Rapidly-exploring Random Tree Algorithm Research Articles

Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm

In the future of automated production processes, the manipulator must be more efficient to complete certain tasks. Compared to single-arm robots, dual-arm robots have a larger workspace and stronger load capacity. Coordinated motion planning of multi-arm robots is a problem that must be solved in the process of robot development. This paper proposes an obstacle avoidance path planning method for the dual-arm robot based on the goal probability bias and cost function in a rapidly-exploring random tree algorithm (GA_RRT). The random tree grows to the goal point with a certain probability. At the same time, the cost function is calculated when the random state is generated. The point with the lowest cost is selected as the child node. This reduces the randomness and blindness of the RRT algorithm in the expansion process. The detection algorithm of the bounding sphere is used in the process of collision detection of two arms. The main arm conducts obstacle avoidance path planning for static obstacles. The slave arm not only considers static obstacles, but also takes on the role of the main arm at each moment as a dynamic obstacle for path planning. Finally, MATLAB is used for algorithm simulation, which proves the effectiveness of the algorithm for obstacle avoidance path planning problems for the dual-arm robot.

Virtual-Goal-Guided RRT for Visual Servoing of Mobile Robots With FOV Constraint

In this article, a virtual-goal-guided rapidly exploring random tree (RRT)-based visual servoing approach is proposed for nonholonomic mobile robots to simultaneously satisfy the field-of-view (FOV) constraint and the velocity constraints during the motion toward the desired pose. The presented approach contains two parts: 1) trajectory planning in the scaled Euclidean space and 2) trajectory tracking control. For the trajectory planning part, a new virtual-goal-guided RRT algorithm is designed to guarantee the FOV constraint and the velocity constraints by iteratively exploring the scaled Euclidean space in the presence of unknown image depth. Specifically, a virtual goal directly behind the desired pose is set to guide the tree to extend laterally into the area wherein the robot is easier to satisfy the FOV constraint. In addition, the lateral extension of the tree also helps decrease the lateral error of the robot as much as possible. Following each successful extension toward the virtual goal node, a greedy extension from the newly explored node to the desired pose is attempted using a polar stabilization controller, so that the planned trajectory can accurately arrive at the desired pose. Each newly explored edge in the scaled space is projected into the image space to check for the FOV limit. For the visual tracking part, the final searched trajectory in the scaled space is first transformed into image feature trajectories, which are then tracked by an image-based visual tracking controller. Experiments validate the effectiveness of the proposed approach.

Real-Time Directed Rapidly Exploring Random Tree Path Planning for Air Collision Avoidance

In this paper, a path planning algorithm is proposed for dynamic obstacle avoidance by improving a rapidly exploring random tree (RRT) algorithm, a sampling-based path-planning algorithm. Although guaranteeing a globally optimal path is impossible when using RRT algorithm, it is advantageous because it can generate the path quickly. Considering this advantage of the RRT algorithm, the directed RRT (DRRT) algorithm was developed, which reduces the generation time and supplements optimality by improving node generation and tree expansion process. It is also added the path smoothing method for the flight. The anytime DRRT algorithm is based on the DRRT algorithm and repeats path generation within the time limit considering the direction of movement of the obstacle and selects the shortest path for the flight. Air collision avoidance between unmanned aerial vehicles (UAVs) considering the intruders as a dynamic obstacle was simulated using the Gazebo simulator. Based on the simulation, the performance of the proposed anytime DRRT algorithm was verified with varying the host UAV and intruder conditions, and its applicability to UAVs was affirmed.

An Adaptive Rapidly-Exploring Random Tree

Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning (MP) problems, in which rapidly-exploring random tree (RRT) and the faster bidirectional RRT (named RRT-Connect) algorithms have achieved good results in many planning tasks. However, sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages. Therefore, several algorithms have been proposed to overcome these drawbacks. As one of the improved algorithms, Rapidly-exploring random vines (RRV) can achieve better results, but it may perform worse in cluttered environments and has a certain environmental selectivity. In this paper, we present a new improved planning method based on RRT-Connect and RRV, named adaptive RRT-Connect (ARRT-Connect), which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments. The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.

Discrete space-based route planning for rotary-wing UAV formation in urban environments

The route planning problem for rotary-wing UAV formation is studied in discrete urban environments. First, a discrete-space consensus algorithm (DSCA) integrating asynchronous planning and grouping mechanism is developed to make the rotary-wing UAVs converge to the desired formation. Then the DSCA is combined with the improved rapidly-exploring random tree (IRRT) algorithm to enable the rotary-wing UAV formation to avoid the obstacles. Finally, the time information of waypoints is complemented by resolving the conflicts among multiple rotary-wing UAVs, thus reducing the rotary-wing UAVs’ time gap of reaching the destinations. The proposed method extends the original consensus theory in the discrete space and for the obstacle avoidance issue. Also, it is valid in the route planning problem for rotary-wing UAV formation considering the real urban environments.

CAF-RRT*: A 2D Path Planning Algorithm Based on Circular Arc Fillet Method

To improve the previous versions of Rapidly-exploring Random Trees (RRT) algorithms, a novel algorithm based on Circular Arc Fillet (CAF-RRT*) for path planning problems in two-dimension workspaces is proposed. Firstly, it is implemented by combining Quick-RRT* with the bidirectional RRT algorithm, which can obtain an initially planned path. Secondly, the cost of the initial planned path cost is reduced by a proposed path optimization strategy based on the triangle rule. Finally, based on the circular arc fillet method, a path smoothing strategy is proposed to process the middle nodes of the path, while further reducing its path cost. The experimental results on MATLAB simulation platform show that compared with other RRT algorithms, the proposed algorithm can reduce the path length by about 2%, ensure the smoothness and security of the path, while increasing the running speed of the algorithm by about 95%, and the experimental results of the robot based on Robot Operating System (ROS) platform also prove the advantages of the algorithm.

Utilizing a Rapidly Exploring Random Tree for Hazardous Gas Exploration in a Large Unknown Area

The use of robotics olfaction for gas source localization or mapping has become a concern given the issues of terrorism or industrial accidents that may cause damage to the environment. A typical scenario is to send a robot to a place where a dangerous gas leak has just occurred. The robot’s task is to map gas concentrations in the region of interest as effectively as possible. This paper addresses how the robot performs gas exploration in a large and unknown environment. One of the issues that needs to be addressed is the fact that the computation time of the path planning, frontier detection, goal decision making and gas distribution mapping is slower if all cells in the occupancy grid map are involved in a large environment. Consequently, the Rapidly-exploring Random Tree (RRT) algorithm is chosen as the main algorithm. The RRT graph guides the robot’s navigation, utilizes the vertices as goal candidates, gas mean and variance value, and searches for a new frontier. A new strategy is proposed to address the frontier exploration and gas exploitation trade-off. Finally, a Robot Operating System (ROS), Gazebo, and a 3D gas simulator are used to compare the proposed strategy performance with the others in a large outdoor environment.

Intelligent control method of underwater inspection robot in netcage

The traditional netcage inspection requires divers to complete, which is inefficient and dangerous. The underwater robot inspection is a way to solve the problem. When the robot is in motion, the camera shoots the netcage, replacing the manual inspection. A new hybrid control strategy based on neural network (NN) and proportional integral differential (PID) is proposed for underwater three-dimensional path tracking, which overcomes the defect that the traditional feedback regulation can only work after the occurrence of deviation. A feedforward controller based on neural network is designed to predict the disturbance of the controlled object and enhance the anti-interference ability of the system. Firstly, implement global path tracking based on azimuth and course. Then when the remotely operated vehicle (ROV) deviates from the path, local path planning with rapidly-exploring random trees (RRT) algorithm. ROV tracks local path and returns to the global path. Finally, using the moving average (MA) algorithm of RRT path smoothing, a smooth path is obtained to minimize ROV jitter, which ensures that the ROV can clearly take pictures of the netcage, and the service life of the ROV is extended. ROV can replace manual inspection, and it only takes about 30 min to rotate a circle, greatly improving work efficiency. The control approach was tested underwater at different depth path tracking scenarios. The experimental results show that in the case of waves <0.5 m, the average tracking error of ROV is <0.5 m, the fluctuation of roll angle and pitch angle is <6°, the average distance error from ROV to mesh netcage is about 0.2 m, and the underwater netcage inspection task is completed stably. [Video attachment: https://youtu.be/NKcgPcej5sI].

Fast-RRT: A RRT-Based Optimal Path Finding Method

As a sampling-based pathfinding algorithm, Rapidly Exploring Random Trees (RRT) has been widely used in motion planning problems due to the ability to find a feasible path quickly. However, the RRT algorithm still has several shortcomings, such as the large variance in the search time, poor performance in narrow channel scenarios, and being far from the optimal path. In this paper, we propose a new RRT-based path find algorithm, Fast-RRT, to find a near-optimal path quickly. The Fast-RRT algorithm consists of two modules, including Improved RRT and Fast-Optimal. The former is aims to quickly and stably find an initial path, and the latter is to merge multiple initial paths to obtain a near-optimal path. Compared with the RRT algorithm, Fast-RRT shows the following improvements: (1) A Fast-Sampling strategy that only samples in the unreached space of the random tree was introduced to improve the search speed and algorithm stability; (2) A Random Steering strategy expansion strategy was proposed to solve the problem of poor performance in narrow channel scenarios; (3) By fusion and adjustment of paths, a near-optimal path can be faster found by Fast-RRT, 20 times faster than the RRT* algorithm. Owing to these merits, our proposed Fast-RRT outperforms RRT and RRT* in both speed and stability during experiments.

Research on Path Planning Algorithm of Bidirectional Rapidly Exploring Random Tree Improved by Artificial Potential Field

Aiming at the defects of path planning in rapidly exploring random tree(RRT) algorithm, such as low efficiency, strong randomness and slow convergence rate, a new algorithm based on artificial potential field was proposed in this paper. The algorithm used the scheme of bidirectional growth of exploration tree to make two growth trees explore and expand outwards from the starting point and the end point at the same time to accelerate the convergence speed of the algorithm. In the early stage, growth pretreatment was added to make the two growing trees take their respective endpoints as the target points and pass through the obstacle free area rapidly at one time. Artificial potential field method was used to modify the growth tree touching the obstacle and guide the path to grow towards the end. The adaptive change probability was used to select different target points as the growth directions in different periods with different probabilities to accelerate the meeting of two growing trees. After a lot of simulation experiments and data analysis, the improved bidirectional RRT algorithm has higher search efficiency, better growth path and fewer sampling points.

An Improved Rapidly-Exploring Random Trees Algorithm Combining Parent Point Priority Determination Strategy and Real-Time Optimization Strategy for Path Planning.

In order to solve the problems of long path planning time and large number of redundant points in the rapidly-exploring random trees algorithm, this paper proposed an improved algorithm based on the parent point priority determination strategy and the real-time optimization strategy to optimize the rapidly-exploring random trees algorithm. First, in order to shorten the path-planning time, the parent point is determined before generating a new point, which eliminates the complicated process of traversing the random tree to search the parent point when generating a new point. Second, a real-time optimization strategy is combined, whose core idea is to compare the distance of a new point, its parent point, and two ancestor points to the target point when a new point is generated, choosing the new point that is helpful for the growth of the random tree to reduce the number of redundant points. Simulation results of 3-dimensional path planning showed that the success rate of the proposed algorithm, which combines the strategy of parent point priority determination and the strategy of real-time optimization, was close to 100%. Compared with the rapidly-exploring random trees algorithm, the number of points was reduced by more than 93.25%, the path planning time was reduced by more than 91.49%, and the path length was reduced by more than 7.88%. The IRB1410 manipulator was used to build a test platform in a laboratory environment. The path obtained by the proposed algorithm enables the manipulator to safely avoid obstacles to reach the target point. The conclusion can be made that the proposed strategy has a better performance on optimizing the success rate, the number of points, the planning time, and the path length.

Optimal collision-free path planning of a free-floating space robot using spline-based trajectories

A significant number of the planned on-orbit servicing and active debris removal missions will be performed by a spacecraft equipped with a robotic arm. In this paper we propose a new method for the path planning of the robotic arm mounted on the free-floating unmanned spacecraft. In the proposed approach the path of the arm is described in the joint space by a spline function. The coordinates of spline knots form the vector of decision variables. The constrained nonlinear optimization problem is formulated and solved with an active-set algorithm. The proposed method generates a collision-free path of the arm that results in the desired position and orientation of the gripper and the desired attitude of the spacecraft. The possibility of achieving all these goals at the same time is the major advantage of the proposed approach. The practical applicability of the presented method is demonstrated in experiments performed on the planar air-bearing microgravity simulator (for a planar case) and using numerical simulations performed with the Monte Carlo method (for a spatial case). In the planar case the optimal path is compared with the path obtained using the bi-directional Rapidly-exploring Random Trees (RRT) algorithm. It is shown that the path obtained using the proposed method is shorter and smoother than the RRT path.

Comparison Between A* and RRT Algorithms for 3D UAV Path Planning

This paper aims to present a comparative analysis of the two most utilized graph-based and sampling-based algorithms and their variants, in view of 3D UAV path planning in complex indoor environment. The findings of this analysis outline the usability of the methods and can assist future UAV path planning designers to select the best algorithm with the best parameter configuration in relation to the specific application. An extensive literature review of graph-based and sampling-based methods and their variants is first presented. The most utilized algorithms which are the A* for graph-based methods and Rapidly-Exploring Random Tree (RRT) for the sampling-based methods, are defined. A set of variants is also developed to mitigate with inherent shortcomings in the standard algorithms. All algorithms are then tested in the same scenarios and analyzed using the same performance measures. The A* algorithm generates shorter paths with respect to the RRT algorithm. The A* algorithm only explores volumes required for path generation while the RRT algorithms explore the space evenly. The A* algorithm exhibits an oscillatory behavior at different resolutions for the same scenario that is attenuated with the novel A* ripple reduction algorithm. The Multiple RRT generated longer unsmoothed paths in shorter planning times but required more smoothing over RRT. This work is the first attempt to compare graph-based and sampling-based algorithms in 3D path planning of UAVs. Furthermore, this work addresses shortcomings in both A* and RRT standard algorithms by developing a novel A* ripple reduction algorithm, a novel RRT variant and a specifically designed smoothing algorithm.

Post Triangular Rewiring Method for Shorter RRT Robot Path Planning

This paper proposed the 'Post Triangular Rewiring' method that minimizes the sacrifice of planning time and overcomes the limit of Optimality of sampling-based algorithm such as Rapidly-exploring Random Tree (RRT) algorithm. The proposed 'Post Triangular Rewiring' method creates a closer to the optimal path than RRT algorithm before application through the triangular inequality principle. The experiments were conducted to verify a performance of the proposed method. When the method proposed in this paper are applied to the RRT algorithm, the Optimality efficiency increase compared to the planning time.

Efficient and High Path Quality Autonomous Exploration and Trajectory Planning of UAV in an Unknown Environment

The ability of an autonomous Unmanned Aerial Vehicle (UAV) in an unknown environment is a prerequisite for its execution of complex tasks and is the main research direction in related fields. The autonomous navigation of UAVs in unknown environments requires solving the problem of autonomous exploration of the surrounding environment and path planning, which determines whether the drones can complete mission-based flights safely and efficiently. Existing UAV autonomous flight systems hardly perform well in terms of efficient exploration and flight trajectory quality. This paper establishes an integrated solution for autonomous exploration and path planning. In terms of autonomous exploration, frontier-based and sampling-based exploration strategies are integrated to achieve fast and effective exploration performance. In the study of path planning in complex environments, an advanced Rapidly Exploring Random Tree (RRT) algorithm combining the adaptive weights and dynamic step size is proposed, which effectively solves the problem of balancing flight time and trajectory quality. Then, this paper uses the Hermite difference polynomial to optimization the trajectory generated by the RRT algorithm. We named proposed UAV autonomous flight system as Frontier and Sampling-based Exploration and Advanced RRT Planner system (FSEPlanner). Simulation performs in both apartment and maze environment, and results show that the proposed FSEPlanner algorithm achieves greatly improved time consumption and path distances, and the smoothed path is more in line with the actual flight needs of a UAV.

A Method of Enhancing Rapidly-Exploring Random Tree Robot Path Planning Using Midpoint Interpolation

It is difficult to guarantee optimality using the sampling-based rapidly-exploring random tree (RRT) method. To solve the problem, this paper proposes the post triangular processing of the midpoint interpolation method to minimize the planning time and shorten the path length of the sampling-based algorithm. The proposed method makes a path that is closer to the optimal path and somewhat solves the sharp path problem through the interpolation process. Experiments were conducted to verify the performance of the proposed method. Applying the method proposed in this paper to the RRT algorithm increases the efficiency of optimization by minimizing the planning time.

Research on path Planning algorithm of Unmanned ship in narrow Water area

Aiming at the problems of strong random search characteristics and high volatility of planning path in bi-directional Rapidly-exploring Random Tree (bi-RRT) algorithm, a new random tree growth strategy is proposed, the connection between random trees is improved to improve the path-finding speed of the algorithm, and the improved bi-RRT algorithm is used to design a path planning algorithm for unmanned ships in narrow waters. At the end of this paper, the unmanned ship path planning experiment in the island and reef area is carried out, and the experimental results show that the unmanned ship path planning algorithm based on the improved bi-RRT algorithm can quickly provide a safe path for the unmanned ship in narrow waters, and has advantages over the traditional bi-RRT algorithm in terms of time performance, planning path length and volatility, which can provide relevant reference for the research of unmanned ship intelligent navigation.

Lifting path planning of mobile cranes based on an improved RRT algorithm

Lifting operations of mobile cranes are one of the commonly-seen and most important activities for prefabrication housing production (PHP) on sites. However, relevant operations are normally based on the experience of operators or project managers, this often leads to low efficiency as well as high accident rate due to dynamic and complex construction sites. Thus, it is important and necessary to develop an appropriate approach to the lifting planning of mobile cranes so as to guide on-site operations. This paper proposes an improved Rapidly-exploring Random Tree (RRT) algorithm for lifting path planning of mobile cranes. Considering the critical role of Nearest Neighbor Search (NNS) in the implementation of RRT algorithm, a novel strategy for searching the nearest neighbor is developed, i.e., Generalized Distance Method and Cell Method. Both methods are tested in simulation-based experiments. The results show that 1) the Generalized distance method not only reduces the search time, but also unifies the unit of distance measurement and clarifies the physical meaning of distance; 2) the Cell method dramatically reduces the traversal range as well as the search time; and 3) both methods improve the quality of lifting path planning of mobile cranes. This improved RRT algorithm enables rapid path planning of mobile cranes in a dynamic and complex construction environment. The outcomes of this research not only contribute to the body of knowledge in spatial path planning of crane lifting operations, but also have the potential of significantly improving efficiency and safety in crane lifting practices.

Path Planning and Obstacle Avoidance for Automated Driving Systems Using Rapidly-Exploring Random Tree Algorithm

Path Planning and Obstacle Avoidance for Automated Driving Systems Using Rapidly-Exploring Random Tree Algorithm

Processor-in-the-Loop Architecture Design and Experimental Validation for an Autonomous Racing Vehicle

Self-driving vehicles have experienced an increase in research interest in the last decades. Nevertheless, fully autonomous vehicles are still far from being a common means of transport. This paper presents the design and experimental validation of a processor-in-the-loop (PIL) architecture for an autonomous sports car. The considered vehicle is an all-wheel drive full-electric single-seater prototype. The retained PIL architecture includes all the modules required for autonomous driving at system level: environment perception, trajectory planning, and control. Specifically, the perception pipeline exploits obstacle detection algorithms based on Artificial Intelligence (AI), and the trajectory planning is based on a modified Rapidly-exploring Random Tree (RRT) algorithm based on Dubins curves, while the vehicle is controlled via a Model Predictive Control (MPC) strategy. The considered PIL layout is implemented firstly using a low-cost card-sized computer for fast code verification purposes. Furthermore, the proposed PIL architecture is compared in terms of performance to an alternative PIL using high-performance real-time target computing machine. Both PIL architectures exploit User Datagram Protocol (UDP) protocol to properly communicate with a personal computer. The latter PIL architecture is validated in real-time using experimental data. Moreover, they are also validated with respect to the general autonomous pipeline that runs in parallel on the personal computer during numerical simulation.