Improved RRT Algorithm Research Articles

Research on multi-vehicle formation control based on improved artificial potential field method

Multi-vehicle formation can perform various special tasks in unstructured environment. How to take into account the safety of vehicles in avoiding obstacles and the ability to maintain formation has a certain research value. In this paper, the four-circle model of vehicle is established first, and the circle radius is adjusted according to the state of vehicle, so as to describe the safety boundary of vehicle. The improved RRT algorithm is used for the whole route planning, and the discrete path points are used as vehicle guidance. Then the artificial potential field is constructed, and the formation coordination potential field is proposed, so that the vehicles can cooperate with other vehicles to keep the preset formation as far as possible when avoiding obstacles. Then the control quantity of the vehicle is calculated according to the force condition of the vehicle in the potential field by the double exponential sliding mode control method. Finally, the effectiveness of the method is verified by the simulation experiments of triangle formation and circular formation under different working conditions, and the formation error is reduced by about 20%.

Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm

Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm

A Motion Planning Method for 6-DOF Mechanical Arm based on Improved RRT Algorithm

A Motion Planning Method for 6-DOF Mechanical Arm based on Improved RRT Algorithm

Path Planning for Robotic Arms Based on an Improved RRT Algorithm

Path Planning for Robotic Arms Based on an Improved RRT Algorithm

An Intelligent Obstacle Avoidance Method for Six Degree of Freedom Robotic Arm Based on Assembly Sequence Planning

Abstract Autonomous operation planning of robotic arms under complex task constraints is still a difficult problem in the current research in this direction, and the existing operation planning methods are facing challenges and urgently need to break through the technical bottleneck. This paper proposes an improved RRT algorithm for six-degree-of-freedom robotic arms, which can be used for obstacle avoidance path planning. Based on the assembly sequence planning method, combining the advantages of RRT and RRT-connect algorithm, applying the idea of target sampling to strengthen the convergence of the algorithm to the target point search, introducing the greedy algorithm to improve the efficiency of the algorithm, combining with the five times of the B-spline interpolation on the path of the smoothing optimization, to shorten the planning path time and length. The Robotics Toolbox toolkit in MATLAB is used to count the time, number of samples, and path length required for the planning of RRT and the improved RRT algorithm, and the dynamic planning library is used to configure the algorithm and verify the path planning effect of the improved algorithm. Compared with the traditional RRT algorithm, using the proposed SDPS-RRT Connect algorithm for six-degree-of-freedom robotic arm path planning, the minimum distance of the connecting rod from the obstacle is 51.4948cm, which is greater than the standard safety distance of 45cm, which verifies the feasibility of the algorithm in practical applications.

Improved RRT global path planning algorithm based on Bridge Test

The RRT algorithm based on random sampling is widely used in planning problems with non-holonomic constraints. In indoor environments, narrow passages exist in the map, and the RRT method suffers from slow convergence speed and poor path quality. Additionally, the typical feature of large indoor room spaces with small exits creates traps where RRT can easily become stuck, making it difficult to find a path quickly. To solve the above problems, this paper proposes an improved RRT algorithm. This method first uses the simplified Bridge Test and point cloud clustering algorithm to locate the narrow passages on the map and place the root nodes in the narrow passage. Then grow multiple random trees from the root nodes, starting and endpoints. As the number of samples increases, the random trees gradually expand and connect to each other. The trees are connected to each other to build a complete path. Finally, the initial path is optimized and the final planning result is obtained. The simulation of the improved RRT, RRT*, RRT, and Bi-RRT is carried out. The simulation results show that the improved RRT algorithm proposed in this paper outperforms in terms of path length, number of exploring nodes, and processing time. We also tested 4 algorithms on a real robot, and the results prove the practical value of the improved algorithm.

Auxiliary Steering Control of Vehicle Driving with Force/Haptic Guidance

The rapid development of the automobile industry has resulted in the development of many vehicles, increased traffic, and frequent accidents. The complexity of road conditions is a major contributor to the occurrence of traffic accidents. Drivers are distracted and hence unable to fully observe all road information and make optimal and timely driving decisions. This study proposes an auxiliary steering control system with force/tactile guidance (ASCFT) and its corresponding control strategy to address this problem. We combined vehicle autonomous path planning based on road condition information and the human–machine sharing control strategy, which integrated the manipulative force of the driver and a virtual guidance force on the steering wheel. Consequently, the ASCFT eliminated the mechanical connection between the steering wheel and the steering wheels in favor of a force/tactile-assisted steering structure, providing the driver with a sense of steering force based on road information. Additionally, we proposed a smooth vehicle trajectory optimization method based on the improved RRT algorithm and a path-following controller based on the forecast information to achieve auxiliary safety driving. The ASCFT’s performance was confirmed through constructing a fixed-base simulator experimental platform with the ASCFT. The results revealed that at the vehicle speed of 60 km/h and a handwheel rotation of 60°, the steering wheel was instantly released and turned back in about 3.5 s. Furthermore, predictive haptic feedback warned the driver of an upcoming obstacle.

An improved RRT algorithm based on prior AIS information and DP compression for ship path planning

The slow convergence, excessive turning points and non-smooth path generation are significant challenges in ship path planning by existing RRT-related algorithms. To address these issues, this paper proposes a novel approach called PI-DP-RRT, which combines prior automatic identification system (AIS) information and Douglas-Peucker (DP) compression for ship path planning. Firstly, we cluster the available AIS data to construct guide regions, the guidance region can provide information to guide the target in the RRT algorithm. Next, we improve the algorithm's sampling strategy based on the guide region by using paranoid sampling, which increases convergence rate. Finally, we optimize the path by applying the improved DP algorithm and a new path optimization method to enhance its smoothness and practicability. Comparative simulation experiments are conducted in real scenarios and challenging environments. The results indicate that the proposed PI-DP-RRT algorithm outperforms other RRT-related algorithms in terms of efficiency, achieving a good balance between algorithm efficiency and accuracy. The planned path reduces the turning range and improves the smoothness of the path, which promotes the safety and efficiency of ship navigation.

A Motion Planning Method for Unmanned Surface Vehicle Based on Improved RRT Algorithm

Aiming at the problem that the path search rules in the traditional path planning methods are divorced from the actual maneuverability of an unmanned surface vehicle (USV), a motion planning method of state prediction rapidly exploring random tree (spRRT) is proposed. This method retains the discrete search of the original rules of RRT while adding the continuity of the motion of USV. Firstly, the state information for each movement (position, yaw angle, velocity, etc.), is calculated based on the mathematical model of USV’s motion which takes into account the complete dynamic constraints. Secondly, this information is added to the RRT path search rules to predict the state points that can be reached by the USV. Furthermore, in order to improve search efficiency and reduce cost, spRRT is enhanced by an elliptic sampling domain (spRRT-Informed). The simulation results indicate that spRRT can effectively plan smooth paths for smoothly navigating USV. The inclusion of the USV motion model has improved steering performance by an average of over 40%. Additionally, the spRRT-Informed enhanced with sampling optimization strategy improves performance by at least 10% over spRRT in terms of sailing time and distance of the path. The results of the simulation conducted in a realistic scenario validate that spRRT-Informed can be used as a reference for practical applications.

Robot Motion Planning Based on Improved RRT Algorithm and RBF Neural Network Sliding

Robot Motion Planning Based on Improved RRT Algorithm and RBF Neural Network Sliding

Indoor Top Mobile Service Robot Based on Improved RRT Algorithm

An indoor top mobile service robot based on the improved RRT algorithm is proposed. The robot is mainly composed of a lifting truss platform and a manipulator, which avoids the mapping and positioning of complex ground and obstacle avoidance planning. At the same time, the kinematics analysis of the manipulator is carried out. In view of the function of obstacle avoidance planning when the manipulator completes the task, the traditional RRT algorithm is improved to overcome the shortcomings of the low efficiency of path planning in the traditional RRT algorithm, and the planned path quality is uncertain and not smooth enough. The simulation environment is built based on ROS platform to verify the feasibility of the algorithm. To a certain extent, it improves the autonomy of robots to complete tasks.

Inland Waterway Ship Path Planning Based on Improved RRT Algorithm

Ship path planning is crucial for the shipping industry, especially for the development of autonomous ships. Many algorithms have been developed over the last few decades to solve the ship path planning problem. However, it is still challenging for ship path planning in an inland waterway. In this paper, an improved RRT algorithm for ship path planning in complex inland waterways is proposed. The improved algorithm has a path shearing and smoothing module, and the function of keeping a safe distance between a moving ship and obstacles. In addition, the algorithm has been tested in two inland waterway scenarios, and the results have confirmed its feasibility and reliability. The path planning algorithm proposed in this research seeks to reduce the risks faced by ship navigation in inland water. It has theoretical and practical significance in improving navigation safety in complex inland waters.

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.

An improved RRT algorithm for automatic parking path planning

An improved RRT algorithm for automatic parking path planning

Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm

To improve the path planning efficiency of a robotic arm in three-dimensional space and improve the obstacle avoidance ability, this paper proposes an improved artificial potential field and rapid expansion random tree (APF-RRT) hybrid algorithm for the mechanical arm path planning method. The improved APF algorithm (I-APF) introduces a heuristic method based on the number of adjacent obstacles to escape from local minima, which solves the local minimum problem of the APF method and improves the search speed. The improved RRT algorithm (I-RRT) changes the selection method of the nearest neighbor node by introducing a triangular nearest neighbor node selection method, adopts an adaptive step and generates a virtual new node strategy to explore the path, and removes redundant path nodes generated by the RRT algorithm, which effectively improves the obstacle avoidance ability and efficiency of the algorithm. Bezier curves are used to fit the final generated path. Finally, an experimental analysis based on Python shows that the search time of the hybrid algorithm in a multi-obstacle environment is reduced to 2.8 s from 37.8 s (classic RRT algorithm), 10.1 s (RRT* algorithm), and 7.4 s (P_RRT* algorithm), and the success rate and efficiency of the search are both significantly improved. Furthermore, the hybrid algorithm is simulated in a robot operating system (ROS) using the UR5 mechanical arm, and the results prove the effectiveness and reliability of the hybrid algorithm.

Research on Path Planning Algorithm of Autonomous Vehicles Based on Improved RRT Algorithm

Research on Path Planning Algorithm of Autonomous Vehicles Based on Improved RRT Algorithm

Research on Force Analysis and Path Planning of Flexible Needle Puncture Bending Modelling

Through the force analysis of the flexible needle puncture process, the corresponding mechanical model is established, and the main factors that cause the flexible needle bending deformation are analysed, and the bending model of the flexible needle puncture process is initially established. By discretizing the bending model, combined with experimental verification, a relatively complete bending model is obtained. Based on the RRT algorithm, the goal guidance algorithm, the optimal path selection algorithm, etc. are proposed, combined with the bending model of the flexible needle, and an improved RRT algorithm suitable for the flexible needle insertion method is established. Through simulation analysis and combined with experimental verification, it is concluded that path planning based on the improved RRT algorithm can bypass important human organs and soft tissues more flexibly, accurately puncture the target position, and achieve relatively safe surgery.

Research on local path planning based on improved RRT algorithm

In order to solve the local path planning of self-driving car in the structured road environment, an improved path planning algorithm named Regional-Sampling RRT (RS-RRT) algorithm was proposed for obstacle avoidance conditions. Gaussian distribution sampling and local biasing sampling were integrated to improve the search efficiency in the sampling phase. In the expansion phase, considering the actual size of the vehicle and obstacles, combined with the goal of safety and comfort, the separating axis theorem (SAT) method and vehicle dynamics were used to detect the collision among vehicle and surrounding obstacles in real time. In the post-processing stage, the driver’s driving consensus and path smoothing algorithm were combined to correct the planning path. In order to track the generated path, the MPC tracking algorithm was designed based on the Four-Wheel-Independent Electric Vehicle (FWIEV) model. The co-simulation software platform of CarSim and MATLAB/Simulink was employed to verify the effectiveness and feasibility of the path planning and tracking algorithm. The results show that compared with basic RRT and Goal-biasing RRT, the proposed RS-RRT algorithm has advantages in terms of number of nodes, path length and running time. The generated path can meet the FWIEV dynamics and path tracking requirements.

Application of Improved RRT Algorithm in Intelligent Vehicle Path Planning Under Complicated Environment

Application of Improved RRT Algorithm in Intelligent Vehicle Path Planning Under Complicated Environment

Path Planning of Continuum Robot Based on Path Fitting

The Continuum Robot has a multiredundant dof structure, which is extremely advantageous in the unstructured environment, and can complete such tasks as aircraft fuel tank inspection. However, due to its complex kinematics and coupling of joint motion, its motion path planning is also a challenging task. In this paper, a path planning method for Continuum Robot based on an equal curvature model in an aircraft fuel tank environment is proposed. Considering the complexity of calculation and the structural characteristics of Continuum Robot, a feasible obstacle avoidance discrete path is obtained by using the improved RRT algorithm. Then, joint fitting is performed on the existing discrete path according to the kinematic model of Continuum Robot, joint obstacle avoidance was conducted in the process of fitting, and finally, a motion path suitable for the Continuum Robot was selected. A reasonable experiment is designed based on MATLAB, and simulation and analysis results demonstrate excellent performance of this method and feasibility of path planning.