Rapidly-exploring Random Tree Algorithm Research Articles

Bio-inspired mobile robot design and autonomous exploration strategy for underground special space

Purpose To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot. Design/methodology/approach A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition. Findings According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task. Originality/value Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter

Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.

Obstacle avoidance motion planning for space redundant manipulator based on improved RRT algorithm

In addressing the challenge of obstacle-avoidance motion planning for space redundant manipulators operating in environments with obstacles, an improved method based on a rapidly exploring random tree (RRT) algorithm is proposed. The method first adopts a target probabilistic sampling strategy to enhance the target orientation. Secondly, an artificial potential field is constructed to steer the growth of the node tree in joint space. Subsequently, a collision detection model between the manipulator and obstacles is established based on the bounding capsule and bounding sphere in Cartesian space. Finally, the paths are pruned and smoothed with a cubic Bezier curve. The simulation results validate the method’s capacity to effectively plan a collision-free path for the space redundant manipulator and the joint motion trajectory is smooth.

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.

ERRRT-A*: A fast path planning algorithm suitable for large-scale scenes

In large-scale scenes, how to quickly obtain paths while ensuring the shortest possible path length is a key issue. Rapidly-exploring Random Tree (RRT) have the characteristic of quickly exploring the state space, but it is often difficult to obtain a short path. To overcome this problem, this paper proposes an improved RRT algorithm based on equidistance retention strategy and A* local search(ERRRT-A*). First, RRT is used for large-step global fast exploration to obtain approximate paths. Then, an equidistance retention strategy is used to discard most of the points and retain a small number of key points. Finally, A* is used to search between each segment to obtain a new path. The ERRRT-A* algorithm is compared with other commonly used algorithms on maps of different size in terms of path length and planning time. Simulation results indicate that compared with other algorithms, this algorithm achieves fast planning in large-scale scenes while obtaining short path length, which can effectively balance the path length and planning time.

Improved rapidly exploring random tree using salp swarm algorithm

Abstract Due to the limitations of the initial rapidly exploring random tree (RRT) algorithm, robotics faces challenges in path planning. This study proposes the integration of the metaheuristic salp swarm algorithm (SSA) to enhance the RRT algorithm, resulting in a new algorithm termed IRRT-SSA. The IRRT-SSA addresses issues inherent in the original RRT, enhancing efficiency and path-finding capabilities. A detailed explanation of IRRT-SSA is provided, emphasizing its distinctions from the core RRT. Comprehensive insights into parameterization and algorithmic processes contribute to a thorough understanding of its implementation. Comparative analysis demonstrates the superior performance of IRRT-SSA over the basic RRT, showing improvements of approximately 49, 54, and 54% in average path length, number of nodes, and number of iterations, respectively. This signifies the enhanced effectiveness of the proposed method. Theoretical and practical implications of IRRT-SSA are highlighted, particularly its influence on practical robotic applications, serving as an exemplar of tangible benefits.

Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework

The rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between port equipment and path optimization under environmental perception, while there is relatively little research on optimization of path smoothness and safety. Therefore, we propose a path optimization model based on the artificial potential field and twin delayed deep deterministic policy gradient (APF-TD3) framework for the port environment. Firstly, we obtain the scheduling task plan of a single AGV by enumeration. Secondly, according to the artificial potential field (APF) algorithm to generate repulsion for obstacles in the harbor and attraction for container storage at the target point with the position information of the AGV as the input data of the reinforcement learning algorithm is inputted into the twin delayed deep deterministic policy gradient algorithm (TD3). Then TD3 selects the optimal action strategy for the AGV according to the input AGV state information and the designed reward mechanism as well as executes the action. Through repeated execution, the optimal action for the next step is selected at each point to generate a path with start and end points. We validate the model by simulating the scale of containerized cargo in the port i.e. small scale, medium scale and large scale scenes. The experimental results show that the method has the shortest path length of 27.519 m, 270.847 m, and 496.389 m compared to artificial potential field and deep deterministic policy gradient (APF-DDPG), APF, and rapidly-exploring random tree (RRT) algorithms, which also have significant advantages in terms of path security and path smoothness. This framework could respond to the scheduling and transportation tasks of single AGV in different environmental layouts and guarantee the smoothness and safety of the path based on the optimization of the path, which promotes the efficient operation and management of ports.

Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning

Abstract With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random Tree (RRT) but with the drawback of not guaranteeing the optimal path. This paper solves this problem by proposing a Memorized RRT Optimization Algorithm (MRRTO Algorithm) using memory as an optimization step. The algorithm obtains a single path from the start point, and another from the target point to store only the last visited new node. The method for computing the nearest node depends on the position, when a new node is added, the RRT function checks if there is another node closer to the new node rather than that is closer to the goal point. Simulation results with different environments show that the MRRTO outperforms the original RRT Algorithm, graph algorithms, and metaheuristic algorithms in terms of reducing time consumption, path length, and number of nodes used.

Improved RRT* Algorithm for Disinfecting Robot Path Planning.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Research on dual robot collaboration method based on improved double ant colony algorithm

Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

HMS-RRT: A novel hybrid multi-strategy rapidly-exploring random tree algorithm for multi-robot collaborative exploration in unknown environments

In this paper, we proposed a novel multi-robot collaborative exploration method to improve the efficiency and robustness of multi-robot exploration in unknown environments. Firstly, a novel frontier detection algorithm based on hybrid multi-strategy rapidly-exploring random tree (HMS-RRT) is proposed, which is composed of an adaptive incremental distance strategy, a subregion sampling strategy and a greedy frontier-based exploration strategy. To improve the frontier detection performance of the algorithm, we adopt the Voronoi diagram to continuously partition the explored region, and dynamically adjust the incremental distance according to the density of obstacles in the subregions. To avoid the algorithm is trapped in the local optimum, we use Gaussian distribution to calculate the sampling probability in each subregion, so that the algorithm tends to sample in the subregion with lower crowded level of nodes and cover the unexplored regions quickly. Secondly, we introduce the greedy frontier-based exploration strategy to explore all Voronoi polygons in turn and refine the search results, meanwhile, the centroid of each frontier region is extracted as the exploration target point. Then, a multi-robot task assignment strategy based on improved market mechanism is introduced to dynamically assign the exploration target points to each robot, and the map-merging algorithm is used in the exploration process to merge several local maps in real-time. Finally, an experimental testing platform is developed based on Robot Operating System (ROS) and a series of experiments are carried out. The results show that our method can improve the efficiency and reliability of multi-robot exploration in both the simulations and the prototype experiments.

Research on kinematics modeling and path planning of a hyper-redundant continuum robot

It has always been the case that work in confined spaces has high difficulty and complexity, and robots are usually required to replace humans to perform the corresponding operations. For the maintenance work in the fuel tank of a certain type of aircraft, a continuum robot with 32 staggered orthogonal joints is designed in this paper. To solve the motion problem of this robot, this paper combines the end-following method with the Jacobi inverse kinematics method, and proposes an end-following-segmented Jacobi method, which improves the accuracy and efficiency of the inverse kinematics solution. A ray path-planning algorithm for continuum robots is proposed for the job requirements of this robot and the specific operating environment inside an airplane fuel tank, and it is compared with the rapidly-exploring random trees (RRT) algorithm and particle swarm optimization (PSO) algorithm. The experimental results show that the planning time of the ray path-planning algorithm is less compared with the RRT algorithm and the PSO algorithm, and the paths obtained are shorter. Finally, a path-following simulation is performed on a 32-joint continuum robot model to verify the followability of the ray path algorithm.

Intelligent path planning by an improved RRT algorithm with dual grid map

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

Research of the Possibilities of Using a Rapidly Exploring Random Tree Algorithm in Solving Problems of Movement Planning of Autonomous Robots

The article is devoted to the analysis of features and evaluation of the prospects for using the RRT method in the problem of motion planning for autonomous robots. It is noted that the expansion of the areas of application of modern robotics is inextricably linked with an increase in the level of functionality and improvement of the designs of the created samples, for which the placement of a manipulator on a mobile platform becomes a typical layout. Based on a review of the literature and generalization of experimental data, it is shown that the use of the RRT method and its modifications opens up the fundamental possibility of developing a universal motion planner for mobile and manipulative robots, including systems with a manipulator on a mobile platform, as well as systems with a redundant or reconfigurable structure. Based on the results of the analysis, it was found that the effectiveness of the RRT method is largely determined by the declared parameter of the growth factor. A decentralized variant of the counter-growth RRT method is proposed, which makes it possible to plan the movements of autonomous mobile robots in the process of their mutual approach and subsequent docking. The fundamental possibility of automatic docking of autonomous robots in an environment with obstacles is confirmed by the results of a full-scale experiment.

Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

AbstractDual-robot system has been widely applied to the field of handling and palletizing for its high efficiency and large workspace. It is one of the key problems of the trajectory planning to determine the collision avoidance method of the dual-robot system. In the present study, a collision avoidance trajectory planning method for the dual-robot system was proposed on the basis of a modified artificial potential field (APF) algorithm. The interference and collision criterion of the dual-robot system was given firstly, which was established based on the method of kinematic analysis in robotics. And then, in consideration of the problem of excessive virtual potential field force induced by using the traditional APF algorithm in the process of dual-robot trajectory planning, a modified APF algorithm was proposed. Finally, the modified APF algorithm was used for motion control of a dual-robot palletizing process, and the collision avoidance performance of the proposed collision avoidance algorithm was studied through a dual-robot palletizing simulation and experiment. The results have shown that with the proposed collision avoidance trajectory planning algorithm, the two robots in dual-robot system can maintain a safe distance at all times during palletizing process. Compared with the traditional APF and rapidly-exploring random tree (RRT) algorithm, the trajectory solution time of the modified APF algorithm is greatly reduced. And the modified APF algorithm’s convergence time is 14.2% shorter than that of the traditional APF algorithm.

Modeling UAV swarm flight trajectories using Rapidly-exploring Random Tree algorithm

The paper presents the concept of determining the flight trajectory of an unmanned aerial vehicle (UAV) swarm that forms a flying ad-hoc network (FANET) for communication with each other. The FANET allows the swarm to follow a predetermined route between the starting and ending areas performing the reconnaissance task of the most significant area during the flight. The primary limitations of the FANET, which is used by the UAV swarm, are discussed. The radio link range between FANET nodes is based on a power budget and various propagation models. Whereas, for determining the UAV flight trajectories, we propose to use a heuristic algorithm based on the modified Rapidly-exploring Random Tree (RRT). This modification of the base RRT algorithm allows for planning the flight of several UAVs with meeting indicated restrictions, e.g., the minimum distance between individual UAVs ensuring communication between them at any time of the flight. Simulation tests are carried out for a swarm composed of several UAVs in environments with a random number of obstacles, corresponding to a real scenario for search and rescue operations.

A Novel Exploration Mechanism of RRT Guided by Deep Q-Network

A novel exploration mechanism for addressing the issues of slow convergence speed, excessive redundancy and low path quality associated with the inherent randomness of Rapidly-exploring Random Tree (RRT)’s sampling approach is presented. First, the node exploration process of the RRT algorithm is modeled as a Markov Decision Process (MDP) by designing the action space and reward function. Subsequently, a novel node exploration mechanism based on RRT-Connect is developed by integrating environmental feedback information. Finally, the combination of Deep Q-Network (DQN) and RRT is achieved through the proposed DQN-RRT algorithm, which incorporates the structure and training method of DQN. Compared to the traditional RRT algorithm, the proposed algorithm balances planning autonomy, reduces search redundancy and efficient obstacle avoidance. Simulations are given to validate the performance optimization function of the proposed algorithm in RRT path planning.

Real-time constraint-based planning and control of robotic manipulators for safe human–robot collaboration

A recent trend in industrial robotics is to have robotic manipulators working side-by-side with human operators. A challenging aspect of this coexistence is that the robot is required to reliably solve complex path-planning problems in a dynamically changing environment. To ensure the safety of the human operator while simultaneously achieving efficient task realization, this paper introduces a computationally efficient planning and control architecture that combines a Rapidly-exploring Random Tree (RRT) path planner with a trajectory-based Explicit Reference Governor (ERG) by means of a reference selector. The resulting scheme can steer the robot arm to the desired end-effector pose in the presence of actuator saturation, limited joint ranges, speed limits, a cluttered static obstacle environment, and moving human collaborators. The effectiveness of the proposed framework is experimentally validated on the Franka Emika Panda robot arm and fed with feedback information from state-of-the-art depth perception systems. Our method outperforms both the standalone RRT and ERG algorithms in cluttered static environments where it overcomes: i) the RRT’s inability to handle dynamic constraints which result in constraint violations and ii) the ERG’s undesirable property of getting trapped in local minima. Finally we employed the RRT+ERG in highly dynamic human–robot coexistence experiments without sacrificing the real-time requirements.

Method for planning the way of UGV using a modification of dynamic bi-directional RRT algorithm.

The path planning problem of unmanned autonomous ground vehicles has always been an acute problem in the field of autonomous ground robotic systems research. From the point of view of the complexity of the tasks during the conduct of combat operations in the urbanized space of the densely built-up city with a constantly changing landscape, the tasks entrusted to the UGV are constantly becoming more difficult, and the scenarios of the use of the UGV demonstrate a diversified trend in the development of new management and control systems. The rapidity of conducting modern military operations in an urbanized space appears as a complex and multifaceted task. Considering the complexity of the UGV movement process, at the current stage of the development of robotic systems, the general trend is to abandon remote control of robotic complexes with the transition to automatic modes, which requires the development and implementation of algorithms for automatic interaction and movement of military mobile robotic systems. To solve the problems of the Rapidly-Exploring Random Tree *Fixed Nodes algorithm regarding its low speed for obtaining track junctions and the impossibility of using it in a dynamic environment when planning a UGV path. To solve the problem of accelerating the acquisition of collision-free paths in real time in two-dimensional space, it is proposed to apply a modified dynamic bidirectional RRT* algorithm with reference nodes. The algorithm is a modification of Rapidly-Exploring Random Tree *Fixed Nodes using a bidirectional greedy search method to speed up and solve the problem of the unidirectional Rapidly-Exploring Random Tree algorithm regarding its slow search speed, as well as the difficulties of decision making in a narrow environment caused by blind random sampling. In the case of dynamic disturbance movement, taking advantage that reference nodes do not require much calculation in planning, in the process of iterative path optimization, the proposed algorithm updates the map information in real time and repairs the damaged output path to complete the dynamic path planning.

Path planning for storage robots using the RRT algorithm

This paper delves into the issue of storage robot path planning, a critical challenge in the field of warehouse automation. To address this challenge, this paper introduces the Rapidly-exploring Random Tree (RRT) algorithm and presents an innovative path planning approach aimed at efficiently handling dynamic environments and multiple constraints.Firstly, this paper extensively explores the complexity of the storage robot path planning problem, emphasizing its crucial nature in practical applications. Next, it provides an in-depth explanation of the principles of the RRT algorithm, particularly highlighting its unique advantages in path planning, especially in high-dimensional spaces and nonlinear constraints. In the implementation section, a detailed description is provided on how to apply the RRT algorithm to storage robot path planning, including the workflow of the algorithm and its key steps.In the discussion section, this paper thoroughly analyzes the potential application value of the RRT algorithm in storage robot path planning and discusses future improvement directions and research areas. Finally, by summarizing the outcomes of this study, the paper emphasizes the pivotal role of the RRT algorithm in storage robot path planning and the positive impact it brings to solving this significant problem.