RRT-based Path Research Articles

RRT-based Path Planning Design for Securing Navigation Performance in GNSS-challenged Urban Environments

RRT-based Path Planning Design for Securing Navigation Performance in GNSS-challenged Urban Environments

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.

Herding by caging: a formation-based motion planning framework for guiding mobile agents

We propose a solution to the problem of herding by caging: given a set of mobile robots (called herders) and a group of moving agents (called sheep), we guide the sheep to a target location without letting them escape from the herders along the way. We model the interaction between the herders and the sheep by defining virtual “repulsive forces” pushing the sheep away from the herders. This enables the herders to partially control the motion of the sheep. We formalize this behavior topologically by applying the notion of caging, a concept used in robotic manipulation. We demonstrate that our approach is provably correct in the sense that the sheep cannot escape from the robots under our assumed motion model. We propose an RRT-based path planning algorithm for herding by caging, demonstrate its probabilistic completeness, and evaluate it in simulations as well as on a group of real mobile robots.

An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints

This article presents a framework that extends a rapidly exploring random tree (RRT) algorithm to plan the motion for a wheeled robot under kinodynamic constraints. Unlike previous RRT-based path planning algorithms that apply complex steer functions during a path sampling phase, this framework uses a straight line to connect a pair of sampled waypoints such that an obstacle-free path can be quickly found. This path is further pruned by the short-cutting algorithm. Under the kinodynamic constraints, we propose a motion-control law that is guided by a pose-based steer function for the robot to reach its destination in a short time. A path deformation strategy is presented that shifts the waypoint away from the collision point such that the trajectory can be generated without any collision. Simulation results demonstrate that the proposed framework needs less computation to generate a smoother trajectory with shorter length than its peers, and experimental results show that simulated trajectories of our controller are very close to real ones and the performance is better than that of a prior pose-based controller.

RRT-based path planning for an intelligent litchi-picking manipulator

Aiming to realize the obstacle avoidance of the litchi-picking robot in dynamic and unstructured environments, an improved rapidly exploring random tree (RRT) algorithm is presented in this study. By establishing the collision detection model of the manipulator and obstacles, the obstacle avoidance path planning is carried out. The idea of target gravity was adopted to accelerate the path search speed. To optimize the path generated by RRT, a genetic algorithm (GA) and smooth processing were proposed. Performance tests in a virtual environment showed that the optimal combination of step sizes in the path search were 1.6 degrees for step size 1 and 2 degrees for step size 2. The time to achieve the path was 0.47 s, and the success rate was 100%. The path length was shortened by 20% after optimization. Experiments in the real environment were carried out for the 6 degrees of freedom (DOF) picking manipulator to pick litchi, and the results showed that the collision-free path planned by the proposed algorithm could successfully drive the manipulator from its initial position to the goal position without any collision.

Faster RRT-based Nonholonomic Path Planning in 2D Building Environments Using Skeleton-constrained Path Biasing

This paper presents a faster RRT-based path planning approach for regular 2-dimensional (2D) building environments. To minimize the planning time, we adopt the idea of biasing the RRT tree-growth in more focused ways. We propose to calculate the skeleton of the 2D environment first, then connect a geometrical path on the skeleton, and grow the RRT tree via the seeds generated locally along this path. We conduct batched simulations to find the universal parameters in manipulating the seeds generation. We show that the proposed skeleton-biased locally-seeded RRT (skilled-RRT) is faster than the other baseline planners (RRT, RRT*, A*-RRT, Theta*-RRT, and MARRT) through experimental tests using different vehicles in different 2D building environments. Given mild assumptions of the 2D environments, we prove that the proposed approach is probabilistically complete. We also present an application of the skilled-RRT for unmanned ground vehicle. Compared to the other baseline algorithms (Theta*-RRT and MARRT), we show the applicability and fast planning of the skilled-RRT in real environment.

Adaptive RRT를 사용한 고 자유도 다물체 로봇 시스템의 효율적인 경로계획

This paper proposes an adaptive RRT (Rapidly-exploring Random Tree) for path planning of high DOF multibody robotic system. For an efficient path planning in high-dimensional configuration space, the proposed algorithm adaptively selects the robot bodies depending on the complexity of path planning. Then, the RRT grows only using the DOFs corresponding with the selected bodies. Since the RRT is extended in the configuration space with adaptive dimensionality, the RRT can grow in the lower dimensional configuration space. Thus the adaptive RRT method executes a faster path planning and smaller DOF for a robot. We implement our algorithm for path planning of 19 DOF robot, AMIRO. The results from our simulations show that the adaptive RRTbased path planner is more efficient than the basic RRT-based path planner.

Adaptive rapidly-exploring random tree for efficient path planning of high-degree-of-freedom articulated robots

This article proposes a method for the path planning of high-degree-of-freedom articulated robots with adaptive dimensionality. For efficient path planning in a high-dimensional configuration space, we first describe an adaptive body selection that selects the robot bodies depending on the complexity of the path planning. Then, the involved joints of the selected body are included in the planning process. That is, it builds the C-space (configuration space) with adaptive dimensionality for sampling-based path planner. Next, by using adaptive body selection, the adaptive rapidly-exploring random tree (RRT) algorithm is introduced, which incrementally grows RRTs in the adaptive dimensional C-space. We show through several simulation results that the proposed method is more efficient than the basic RRT-based path planner, which requires full-dimensional planning.

Rapidly Exploring Random Tree Algorithm-Based Path Planning for Robot-Aided Optical Manipulation of Biological Cells

In numerous cellular applications, cells are transported to specific positions or extracted from complex cell solutions. Therefore, an efficient cell transportation path planner for these applications is important for avoiding collisions with other cells or obstacles. In this paper, a path planning approach to transporting cells using a robot-aided optical manipulation system is presented. Optical tweezers functions as a special end-effector in transporting a target cell to the desired position along the generated path. The path planner is designed based on the rapidly exploring random trees (RRT) algorithm for calculating a collision-free path for cell transportation. Both static and dynamic path planners are developed. For the dynamic path planner, an online monitoring strategy is employed to dynamically avoid collisions with randomly appeared obstacles caused by environmental influence such as the Brownian movement of microparticles. Experiments of transporting yeast cells are performed to demonstrate the effectiveness of the proposed approach. Note to Practitioners - Manipulations of cells and other microparticles represent an essential process for most cell-based bioengineering applications, such as cytopathology, cell sociology, and cytotaxonomy. Cell transportation, which is treated as a typical cell manipulation task, has recently received considerable attention because of its wide applications. This paper presents a novel approach to applying RRT-based path planner to cell transportation with a robot-aided optical manipulation system. The research outcome provides a unique solution to achieving cell transportation automatically and efficiently.

3D Robust Online Motion Planning for Steerable Needles in Dynamic Workspaces Using Duty-Cycled Rotation

This work presents a closed-loop strategy for 3D online motion planning of beveled steerable needles using duty-cycled rotation. The algorithm first selects an entry point that minimizes a multi-criteria cost function and then combines an RRT-based path planner with an intraoperative replanning algorithm and workspace feedback information to constantly update the needle inputs and adjust the trajectory. Simulations in a workspace based on a typical prostate needle steering scenario show that the algorithm is robust against disturbances and model uncertainties and can provide online trajectories to avoid obstacles even under the presence of physiological motion.

Parallel RRT-based path planning for selective disassembly planning

The planning of disassembly sequences requires the identification of the extraction trajectories of the different parts or assemblies. The failure to find these trajectories can make a planner fail to generate correct sequences or not evaluate potential solutions. In this paper, we analyze the disassembly path-planning problem, its relation to the general path-planning problem and the main differences between both of them, such as the lack of a target configuration. We present a modification of the rapid-growing random tree-based algorithm (RRT) that addresses these differences. RRTs are easily parallelized so we analyze two different parallelization methods using dual-core-based CPUs as well as the impact of the target selection probability of the algorithm in execution time. The method described is applied to several real-world and synthetic examples.