Rapidly-exploring Random Tree Research Articles

Integrated BIM-based simulation for automated time-space conflict management in construction projects

The time-space conflicts may have severe negative impacts on the productivity and safety level in a construction site. However, little attention is paid to propose an automated, integrated system for managing potential time-space conflicts, considering the site space system's dynamic nature. This study contributes to the body of knowledge by proposing a novel integrated approach to manage time-space conflicts automatically. The proposed hybrid approach is based on Discrete Event Simulation (DES), Building Information Modeling (BIM), and Rapidly-exploring Random Tree (RRT) path planning. All types of time-space conflicts during the entire project timeline are automatically detected, evaluated, and resolved based on predefined if-then rules. The proposed approach is applied successfully in a real case study to estimate the project duration considering site space limitations. The results indicate that the proposed system can considerably prevent the negative impacts of time-space conflicts, particularly in congested construction sites.

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.

FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs

This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.

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.

Abstraction-Guided Sampling for Motion Planning

Motion planning in continuous space is a fundamentalrobotics problem that has been approached from many per-spectives. Rapidly-exploring Random Trees (RRTs) usesampling to efficiently traverse the continuous and high-dimensional state space. Heuristic graph search methods uselower bounds on solution cost to focus effort on portions ofthe space that are likely to be traversed by low-cost solutions.In this work, we bring these two ideas together in a tech-nique called f -biasing: we use estimates of solution cost,computed as in heuristic search, to guide sparse sampling,as in RRTs. We see this new technique as strengthening theconnections between motion planning in robotics and combi-natorial search in artificial intelligence.

Adapting a Rapidly-Exploring Random Tree for Automated Planning

Rapidly-exploring random trees (RRTs) are data structures and search algorithms designed to be used in continuous path planning problems. They are one of the most successful state-of-the-art techniques as they offer a great degree of flexibility and reliability. However, their use in other search domains has not been thoroughly analyzed. In this work we propose the use of RRTs as a search algorithm for automated planning. We analyze the advantages that this approach has over previously used search algorithms and the challenges of adapting RRTs for implicit and discrete search spaces.

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.

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.

Complex Environment Path Planning for Unmanned Aerial Vehicles.

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

Cooperative Target Tracking in Aquatic Environment Using Dual Robotic Dolphins

This article proposes a modified rapidly exploring random tree (RRT)-based path planner and behavior-based cooperative tracking strategy for a dual robotic dolphin system to fulfill a cooperative target-tracking task. Specifically, with full consideration of both task requirements and mechatronic configuration, a robotic dolphin with a waist-caudal propulsive mechanism for thrust forces and differential bilateral flippers for maneuverability is developed. To satisfy the demand of fast path planning, a variant RRT algorithm is employed to generate feasible paths for the dual robotic dolphins with fewer waypoints, faster convergence speed, and better stability. Furthermore, a behavior-based approach in conjunction with centralized architecture is implemented to achieve high-level decision-making. Finally, simulations, analysis, as well as field experiments are carried out to verify the effectiveness of the proposed control scheme. The success of the experiments further offers insight into the mechanisms of cooperative multirobot target tracking in aquatic environments.

Unified robot task and motion planning with extended planner using ROS simulator

Safe and efficient exploration of unknown and unstructured environments is critical for an autonomous mobile robot in high-dimensional workspace. Autonomous exploration is essential to safely and robustly build a complete and accurate path in an environment map with obstacles. Task planning tends to find a sequence of discrete actions in solving the symbolic problems. Motion planning finds the optimal path by avoiding the geometric obstacles in the environment. Fusion of these two approaches is advantageous in finding a path in unknown environments. This paper presents a hybrid path planning approach, termed RobMAP (Robotic Motion and Action Planning) for achieving efficient navigation by performing desired tasks in a sequence and thereby generate the optimal path in a large- scale indoor environment. In achieving this, action-planning is done using Planning Domain Definition Language (PDDL) planner through ROSPlan framework and collision-free path is generated using sampling-based Rapidly exploring Random Tree (RRT) algorithm. The ROSPlan system is used in less constrained workspace and it estimates the optimized information gain in exploring each action. Further, the integration of these two methods leads to an intelligent motion planner that searches for an optimal path towards a goal based on the PDDL action planner specification. Extensive experimentation is performed using Robot Operating System (ROS) controlled Rviz tool on a synthetic indoor environment by executing a sequence of actions specified using PDDL. The Turtlebot is equipped with a laser sensor and it explores the environment map with higher precision and accuracy in finding the optimal path. The demonstrated results corroborate that the proposed method of integrating RRT with PDDL provides optimal solutions with lesser path length in much lesser run time. The accuracy, precision and coverage area are higher when compared with other exploration strategies used in the existing work.

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.

Folding Process Planning of Rigid Origami Using the Explicit Expression and Rapidly Exploring Random Tree Method

Abstract In this study, we propose a novel method for planning the folding process of a rigid origami mechanism, i.e., we explore the intermediate process of the mechanism from an initial state to a target state without self-intersection via a path-finding algorithm. A typical problem associated with a path-finding algorithm is that a feasible configuration space of rigid origami is a lower-dimensional subset of the entire parameter space. When all the folding angles are considered as free parameters to plan the folding process, it is generally not possible to obtain a feasible configuration via sampling. In this study, the parameters corresponding to the degree-of-freedom (DOF) are used as independent variables, and the remaining fold angles are considered as dependent variables that can be calculated via the explicit expression method (EEM). First, we explain the method for choosing the parameters related to DOF to represent the configuration of the origami mechanism. Then, we show the procedure for selecting a valid configuration from many possible configurations computed via EEM. For this purpose, we introduce criteria for each vertex to determine whether the two configurations can be continuously connected. Next, the method for planning the folding process of rigid origami is introduced via the rapidly exploring random tree (RRT) method. Finally, we implemented the folding process simulation platform and applied it to different patterns. The results of the experiments are presented.

Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles

This letter presents a novel on-line path planning method that enables aerial robots to interact with surfaces. We present a solution to the problem of finding trajectories that drive a robot towards a surface and move along it. Triangular meshes are used as a surface map representation that is free of fixed discretization and allows for very large workspaces. We propose to leverage planar parametrization methods to obtain a lower-dimensional topologically equivalent representation of the original surface. Furthermore, we interpret the original surface and its lower-dimensional representation as manifold approximations that allow the use of Riemannian Motion Policies (RMPs), resulting in an efficient, versatile, and elegant motion generation framework. We compare against several Rapidly-exploring Random Tree (RRT) planners, a customized CHOMP variant, and the discrete geodesic algorithm. Using extensive simulations on real-world data we show that the proposed planner can reliably plan high-quality near-optimal trajectories at minimal computational cost. The accompanying multimedia attachment demonstrates feasibility on a real OMAV. The obtained paths show less than 10% deviation from the theoretical optimum while facilitating reactive re-planning at kHz refresh rates, enabling flying robots to perform motion planning for interaction with complex surfaces.

Path planning for UAV based on improved dynamic step RRT algorithm

Path planning is an important problem in the field of unmanned aerial vehicles (UAVs). However, it is inefficient for many rapidly-exploring random tree (RRT) based methods to rapidly find a feasible solution in a complex environment. To solve the path planning problem for the UVA in a complex environment, an improved dynamic step size RRT algorithm combined with a new path length control strategy is proposed. Firstly, the algorithm adopts a biased-goal sampling strategy to guide the growth of the tree, and an expansion direction constraint strategy is adopted to limit the expansion direction of the tree. Secondly, an improved dynamic step size strategy is proposed to speed up the path searching process. Thirdly, a path length constraint strategy is designed, which is used to constrain the path length during the searching process for a solution. Finally, simulation results demonstrate that the proposed method achieves improvement in both computational time and the path quality.

RRT-Based Game Level Analysis, Visualization, and Visual Refinement

Automating parts of game creation benefits both professional and amateur game designers and much previous work has already made progress on this front. In this paper we tackle automating level design. We describe a general graph-based representation for game levels and present a preliminary system that leverages this representation. Our system automatically explores existing levels of a 2D platform game using the rapidly-exploring random tree (RRT) algorithm and constructs a compact graph representation from this exploration. Our system can also modify a graph representation on-the-fly to reflect user-directed changes to the existing level structure. This work constitutes an initial step toward the larger goal of automating level design in a general way.

Design Considerations for a Steerable Needle Robot to Maximize Reachable Lung Volume.

Steerable needles that are able to follow curvilinear trajectories and steer around anatomical obstacles are a promising solution for many interventional procedures. In the lung, these needles can be deployed from the tip of a conventional bronchoscope to reach lung lesions for diagnosis. The reach of such a device depends on several design parameters including the bronchoscope diameter, the angle of the piercing device relative to the medial axis of the airway, and the needle's minimum radius of curvature while steering. Assessing the effect of these parameters on the overall system's clinical utility is important in informing future design choices and understanding the capabilities and limitations of the system. In this paper, we analyze the effect of various settings for these three robot parameters on the percentage of the lung that the robot can reach. We combine Monte Carlo random sampling of piercing configurations with a Rapidly-exploring Random Trees based steerable needle motion planner in simulated human lung environments to asymptotically accurately estimate the volume of sites in the lung reachable by the robot. We highlight the importance of each parameter on the overall system's reachable workspace in an effort to motivate future device innovation and highlight design trade-offs.