Bidirectional Rapidly-exploring Random Trees Research Articles

A novel global path planning method for robot based on dual-source light continuous reflection

Aiming to address the problem of robot path planning in environments containing narrow passages, this paper proposes a novel global path planning method: the DSR (Dual-source Light Continuous Reflection Exploration) algorithm. This algorithm, inspired by the natural reflection of light, employs the concept of continuous reflection for path planning. It can efficiently generate an asymptotically optimal path on the map containing narrow passages. The DSR algorithm has been evaluated on different maps with narrow passages and compared with other algorithms. In comparison with the bidirectional Rapidly-exploring Random Tree algorithm, the DSR algorithm achieves a significant reduction in both path length (by 27.08% and 34.35%) and time consumption (by 98.47% and 91.03%). Numerical simulations and experimental analysis have demonstrated the excellent performance of the DSR algorithm.

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

AbstractThis 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.

Dynamic Path Planning and Motion Control of Microrobotic Swarms for Mobile Target Tracking

Magnetic field-driven microrobotic swarms have drawn extensive attention, especially in the field of automatic control. Realizing dynamic path planning and motion control of microrobotic swarms for mobile target tracking is one of the important tasks that still remains unsolved. In this paper, we firstly present an enhanced bidirectional rapidly-exploring random tree star (EB-RRT*) algorithm considering the physical size of the swarm to dynamically plan the optimal path for obstacle avoidance. An image-guided motion controller, which consists of a direction controller and a Genetic Algorithm based Linear Quadratic Regulator (GA-LQR) velocity controller, is then proposed to realize mobile target tracking using microrobotic swarms. Targeted bursting algorithm is subsequently developed to meet the requirement of tracking high-speed ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m/s$</tex-math> </inline-formula> ) mobile targets. Simulations are performed to validate the proposed methods and obtain the proper ranges of the input parameters for the controllers. Finally, the control effectiveness of mobile target tracking in different conditions and environments is validated by experimental results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to develop an effective control scheme for mobile target tracking using microrobotic swarms. Conventional control schemes mainly focus on the control of single microrobots to reach static targets, and thus the desired path is fixed once planned. In addition, the motion of single monolithic microrobots can be modelled precisely. However, in mobile target tracking using microrobotic swarms, dynamic planning algorithms are demanded to frequently update the desired path. Swarms consisting of millions of micro-agents are also difficult to be modelled due to the complex agent-agent interactions. In this work, an effective control scheme consisting of a dynamic path planner, a motion controller and a targeted bursting unit is developed. Real-time dynamic paths will be planned even though the positions of the swarm and the target change rapidly. The precise control of the swarm direction and velocity are achieved, and moreover, using the targeted bursting algorithm, the swarm can be accelerated to approach mobile targets accurately with higher efficiency. Experimental results validates the proposed tracking strategy in different environments with virtual obstacles. The proposed control scheme paves the way for a better understanding of advanced motion control methods for microrobotic swarms.

A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.

A probability smoothing Bi-RRT path planning algorithm for indoor robot

Bidirectional rapidly exploring random trees (Bi-RRTs) have been widely applied in path planning and have been demonstrated to yield the optimal path for indoor robots. However, the quality of the initial solution is not guaranteed, and the convergence speed to the optimal solution is slow. To overcome these limitations, this paper proposes a novel probability-smoothing Bi-RRT (PSBi-RRT) algorithm for path planning. Specifically, the kinematic model is established, and the posture of the indoor robot is estimated. Thereafter, a target biasing strategy is introduced to select sampling points in the sampling region. In addition, a node correction mechanism is applied to correct node coordinates to reduce the collision probability in cluttered environments. Finally, two trees are connected using the direct connection method. The θ-cut mechanism is designed to remove the redundancy points, and the remaining nodes are connected using a triangular inequality-based RRT to ensure that the final path is optimal. Furthermore, the proposed method benefits from the properties of Bi-RRT, which offers low-cost solutions with fewer iterations compared with Bi-RRT alone. Different simulations were performed to demonstrate the significance of PSBi-RRT in comparison with Bi-RRT, InformedBi-RRT*, and QuickBi-RRT*.

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.

Efficient Path Planning for a Microrobot Passing through Environments with Narrow Passages.

This paper presents an efficient path-planning algorithm for microrobots attempting to pass through environments with narrow passages. Because of the extremely small size of a microrobot, it is suitable for work in this kind of environment. The rapidly exploring random tree (RRT) algorithm, which uses random sampling points, can quickly explore an entire environment and generate a sub-optimal path for a robot to pass through it; however, the RRT algorithm, when used to plan a path for a microrobot passing through an environment with narrow passages, has the problem of being easily limited to local solutions when it confronts with a narrow passage and is unable to find the final path through it. In light of this, the objectives of the considered path planning problem involve detecting the narrow passages, leading the path toward an approaching narrow passage, passing through a narrow passage, and extending the path search more efficiently. A methodology was proposed based on the bidirectional RRT in which image processing is used to mark narrow passages and their entrances and exits so that the bidirectional RRT can be quickly guided to them and combined with the deterministic algorithm to find paths through them. We designed the methodology such that RRT generates the sampling points for path growth. The multiple importance sampling technique is incorporated with bidirectional RRT, named MIS-BiRRT, to make the path grow faster toward the target point and narrow passages while avoiding obstacles. The proposed algorithm also considers multiple candidate paths simultaneously to expand the search range and then retain the best one as a part of the planning path. After validation from simulation, the proposed algorithm was found to generate efficient path planning results for microrobots to pass through narrow passages.

An Enhanced Navigation Algorithm with an Adaptive Controller for Wheeled Mobile Robot Based on Bidirectional RRT

A navigation algorithm providing motion planning for two-wheeled mobile robots is proposed in this paper. The motion planning integrates path planning, velocity planning and controller design. Bidirectional rapidly-exploring random trees algorithms (RRT) with path pruning and smoothing mechanism are firstly used to obtain a collision-free path to the robot destination with directional continuity. Secondly, velocity planning based on trapezoidal velocity profile is used in both linear and angular velocities, but the position error of the endpoint of the curve appears due to the coupling problem of the nonlinear system. To reduce the error, an approximation method is used to gradually modify several parts of time length of the trapezoidal velocity profile, so the continuity of the path can still be maintained. Thirdly, the controller keeping the robot on the planned path and velocity is designed based on the dynamic model of the robot. The parameters of this controller are estimated by the adaptive low, and the gain of controller is dynamic adjusted by fuzzy logic control to avoid the case that the control value is saturated. The controller stability and the convergence of tracking error is guaranteed by Lyapunov theory. Simulation results are presented to illustrate the effectiveness and efficiency.

Application of bidirectional rapidly exploring random trees (BiRRT) algorithm for collision-free trajectory planning of free-floating space manipulator

AbstractOn-orbit servicing and active debris removal missions will rely on the use of unmanned satellite equipped with a manipulator. Capture of the target object will be the most challenging phase of these missions. During the capture manoeuvre, the manipulator must avoid collisions with elements of the target object (e.g., solar panels). The dynamic equations of the satellite-manipulator system must be used during the trajectory planning because the motion of the manipulator influences the position and orientation of the satellite. In this paper, we propose application of the bidirectional rapidly exploring random trees (BiRRT) algorithm for planning a collision-free trajectory of a manipulator mounted on a free-floating satellite. A new approach based on pseudo-velocities method (PVM) is used for construction of nodes of the trajectory tree. Initial nodes of the second tree are selected from the set of potential final configurations of the system. The proposed method is validated in numerical simulations performed for a planar case (3-DoF manipulator). The obtained results are compared with the results obtained with two other trajectory planning methods based on the RRT algorithm. It is shown that in a simple test scenario, the proposed BiRRT PVM algorithm results in a lower manipulator tip position error. In a more difficult test scenario, only the proposed method was able to find a solution. Practical applicability of the BiRRT PVM method is demonstrated in experiments performed on a planar air-bearing microgravity simulator where the trajectory is realised by a manipulator mounted on a mock-up of the free-floating servicing satellite.

Efficient Robot Motion Planning Using Bidirectional-Unidirectional RRT Extend Function

In this article, based on the rapidly-exploring random tree (RRT), we propose a novel and efficient motion planning algorithm using bidirectional RRT search. First, a RRT extend function is used to organize the sampled states under kinodynamic constraints. Meanwhile, the bidirectional search strategy is implemented to grow a forward tree and backward tree simultaneously in the tree extension process. When these two trees meet each other, the backward tree will act as a heuristic to guide the forward tree to continuously grow toward the goal state, where the algorithm switches to unidirectional search mode. Therefore, the two-point boundary value problem (BVP) in the connection process is avoided, and the extension process gets much accelerated. We also prove that probabilistic completeness is guaranteed. Numerical simulations are conducted to demonstrate that the proposed algorithm performs much better than the state-of-the-art algorithms in different environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to develop an efficient sampling-based motion planning algorithm for mobile robots. Conventional sampling-based algorithms are time-consuming to find a feasible solution under differential constraints. When applying bidirectional search strategy to improve them, the complex 2-point BVP is required to solve. In this article, the backward free is regarded as a heuristic to guide the tree growth. On the one hand, the advantage of bidirectional search is retained. On the other hand, the 2-point BVP is avoided. Therefore, the bidirectional-unidirectional technique can achieve efficient robot motion planning. The proposed algorithm can be extended to other specified sampling-based algorithms to further improve their performance. Besides, it can be also applied to autonomous driving, service robot and medical robots to achieve efficient motion planning.

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.

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.

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.

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.

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.

Deep learning-based optimization for motion planning of dual-arm assembly robots

With the rapid technological and economic development, a growing number of companies are employing robots for their production and service operations. Motion planning is a fundamental topic in robotics that has received wide attention due to its importance in the development of industry 4.0 and intelligent manufacturing systems. This study sought to develop a deep learning-based optimization algorithm for planning collision-free trajectories of dual-arm assembly robots in complex operational environments. Given the high dimensionality of the robotic motion patterns, a Bi-directional Rapidly-exploring Random Tree integrated with the Long Short-term Memory (LSTM-BiRRT) method is proposed to enhance the effectiveness and efficiency of the planning process. Numerical experiments demonstrated that the LSTM-BiRRT algorithm outperforms the state-of-the-art approaches developed for motion planning of dual-arm robots in both two- and three-dimensional environments. The developed algorithm reduces the path length of the robotic operations at a significantly shorter computational time. The LSTM-BiRRT algorithm can serve as a strong benchmark for future developments as well as applications in the process autonomy across intelligent supply chains.

Sea Ice Warning Visualization and Path Planning for Ice Navigation Based on Radar Image Recognition

Ice navigation is currently one of the important issues in path planning studies. Most studies are using sea ice models or ice charts for large-scale path planning, but the weather and ice conditions in the polar waters usually change quickly and are not easy to predict. Under these conditions, small-scale path planning that can be adjusted in time for sea ice collision avoidance is needed. This study uses marine radar imaging to reconstruct the ice navigation scene, and establishes a visual-ization function of sea ice warning on the radar image. We combine the bidirectional rapidly-exploring random tree (B-RRT) algorithm and the greedy algorithm to propose a path planning algorithm that generates various path planning schemes for ice navigation. After a number of path planning schemes are quickly generated, the path risk index proposed in this study can be used to evaluate and choose a relatively optimal path planning scheme with lower risk, as well as serve as an assistant reference for path planning decisions in ice navigation.

Hybrid Bidirectional Rapidly Exploring Random Tree Path Planning Algorithm with Reinforcement Learning

The randomness of path generation and slow convergence to the optimal path are two major problems in the current rapidly exploring random tree (RRT) path planning algorithm. Herein, a novel reinforcement-learning-based hybrid bidirectional rapidly exploring random tree (H-BRRT) is presented to solve these problems. To model the random exploration process, a target gravitational strategy is introduced. Reinforcement learning is applied to the improved target gravitational strategy using two operations: random exploration and target gravitational exploration. The algorithm is controlled to switch operations adaptively according to the accumulated performance. It not only improves the search efficiency, but also shortens the generated path after the proposed strategy is applied to a bidirectional rapidly exploring random tree (BRRT). In addition, to solve the problem of the traditional RRT continuously falling into the local optimum, an improved exploration strategy with collision weight is applied to the BRRT. Experimental results implemented in a robot operating system indicate that the proposed H-BRRT significantly outperforms alternative approaches such as the RRT and BRRT. The proposed algorithm enhances the capability of identifying unknown spaces and avoiding local optima.

Kinematic Constrained Bi-directional RRT with Efficient Branch Pruning for robot path planning

In this paper, we present a novel path planning algorithm called Kinematic Constrained Bi-directional Rapidly-exploring Random Tree with Efficient Branch Pruning (KB-RRT*), which is designed on the basis of Bi-directional Rapidly-exploring Random Tree (Bi-RRT) method. The KB-RRT* is suitable for path planning of a differential drive mobile robot. By incorporating the kinematic constraints, the KB-RRT* can avoid unnecessary growth of the tree and quickly find a feasible path for the agent. In addition, the proposed efficient branch pruning strategy helps the generated state find a better parent state and delete the edges with high cost around it. Therefore, the KB-RRT* achieves fast and high-quality path planning. The results of numerical simulations reveal that the proposed KB-RRT* can achieve better performance compared with the conventional path planning algorithms.

Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.