Efficient Path-planning Algorithm Research Articles

Simple Application of A* Algorithm on Robot path-planning for the Shortest Path

When the robot encounters circumstances where it has to go from its current position to a given goal in an undetermined environment, it has to deal with the path-planning problem. The A* algorithm, as a classic algorithm for efficient path searching in robot engineering, is an interesting and precious topic to study. As part of a project to design a cargo-delivering robot which will operate in a multi-purpose stadium, considering the complex structure of the building, an efficient path-planning algorithm must be implemented to help the robot fulfil its delivery. The heuristic function and its parameters, which are important for the speed of making decisions, should be determined under tests in different situations. By using MATLAB, the detailed process of the A* algorithm using different heuristic functions and parameters can be clearly shown in a 2-D grid map simulating the scene, including searched areas and the final path. With the help of the images and the output path length, the heuristic function and the parameters are finally decided. The method is now good for the designed robot to operate in simulations with static obstacles, but it can be improved to deal with automatic obstacle avoidance in real-life cases, where people, balls, etc. are actually moving.

Development of metaheuristic algorithms for efficient path planning of autonomous mobile robots in indoor environments

The application of efficient path planning algorithms for two-wheeled Autonomous Mobile Robots (AMRs) in static environments with obstacles is a significant challenge in robotics research. Existing methods, such as the A star (A*) algorithm utilized in Robot Operating System 2 (ROS2), can provide optimal paths but may have high computational complexity in intricate environments. This study explores the potential of three metaheuristic algorithms - Improved Particle Swarm Optimization (IPSO), Improved Grey Wolf Optimizer (IGWO), and Artificial Bee Colony (ABC) - for planning efficient and smooth paths in static environments. These algorithms are selected due to their ability to efficiently find near-optimal solutions and avoid local minima. In this study, the researchers designed and built a two-wheeled AMR using a Raspberry Pi 4 microcontroller as the main processing unit, working in conjunction with an Arduino Mega for controlling the DC motor drive through an MDD10A motor driver circuit. The robot is equipped with an RPLiDAR A1 sensor to read 360-degree distance values for mapping and obstacle avoidance. The experimental results clearly indicate that the metaheuristic algorithms, especially ABC, can calculate paths up to 7% shorter than A* while requiring only one-tenth of the time. Moreover, ABC demonstrates superior motion smoothness when applied to the actual two-wheeled robot in static environments. This work represents a significant step in developing algorithms for two-wheeled robots that are ready to support real-world operations in industries, logistics, healthcare, or various service sectors, which can help increase efficiency and reduce operating costs in the future.

Chaotic Path-Planning Algorithm Based on Courbage–Nekorkin Artificial Neuron Model

Developing efficient path-planning algorithms is an essential topic in modern robotics and control theory. Autonomous rovers and wheeled and tracked robots require path generators that can efficiently cover the explorable space with minimal redundancy. In this paper, we present a new path-planning algorithm based on the chaotic behavior of the Courbage–Nekorkin neuron model with a coverage control parameter. Our study aims to reduce the number of iterations required to cover the chosen investigated area, which is a typical efficiency criterion for this class of algorithms. To achieve this goal, we implemented a pseudorandom bit generator (PRBG) based on a Courbage–Nekorkin chaotic map, which demonstrates chaotic behavior and successfully passes all statistical tests for randomness. The proposed PRBG generates a bit sequence that can be used to move the tracked robot in four or eight directions in an operation area of arbitrary size. Several statistical metrics were applied to evaluate the algorithm’s performance, including the percentage of coverage of the study area and the uniformity of coverage. The performance of several competing path-planning algorithms was analyzed using the chosen metrics when exploring two test areas of the sizes 50 × 50 cells and 100 × 100 cells, respectively, in four and eight directions. The experimental results indicate that the proposed algorithm is superior compared to known chaotic path-planning methods, providing more rapid and uniform coverage with the possibility of controlling the covered area using tunable parameters. In addition, this study revealed the high dependence of the coverage rate on the starting point. To investigate how the coverage rate depends on the choice of chaotic map, we implemented six different PRBGs using various chaotic maps. The obtained results can be efficiently used for solving path-planning tasks in both real-life and virtual (e.g., video games) applications.

Path planning for mobile robots in unstructured orchard environments: An improved kinematically constrained bi-directional RRT approach

In complex orchard environments, efficient path-planning algorithms for mobile robots are crucial for achieving accurate autonomous navigation and mobile operations. The existence of kinematic constraints for mobile robots in orchards and the unstructured layouts of orchard environments, which primarily feature irregular obstacles, pose considerable challenges for path-planning algorithms. Ideal path-planning algorithms must overcome the above problems and plan navigation paths that are safe, efficient, and consistent with the kinematic constraints of mobile robots in an obstacle-filled environment. In this study, to satisfy the above requirements, the Improved Kinematically Constrained Bi-directional Rapidly Exploring Random Tree (IKB-RRT) algorithm is proposed, which includes three innovations. First, an improved artificial potential field method is designed for guidance-node generation in complex orchard environments; hence, the nodes provide the path guidance for the random expansion trees that are subject to the kinematic constraints, avoiding obstacle regions and expanding towards the target point. Second, a motion-control parameter sampling method for the robot configuration is introduced to control the random expansion tree to generate path nodes and trajectories that satisfy the mobile robot constraints. Third, a path optimization strategy for dual-tree connected regions is proposed to avoid sudden changes in the heading angle at dual-tree connected waypoints and to improve the overall path smoothness. Path planning is performed in four orchard environments with different characteristics. The algorithm test results show that, in different scenarios, the proposed IKB-RRT algorithm reduces the path length by a maximum of 13.94% and 12.20% and the planning time by a maximum of 61.41% and 68.84%, significantly improving the overall smoothness of the path relative to the KB-RRT* and KB-RRT algorithms, respectively. The results fully verify that the IKB-RRT algorithm has obvious advantages in handling the path-planning problem that arises for mobile robots in complex orchard environments. The proposed algorithm can be implemented in future orchard mobile robots to enable effective path planning.

A constrained locking sweeping method and velocity obstacle based path planning algorithm for unmanned surface vehicles in complex maritime traffic scenarios

Unmanned surface vehicles (USVs) have wide applicability owing to their high velocities, intelligence, and unmanned operational characteristics, and therefore, they have garnered considerable research interest. To accommodate complex maritime environments comprising static/dynamic obstacles, a novel efficient path-planning algorithm called the constrained locking sweeping method and velocity obstacle (CLSM-VO) is developed. This algorithm uses the locking sweeping method (LSM) to provide the initial yaw angle for the velocity obstacle (VO) algorithm. While retaining the performance of the VO algorithm, the initial yaw angle based on the LSM can help the VO algorithm optimise the global search performance. Compared with conventional path-planning algorithms, the proposed algorithm demonstrates higher computational efficiency and is applicable to complex dynamic environments with multiple moving obstacles. Additionally, a constraint layer is designed, by combining the proposed algorithm with the yaw-velocity performance of the Tianxing-1 USV, such that the generated path exhibits better smoothness. The adjustable avoidance distance calculated by the proposed algorithm can help prevent collision accidents compared to that by existing algorithms. To evaluate the algorithm performance, particularly the capacity to handle complex dynamic environments, multiple simulations are performed in environments built using electronic nautical charts. The results indicate that the proposed algorithm can effectively handle complex maritime traffic scenarios by generating smooth and safe trajectories.

An Efficient Path Planning Algorithm Using a Potential Field for Ground Forces

With the development and proliferation of unmanned weapons systems, path planning is becoming increasingly important. Existing path-planning algorithms mainly assume a well-known environment, and thus pre-planning is desirable, but the actual ground battlefield is uncertain, and numerous contingencies occur. In this study, we present a novel, efficient path-planning algorithm based on a potential field that quickly changes the path in a constantly changing environment. The potential field is composed of a set of functions representing enemy threats and a penalty term representing distance to the target area. We also introduce a new threat function using a multivariate skew-normal distribution that accurately expresses the enemy threat in ground combat.

Efficient Path Planning for a Microrobot Passing through Environments with Narrow Passages.

This paper presents an efficient path-planning algorithm for microrobots attempting to pass through environments with narrow passages. Because of the extremely small size of a microrobot, it is suitable for work in this kind of environment. The rapidly exploring random tree (RRT) algorithm, which uses random sampling points, can quickly explore an entire environment and generate a sub-optimal path for a robot to pass through it; however, the RRT algorithm, when used to plan a path for a microrobot passing through an environment with narrow passages, has the problem of being easily limited to local solutions when it confronts with a narrow passage and is unable to find the final path through it. In light of this, the objectives of the considered path planning problem involve detecting the narrow passages, leading the path toward an approaching narrow passage, passing through a narrow passage, and extending the path search more efficiently. A methodology was proposed based on the bidirectional RRT in which image processing is used to mark narrow passages and their entrances and exits so that the bidirectional RRT can be quickly guided to them and combined with the deterministic algorithm to find paths through them. We designed the methodology such that RRT generates the sampling points for path growth. The multiple importance sampling technique is incorporated with bidirectional RRT, named MIS-BiRRT, to make the path grow faster toward the target point and narrow passages while avoiding obstacles. The proposed algorithm also considers multiple candidate paths simultaneously to expand the search range and then retain the best one as a part of the planning path. After validation from simulation, the proposed algorithm was found to generate efficient path planning results for microrobots to pass through narrow passages.

Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA.

With their intelligence, flexibility, and other characteristics, automated guided vehicles (AGVs) have been popularized and promoted in traditional industrial markets and service industry markets. Compared with traditional transportation methods, AGVs can effectively reduce costs and improve the efficiency of problem solving in various application developments, but they also lead to serious path-planning problems. Especially in large-scale and complex map environments, it is difficult for a single algorithm to plan high-quality moving paths for AGVs, and the algorithm solution efficiency is constrained. This paper focuses on the indoor AGV path-planning problem in large-scale, complex environments and proposes an efficient path-planning algorithm (IACO-DWA) that incorporates the ant colony algorithm (ACO) and dynamic window approach (DWA) to achieve multiobjective path optimization. First, inspired by the biological population level, an improved ant colony algorithm (IACO) is proposed to plan a global path for AGVs that satisfies a shorter path and fewer turns. Then, local optimization is performed between adjacent key nodes by improving and extending the evaluation function of the traditional dynamic window method (IDWA), which further improves path security and smoothness. The results of simulation experiments with two maps of different scales show that the fusion algorithm shortens the path length by 9.9 and 14.1% and reduces the number of turns by 60.0 and 54.8%, respectively, based on ensuring the smoothness and safety of the global path. The advantages of this algorithm are verified. QBot2e is selected as the experimental platform to verify the practicability of the proposed algorithm in indoor AGV path planning.

An Improved Q-RRT* Algorithm Based on Virtual Light

The Rapidly-exploring Random Tree (RRT) algorithm is an efficient path-planning algorithm based on random sampling. The RRT* algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solution. However, it has been proven to take an infinite time to do so. An improved Quick-RRT* (Q-RRT*) algorithm based on a virtual light source is proposed in this paper to overcome this problem. The virtual light-based Q-RRT* (LQ-RRT*) takes advantage of the heuristic information generated by the virtual light on the map. In this way, the tree can find the initial solution quickly. Next, the LQ-RRT* algorithm combines the heuristic information with the optimization capability of the Q-RRT* algorithm to find the approximate optimal solution. LQ-RRT* further optimizes the sampling space compared with the Q-RRT* algorithm and improves the sampling efficiency. The efficiency of the algorithm is verified by comparison experiments in different simulation environments. The results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.

Real-time path planning module for autonomous vehicles in cluttered environment using a 3D camera

This paper is concerned with the real-time path planning of AGVs in a cluttered environment. In order to perform real-time operations with limited processing resources, an efficient path-planning algorithm and identification of the obstacles by a single sensor are presented. For an AGV, path planning in a cluttered environment is a challenging task owing to its lack of information about the surroundings and its need to re-plan its path quickly whenever it senses obstacles nearby. Therefore, an efficient path-planning algorithm that offers an AGV sufficient time to re-plan its path to avoid moving obstacles is proposed and, to measure its computational efficacy, its time complexity is considered. In real-time experimentation of autonomous path-planning, AGV relies completely on perception system to sense the immediate environment and avoid obstacles when it traverses towards the goal. As the Time-of-Flight (ToF)-based PMD (Photonic Mixer Device) three dimensional (3D) sensor can provide range and intensity data at low computational cost, it is utilised as a single proprioceptive sensor to detect static and dynamic obstacles.

Constrained Global Path Optimization for Articulated Steering Vehicles

This paper proposes a new efficient path-planning algorithm for articulated steering vehicles operating in semi-structured environments, in which obstacles are detected online by the vehicle's sensors. The first step of the algorithm is offline and computes a finite set of feasible motions that connect discrete robot states to construct a search space. The motion primitives are parameterized using Bézier curves and optimized as a nonlinear programming problem (NLP) equivalent to the constrained path planning problem. Applying the A* search algorithm to the search space produces the shortest paths as a sequence of these primitives. The sequence is drivable and suboptimal, but it can cause unnatural swerves. Therefore, online path smoothing, which uses a gradient-based method, is applied to solve another NLP. Numerical simulations demonstrate that performance of the proposed algorithm is significantly better than that of existing methods when determining constrained path optimization. Moreover, field experimental results demonstrate the successful generation of fast and safe trajectories for real-time autonomous driving.

Real-Time Path Planning Based on Hybrid-VANET-Enhanced Transportation System

Real-time path planning can efficiently relieve traffic congestion in urban scenarios. However, how to design an efficient path-planning algorithm to achieve a globally optimal vehicle-traffic control still remains a challenging problem, particularly when we take drivers' individual preferences into consideration. In this paper, we first establish a hybrid intelligent transportation system (ITS), i.e., a hybrid-VANET-enhanced ITS, which utilizes both vehicular ad hoc networks (VANETs) and cellular systems of the public transportation system to enable real-time communications among vehicles, roadside units (RSUs), and a vehicle-traffic server in an efficient way. Then, we propose a real-time path-planning algorithm, which not only improves the overall spatial utilization of a road network but reduces average vehicle travel cost for avoiding vehicles from getting stuck in congestion as well. A stochastic Lyapunov optimization technique is exploited to address the globally optimal path-planning problem. Finally, the transmission delay of the hybrid-VANET-enhanced ITS is evaluated in VISSIM to show the timeliness of the proposed communication framework. Moreover, system-level simulations conducted in Java demonstrate that the proposed path-planning algorithm outperforms the traditional distributed path planning in terms of balancing the spatial utilization and drivers' travel cost.

HGA*, an efficient algorithm for path planning in a plane

Existing and prospective approaches and methods are considered for path planning in a plane. The existing planning algorithms are analyzed and qualitatively assessed. A new efficient path-planning algorithm, HGA*, is described, which enables searching for a plan under conditions of limited time and computational resources and under additional constraints (such as the dynamic changes and partial observability of the planning terrain). The results are presented, which show the experimentally proven advantages of HGA* over the available analogues.

A Structure‐Controlled Investigation of Lipase Enantioselectivity by a Path‐Planning Approach

A novel approach based on efficient path-planning algorithms was applied to investigate the influence of substrate access on Burkholderia cepacia lipase enantioselectivity. The system studied was the transesterification of 2-substituted racemic acid derivatives catalysed by B. cepacia lipase. In silico data provided by this approach showed a fair qualitative agreement with experimental results, and hence the potential of this computational method for fast screening of racemates. In addition, a collision detector algorithm used during the pathway searches enabled the rapid identification of amino acid residues hindering the displacement of substrates along the deep, narrow active-site pocket of B. cepacia lipase and thus provided valuable information to guide the molecular engineering of lipase enantioselectivity.

Path planning for autonomous UAV via vibrational genetic algorithm

PurposeIt is aimed to provide an efficient algorithm for path planning in guidance of autonomous unmanned aerial vehicle (UAV) through 3D terrain environments.Design/methodology/approachAs a stochastic search method, vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for path planning.FindingsUsing VGA, an efficient path planning algorithm for autonomous UAV was obtained under low population rate and short generation cycle conditions.Originality/valueVGA decreased the required time for optimal path solution beside its simplicity. Low population rate and short generation cycle are the main benefits of VGA.

Path Planning for Autonomous Underwater Vehicles

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Efficient path-planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path-planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching (FM)-based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a 2-D continuous path from a discrete representation of the environment. Second, we take underwater currents into account thanks to an anisotropic extension of the original FM algorithm. Third, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic media. Finally, a multiresolution method is introduced to speed up the overall path-planning process. </para>

Construction Robot Path-Planning for Earthwork Operations

In developing an intelligent mobile construction robot, a navigation system that can provide an effective and efficient path-planning algorithm is a very important element. The purpose of a path-planning method for a mobile construction robot is to find a continuous collision-free path from the initial position of the construction robot to its target position. This paper presents an improved Bug-based algorithm, called SensBug, which can produce an effective path in an unknown environment with both stationary and movable obstacles. The contributions, which make it possible to generate an effective and short path, are an improved method to select local directions, a reverse mode, and a simple leaving condition. Some emerging technologies that can be used for implementing an intelligent construction robot are introduced in this paper. DOI: 10.1061/~ASCE!0887-3801~2003!17:2~97! CE Database subject headings: Construction planning; Automation; Earthwork; Robotics.

Collision-free motion planning of multiarm robots using evolutionary algorithms

This paper presents an evolutionary algorithm for the collision-free path planning of multiarm robots. A global path planning technique is used where the paths are represented by a string of via-points that the robots have to pass through, connected together by cubic spline polynomials. Since the entire paths of the robots are considered for optimization, the problem of deadlock between the arms and the static obstacles does not occur. Repeated path modification is done through evolutionary techniques to find an optimized path with respect to length and collision among the robots and the obstacles and the robots themselves. The proposed algorithm departs from simple genetic algorithms in that floating point vector strings represent the chromosomes and customized operators are used to improve upon the performance of the search. Moreover, a local search is carried out on each individual in addition to the global population based search. The result is a highly efficient path-planning algorithm that can deal with complex problems easily. Simulation results are presented for collision-free paths planned for two planar arms and then for two 3 degree-of-freedom (DOF) PUMA®-like arms moving in three-dimensional operational space.

An Efficient Path-Planning Algorithm for a Robotic Manipulator by Automatic Selection Search of Indispensable Regions in Its Configuration Space

In this paper, we propose a feasible motion planning algorithm for a robotic manipulator and its obstacles. The algorithm quickly selects a feasible sequence of collision-free motions while adaptively expanding a graph in the implicit configuration joint-space. In the configuration graph, each arc represents an angle difference of the manipulator joint; therefore, an arc sequence represents a continuous sequence of robot motions. Thus, the algorithm can execute a continuous sequence of collision-free motions. Furthermore, the algorithm expands the configuration graph only in space which is to be cluttered in the implicit configuration joint-space and which is needed to select a collision-free sequence between the initial and target positions/orientations. The algorithm maintains the configuration graph in a small size and quickly selects a collision-free sequence from the configuration graph, whose shape is to be simple enough to move the manipulator in practical applications.