Rapidly-exploring Random Tree Algorithm Research Articles

An Improved RRT Based Path Planning with Safe Navigation

Path planning has been a crucial problem in robotics research. Some algorithms, such as Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT), have been proposed to tackle this problem. However, these algorithms have their own limitations when applying in a real-world domain, such as RoboCup Small-Size League (SSL) competition. This paper raises a novel improvement to the existing RRT algorithm to make it more applicable in real-world.

A Motion Planning Based on Selective Disassembly Sequence Planning Method

Selective disassembly sequence planning is one of the most important stages in product repairing and recycling. Due to the lack of generalization of existing methods, a motion planning based selective disassembly sequence planning method is proposed. Aiming at the feature of huge amount and irregular shapes of complex product parts, a motion planning method based on dynamic self-adapted multiple trees based rapidly-exploring random tree(RRT) algorithm is introduced to plan the disassembly path of the parts initially. Based on that, a hierarchical structure of the assembly is generated which leads to the generation of the disassembly constraint relationship graph of all the assembly parts. By analysis of the generated graph, the disassembly sequence of selected part can be obtained. Results from test on a part from a complex chassis assembly validate this approach.

휴리스틱 입력 분석을 이용한 RRT 기반의 Simulink/Stateflow 모델 테스트 케이스 생성 기법

본 논문은 Simulink/Stateflow 모델 기반의 테스트 케이스를 자동으로 생성하기 위하여, 휴리스틱 입력 분석을 이용한 Rapidly-exploring Random Tree(RRT) 기법을 제안한다. RRT는 모델 기반 블랙박스 테스트 케이스 생성 시 반드시 해결해야 되는 도달 가능성 문제를 효율적으로 해결할 수 있는 방법이지만, 모델의 내부 상태와 테스트 목표를 고려하지 않고 무작위로 모델의 입력을 생성하기 때문에 테스트 케이스 생성 효율이 떨어지는 단점이 있다. 제안하는 기법에서는 RRT를 확장해나갈 때 필요한 입력을, 모델의 현재 상태에서 만족 할 수 있는 테스트 목표를 분석하고 이를 달성할 수 있는 모델의 입력을 분석 결과에 따라 휴리스틱하게 결정함으로써, RRT의 장점을 보존하면서, 테스트 케이스 생성 효율을 높일 수 있다. 제안된 기법은 자동차에 사용되는 실 부품 ECU의 Simulink/Stateflow 모델을 대상으로 한 실험을 통해 성능이 평가되었으며, 기존 RRT와 비교하여 테스트 케이스 생성 효율이 높은 것을 보였다. This paper proposes a modified RRT (Rapidly exploring Random Tree) algorithm utilizing a heuristic input analysis and suggests a test case generation method from Simulink/Stateflow model using the proposed RRT algorithm. Though the typical RRT algorithm is an efficient method to solve the reachability problem to definitely be resolved for generating test cases of model in a black box manner, it has a drawback, an inefficiency of test case generation that comes from generating random inputs without considering the internal states and the test targets of model. The proposed test case generation method increases efficiency of test case generation by analyzing the test targets to be satisfied at the current state and heuristically deciding the inputs of model based on the analysis during expanding an RRT, while maintaining the merit of RRT algorithm. The proposed method is evaluated with the models of ECUs embedded in a commercial passenger's car. The performance is compared with that of the typical RRT algorithm.

Multi-agent motion planning for nonlinear Gaussian systems

In this paper, a multi-agent motion planner is developed for nonlinear Gaussian systems using a combination of probabilistic approaches and a rapidly exploring random tree (RRT) algorithm. A closed-loop model consisting of a controller and estimation loops is used to predict future distributions to manage the level of uncertainty in the path planner. The closed-loop model assumes the existence of a feedback control law that drives the actual system towards a nominal system. This ensures the uncertainty in the evolution does not grow significantly and the tracking errors are bounded. To trade conservatism with the risk of infeasibility and failure, we use probabilistic constraints to limit the probability of constraint violation. The probability of leaving the configuration space is included by using a chance constraint approach and the probability of closeness between two agents is imposed using an overlapping coefficient approach. We augment these approaches with the RRT algorithm to develop a robust path planner. Conflict among agents is resolved using a priority-based technique. Numerical results are presented to demonstrate the effectiveness of the planner.

Spline-Based RRT Path Planner for Non-Holonomic Robots

Planning in a cluttered environment under differential constraints is a difficult problem because the planner must satisfy the external constraints that arise from obstacles in the environment and the internal constraints due to the kinematic/dynamic limitations of the robot. This paper proposes a novel Spline-based Rapidly-exploring Random Tree (SRRT) algorithm which treats both the external and internal constraints simultaneously and efficiently. The computationally expensive numerical integration of the system dynamics is replaced by an efficient spline curve parameterization. In addition, the SRRT guarantees continuity of curvature along the path satisfying any upper-bounded curvature constraints. This paper presents the underlying theory to the SRRT algorithm and presents simulation and experiment results of a mobile robot efficiently navigating through cluttered environments.

Information-Theoretic Motion Planning for Constrained Sensor Networks

This paper considers the problem of online informative motion planning for a network of heterogeneous mobile sensing agents, each subject to dynamic constraints, environmental constraints, and sensor limitations. Previous work has not yielded algorithms that are amenable to such general constraint characterizations. In this paper, the information-rich rapidly-exploring random tree algorithm is proposed as a solution to the constrained informative motion planning problem that embeds metrics on uncertainty reduction at both the tree growth and path selection levels. The proposed algorithm possesses a number of beneficial properties, chief among them being the ability to quickly find dynamically feasible, informative paths, even subject to the aforementioned constraints. The utility of the proposed algorithm in efficiently localizing stationary targets is demonstrated in a progression of simulation results with both single-agent and multiagent networks. These results show that the information-rich rapidly-exploring random tree algorithm can be used online and in real-time to generate and execute information-rich paths in tightly constrained environments.

Improved path planning algorithm of rapidly-exploring random tree for biped robot

To solve the problems that the Rapidly-exploring Random Tree(RRT) path planning is unstable and not taking cost into consideration,an anytime RRT Algorithm was proposed.The algorithm produced an initial solution very quickly,and then improved its growth by reusing information from the previous trees.Besides,to improve the algorithm,a biased distribution was produced to save the cost by using a waypoint cache.The resulted approach produced an initial solution very quickly,and then improved the quality of this solution within given time.It was guaranteed that subsequent solution would be less costly than all previous ones by using the bound.In the biped robot's simulation experiment,compared to the initial algorithm,the number of search nodes created by the improved algorithm decreases from 883 to 704 and the efficiency increases approximately 25%.The simulation result demonstrates the effectiveness of the improved algorithm.

분할된 RRT 공간을 이용한 Simulink/Stateflow모델 테스트케이스 생성

This paper proposes a black-box based test case generation method for Simulink/Stateflow model utilizing the RRT algorithm which is a method to efficiently solve the path planning for complicated systems. The proposed method in the paper tries to solve the reachability problem with the RRT algorithm, which has to be solved for black-box based test case generations. A major problem of the RRT based test case generation algorithms is that the cost such as running time and required memory size is too much for complicated Stateflow model. The typical RRT algorithm expands rapidly-exploring random tree (RRT) in a single state space. But the proposed method expands it in dynamic state space based on the state of Simulink model, consequently reducing the cost. In the paper, a new definition of RRT state space, a distance measure and a test case generation algorithm are proposed. The performance of proposed method is verified through the experiment against Stateflow model.

Parallelizing RRT on Large-Scale Distributed-Memory Architectures

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the message passing interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion-planning problems and analyze the various factors influencing their performance.

Squeezed screw trajectories for smooth regrasping movements of robot fingers

In dextrous robotic manipulation we are often confronted with the problem that the robot fingers need to be repositioned on the object surface, either because a finger reaches its workspace limits or because the manipulation planner decides to do so due to its manipulation strategy. In this paper we propose a trajectory type for the purpose of regrasping, named “Squeezed Screw”. It is smooth and has a relatively simple shape. It is a superposition of a movement on an ellipse with a translation in an arbitrary direction in 3D. It can be generated from only eight input parameters. An algorithm was developed to calculate the path geometry from these parameters. A comparison with a Rapidly-exploring Random Tree algorithm points out the advantages of this novel trajectory type.

Computational path planner for product assembly in complex environments

Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.

Conditional Density Growth (CDG) model: a simplified model of RRT coverage for kinematic systems

SUMMARYIt has been shown before that the Rapidly Exploring Random Tree (RRT) algorithm is probabilistically and resolution complete; and that the probability of finding a particular solution path can be related to the number of nodes. However, little analysis exists on the rate at which the tree covers the configuration space. In this paper, we present a stochastic difference equation which models how the tree covers the configuration space as a function of the number of nodes in the tree. Using two simplifying assumptions, appropriate for holonomic, kinematic systems in expansive configuration spaces, we derive closed-form solutions for the expected value and variance of configuration space coverage, which only depend on two easily computable parameters. Using a grid-based coverage measurement, we present experimental evidence supporting this model across a range of dimensions, obstacle densities, and parameter choices. Collecting data from 1000 RRTs, we provide evidence that configuration space coverage concentrates tightly around the expected coverage predicted by the model; and the results of the Chi-squared test suggest that the distribution of coverage across these runs is highly Gaussian. Together these results enable one to predict the expected coverage, along with a confidence interval, after a certain number of nodes have been added to the tree. We also applied the model to an example with extremely narrow passages and to a system with non-holonomic kinematics. The expected value prediction is still qualitatively accurate; but the rate constant is reduced and the variance is higher. Overall, in addition to its theoretical value, the model may find future application as an online measure of search-progress and problem difficulty, useful for adaptive variants of the basic RRT algorithm.

A vision-based collision avoidance technique for micro air vehicles using local-level frame mapping and path planning

This paper presents a vision-based collision avoidance technique for small and miniature air vehicles (MAVs) using local-level frame mapping and path planning. Using computer vision algorithms, a depth map that represents the range and bearing to obstacles is obtained. Based on the depth map, we estimate the range, azimuth to, and height of obstacles using an extended Kalman filter that takes into account the correlations between obstacles. We then construct maps in the local-level frame using cylindrical coordinates for three dimensional path planning and plan Dubins paths using the rapidly-exploring random tree algorithm. The behavior of our approach is analyzed and the characteristics of the environments where the local path planning technique guarantees collision-free paths and maneuvers the MAV to a specific goal region are described. Numerical results show the proposed technique is successful in solving path planning and multiple obstacle avoidance problems for fixed wing MAVs.

Research on Assembly Path Planning and Optimization of Complex Structures

In order to solve the difficult problem of assembly path planning of complex structures,an obstacle and greedy rule based rapidly-exploring random tree(OG-RRT) algorithm is proposed.The algorithm is based on basic RRT algorithm,adopts the combination of random sampling,goal sampling and local sampling,guides the extended direction of the random tree with the collision information(collision normal vector and collision point) of the target part and obstacles,extends nodes by greedy rule along each direction with translation-after-rotation expansion strategy.The piecewise linear approximation method is utilized to optimize the original assembly path.An assembly path planning prototype system is developed and some examples are tested to verify feasibility of the proposed algorithm.

Obstacle Displacement Prediction for Robot MotionPlanning and Velocity Changes

This paper introduces a new path-motion planning method for autonomous mobile robot which should move safely in unknown Dynamic environment. The environment may have numbers of obstacles of arbitrary shape and obstacles are allowed to move. We describe our approach to solve the motion-planning problem, using neural networks-based technique .The neural network is used to model the obstacles motion and predict their movements. According to one step ahead prediction of obstacle's trajectory, a rule-based technique for avoiding obstacles by adjusting the robot's velocity is suggested .This sensor based online method gives a safe but near optimal path. Simulation examples of generated path with proposed techniques will be presented and show the efficiency of the method. used to know the one step ahead and final destination of the obstacle. The prediction was combined to a POMDP method to control robot movement. In another work a probabilistic model of uncertainty is used in (6). The planning algorithm is based on an extension of the Rapidly-exploring Random Tree algorithm, where the obstacles trajectory and the probability of collision are explicitly taken into account. The algorithm is used in a partial motion planner, and the probability of collision is updated in real-time. The proposed method in (7) combines a dynamic occupancy grid with the Probabilistic Velocity Obstacle (PVO). The velocity space was considered and, under the hypothesis of linear constant velocity of the obstacles, the probability of collision was estimated for each possible linear velocity of the robot. The limits of the perception system, such as sensor range and occlusions, and the uncertainties on position and velocity estimation, contribute directly to the computation of the probability of collision. In all of these works because of inclusion of robot and obstacles' velocity in the formulation, the complexity of solution increases. We choose not to include the speed of the robot, or include speed actions in the action set and In the formulation of the problem. We prefer to use robot or obstacle displacement instead, to make ease in computation. This choice would further decrease the complexity of solution.

A Probabilistically Robust Path Planning Algorithm for UAVs Using Rapidly-Exploring Random Trees

The computationally efficient search for robust feasible paths for unmanned aerial vehicles (UAVs) in the presence of uncertainty is a challenging and interesting area of research. In uncertain environments, a "conservative" planner may be required but then there may be no feasible solution. In this paper, we use a chance constraint to limit the probability of constraint violation and extend this framework to handle uncertain dynamic obstacles. The approach requires the satisfaction of probabilistic constraints at each time step in order to guarantee probabilistic feasibility. The rapidly-exploring random tree (RRT) algorithm, which enjoys the computational benefits of a sampling-based algorithm, is used to develop a real-time probabilistically robust path planner. It incorporates the chance constraint framework to account for uncertainty within the formulation and includes a number of heuristics to improve the algorithm's performance. Simulation results demonstrate that the proposed algorithm can be used for efficient identification and execution of probabilistically safe paths in real-time.

비용 인지 RRT 경로 계획 알고리즘

In this paper, we propose a cost-aware Rapidly-exploring Random Tree (RRT) path planning algorithm for mobile robots. A mobile robot is presented with a cost map of the field of interest and assigned to move from one location to another. As a robot moves, the robot is penalized by the cost at its current location according to the cost map. The overall cost of the robot is determined by the trajectory of the robot. The goal of the proposed cost-aware RRT algorithm is to find a trajectory with the minimal cost. The cost map of the field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware RRT algorithm extends the basic RRT algorithm by considering the cost map when extending a motion segment. We show that the proposed algorithm gives an outstanding performance compared to the basic RRT method. We also demonstrate that the use of rejection sampling can give better results through extensive simulation.

Decentralized path planning for multi-agent teams with complex constraints

This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent's incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team's overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates' plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.

Fault tolerant motion planning of robotic manipulators based on a nested RRT algorithm

PurposeThe purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.Design/methodology/approachAnother RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.FindingsIt is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.Originality/valueThe paper presents a new nested RRT algorithm for fault tolerant motion planning.

Finite Receding-Horizon Incremental-Sampling Tree With Application to a Fixed-Wing UAV

An algorithm called Finite Receding-Horizon Incremental-Sampling Tree (RH-IST) will be presented. It contains elements from the well-known Rapidly Exploring Random Tree algorithm and receding-horizon framework to provide a computationally inexpensive planning algorithm for vehicles with limited sensing capability in uncharted environments. To resemble a finite planning horizon property in the algorithm, a deterministic configuration space sampling is conducted on the surface of a sphere around the root of the tree. To produce near optimal trajectories, samples are connected according to their cumulative costs, resulting in an optimization heuristic. In the tree construction phase, each branch of the tree consists of the trajectory of a fixed-wing unmanned aerial vehicle. The steering method necessary to construct the branches comprises of a closed-loop time-domain simulation on guidance level with a non-linear 3-D mass point representation of the plant, a path following controller and a reference path based on Bézier-splines. The feasible best branch of the RH-IST is chosen to provide the input for the execution of the plant based on the evaluation of a cost-functional. Simulations demonstrate the adaptive behavior of the RH-IST in a 3-D urban environment with static and unknown obstacles.