally, the CERRT algorithm integrates environment awareness at collision points, enabling rapid identification and navigation through narrow passages using local simple sampling techniques. We also introduce the bidirectional shrinking optimization strategy (BSOS) based on the pruning optimization strategy (POS) to further enhance the quality of path solutions. Extensive simulations demonstrate that the CERRT algorithm outperforms the RRT and RRV algorithms in various complex environments, such as mazes and narrow passages. It exhibits shorter running times and generates higher-quality paths, making it a promising approach for mobile robot path planning in challenging environments.

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment.

When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances.

The Research Progress of Path Planning Algorithms

At present, path-planning for mobile robots is a hot problem. In the previous scientific research, A*, Dijkstra, rapidly-exploring random tree (RRT) and many other algorithms have appeared. To provide a clearer understanding of these algorithms, this article organized the idea and development of Q-learning, A* algorithm, ant colony algorithm, RRT algorithm and Dijkstra algorithm. According to the time order, this article researched several applications of the five algorithms respectively. Besides, optimization of the five algorithms in the past few years were listed. There are also examples of combined algorithms, which have better efficiency compared to using one algorithm alone. By analyzing the five algorithm and their optimization, the common problem and solution was found. The common problem of these algorithms is that a shorter and smoother path needs to be solved and the convergence speed needs accelerating. At present, the main solution can be divided into two aspects. One is to improve the algorithm itself, and the other is to combine different algorithms. In the future, more kinds of combined algorithms will emerge, and a better solution of path planning can be obtained to improve efficiency and reduce energy cost.

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

PurposeThe goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.Design/methodology/approachThe path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.FindingsThe algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.Originality/valueAn improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

Research on hybrid navigation algorithm and multi-objective cooperative planning method for electric inspection robot

For mobile robots working in the power inspection environment, navigation algorithms and collaborative path planning are the keys to understanding and reproducing human power inspection’s thinking mode. Firstly, the paper proposed a hybrid navigation algorithm of reflector and laser based on the generalized inverse of the least squares solution. Secondly, to solve the problem of multi-robot inspection cooperative path planning, the traffic rule reservation table generated a dynamic weighted map representing traffic congestion in power inspection to prevent multi-robot collision. Thirdly, through the distributed control method, the robot used the dynamic weighted map to improve the RRT (Rapidly-Exploring Random Trees)algorithm to achieve the purpose of active cooperative path planning, reducing the trajectory’s critical path points and solving the cooperative congestion. Finally, the positioning and calibration compensation can be carried out through the experiment by the reflector navigation algorithm in a large-scale complex environment. Comparing the path planning methods of the A* algorithm and the dynamic weighted RRT algorithm, the dynamic weighted RRT algorithm reduces the number of extended nodes. It solves the multi-robot planning collision problem while ensuring the search path’s completeness. Meanwhile, the dynamic weighted RRT algorithm can save the running time of the multi-robot system and effectively improve the efficiency of the power inspection system.

Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.

Implementation of a Real-Time Object Pick-and-Place System Based on a Changing Strategy for Rapidly-Exploring Random Tree

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.

An RRT That Uses MSR-Equivalence for Solving the Self-Reconfiguration Task in Lattice Modular Robots

A modular self-reconfigurable robot can change its configuration to effectively adapt itself to perform a wide range of tasks. The Modular Self-reconfigurable Robot (MSR) is constructed from a collection of connected modules that can move relative to each other and change the system's shape. This research presents a novel sliding Triangular Lattice Modular Robot (STRIMOR). We use a Rapidly-exploring random tree (RRT*) algorithm to solve the self-reconfiguration (SR) problem. This algorithm is computationally expensive due to the configuration space dimension. In order to increase the number of modules we define ‘Topological Configuration’ which better describes the configuration's unique properties. This new approach significantly reduces the tree size by representing a set of MSR-equivalence configurations as a single topological configuration. Furthermore, we examine the advantage of moving several module-groups in a single movement and choose their number by several density functions. Our improvements are presented using a set of simulated SR problems with different complexity levels. On top of that, We designed and fabricated the STRIMOR's dual-type modules and discuss how such a design simplifies the system's mechanical complexity.

Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment.

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs' impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment.

Toward distributed control of networked omnidirectional automated ground vehicles in complex environment with delay communication

The utilization of multiple small-sized automated ground vehicles (AGVs) with omnidirectional movement for cooperative transportation of large objects in manufacturing and logistics is a growing area of research. Utilizing multiple AGVs can improve system flexibility and efficiency compared to using a single large AGV, particularly in clustered and narrow spaces. This study presents a control framework for multiple AGVs to cooperatively transport an object in a cluster environment. The framework utilizes the rapidly-exploring random tree (RRT∗) algorithm to plan a feasible path through the environment, and a new distributed synchronization control algorithm for each AGV to track its desired trajectory and synchronize with its neighbors. The proposed control framework can be applied in cases with time-varying communication delays and networks with directed spanning tree connections, making it more versatile than existing synchronization control methods. Simulation and experimental results are provided to validate the proposed framework.

Manipulator Motion Planning Based on Improved RRT Algorithm

Aiming at the problems that the Rapidly-exploring Random Tree (RRT) algorithm has more time-consuming and longer paths in the manipulator motion planning, we propose an improved RRT algorithm based on probabilistic target bias strategy and triangular inequality pruning method. This paper simulates the algorithm from three dynamic environments with different numbers of obstacles. The simulation results show that the proposed improved RRT algorithm can plan a shorter path in less time.

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

This paper proposes the use of a small differential robot with two DC motors, controlled by an ESP32 microcon-troller, that implements the Rapidly Exploring Random Trees algorithm to navigate from an origin point to a destination point in an unknown but observable environment. The motivation behind this research is to explore the use of a low-cost, versatile and efficient robotic platform for autonomous navigation in complex environments. This work presents a practical and cost-effective solution that can be easily replicated and implemented in various scenarios such as search and rescue, surveillance, and industrial automation. The proposed robotic platform is equipped with a set of sensors and actuators that allow it to observe the environment, estimate its position, and move through it. The Rapidly Exploring Random Trees algorithm is implemented to generate a path from an origin to a destination point, avoiding obstacles and adjusting the robot’s motion accordingly. The implementation of this algorithm enables the robot to navigate through complex environments with high efficiency and reliability, making it a suitable solution for a wide range of applications. The results obtained through simulations and experiments show that the proposed robotic platform and algorithm achieve high performance and accuracy in autonomous navigation, even in complex environments.

UAV Trajectory Planning for Complex Open Storage Environments Based on an Improved RRT Algorithm

Multi-rotor UAVs (Unmanned Aerial Vehicles) have been increasingly used for hazardous inspection tasks in complex open-air warehouse storage environments due to their high maneuverability and aerial perspective. To facilitate rapid response to patrol missions and improve the efficiency of UAV trajectory planning. This paper established a rotary-wing UAV trajectory plan model considering UAV patrol efficiency, trajectory cost, and power consumption cost. Secondly, an improved SSA (salp swarm algorithm) is incorporated for the shortcomings of low algorithmic search efficiency and unsmooth paths when planning paths in the traditional RRT (Rapidly-exploring Random Trees). The predation mechanism of the salps group is incorporated into the random sampling of the RRT algorithm, which reduces the invalid sampling of random points and introduces the adaptive leader structure, and reverses the search strategy to improve the algorithm’s global search for superiority at the later stage of the search. Finally, the designed LASSA-RRT algorithm is subjected to simulation experiments and compared with RRT, RRT*, IRRT, and PF-RRT* in a cross-sectional manner. The results show that the LASSA-RRT algorithm has an average reduction of 55.83% in sampling times, 51.91% in run time, 13.17% in track length, and 0.1491% in flight cost. In summary, this paper’s UAV trajectory planning method can be effectively applied to complex open storage environments. It can provide a helpful reference direction for UAV trajectory planning.

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.

Camera Pose Optimization for 3D Mapping

Digital 3D models of environments are of great value in many applications, but the algorithms that build them autonomously are computationally expensive and require a considerable amount of time to perform this task. In this work, we present an active simultaneous localisation and mapping system that optimises the pose of the sensor for the 3D reconstruction of an environment, while a 2D Rapidly-Exploring Random Tree algorithm controls the motion of the mobile platform for the ground exploration strategy. Our objective is to obtain a 3D map comparable to that obtained using a complete 3D approach in a time interval of the same order of magnitude of a 2D exploration algorithm. The optimisation is performed using a ray-tracing technique from a set of candidate poses based on an uncertainty octree built during exploration, whose values are calculated according to where they have been viewed from. The system is tested in diverse simulated environments and compared with two different exploration methods from the literature, one based on 2D and another one that considers the complete 3D space. Experiments show that combining our algorithm with a 2D exploration method, the 3D map obtained is comparable in quality to that obtained with a pure 3D exploration procedure, but demanding less time.

A nine-degree-of-freedom modular redundant robotic manipulator: Development and experimentation

Performing interactive tasks in complex obstacle environments is a formidable challenge for manipulators. Redundant manipulators have the advantage of being highly flexible and can be used to perform interactive tasks in complex environments. This paper presents a nine-degree-of-freedom (DOF) modular redundant manipulator with nine identical modular joints. To improve the obstacle avoidance capability in narrow environments, the bidirectional rapidly-exploring random trees (bi-RRT) algorithm and the oriented bounding box (OBB) are combined for trajectory planning of redundant robotic manipulators. To enhance the interaction capability in uncalibrated dynamic environments, an improved virtual semi-active damping control (VSADC) algorithm based on particle swarm optimization (PSO) is proposed, and it can effectively estimate impedance parameters and positions of the dynamic environment. Finally, combined with the proposed control schemes, simulation and experimental results demonstrate that the designed redundant manipulator can traverse complex obstacle environments and interact with the unknown dynamic environment.