Robotic Planning Research Articles

Research on Autonomous Mobile Robot Path Planning Based on M-RRT Algorithm

The Rapidly-Exploring Random Tree (RRT) algorithm has demonstrated proficiency in adapting to path search challenges within high-dimensional dynamic environments. However, a notable limitation of the RRT algorithm lies in its inability to fulfill the criteria for achieving the shortest and smoothest path for mobile sensing nodes. To address the limitations of the conventional RRT algorithm and enhance the path planning for mobile robots, this paper proposed an innovative approach named M-RRT, designed to overcome the aforementioned shortcomings and optimize the path planning process for mobile sensing nodes. First, the search area is constructed according to the defined coverage density. After searching the path in the search area, the RRT algorithm uses the greedy method to delete the intermediate nodes in the path, and obtains the uniquly optimal path. Finally, the Bezier curve is used to optimize the path, which makes the path shortest and meets the dynamic requirements of the mobile node. Simulation results show that M-RRT has better path and faster convergence speed than traditional RRT, which can better meet the planning requirements of mobile nodes.

Cooperative path planning study of distributed multi-mobile robots based on optimised ACO algorithm

The rapid development of robotics technology has driven the growth of robot types and the development of related technologies. As an important aspect of robot research, path planning technology plays an irreplaceable role in practical production and application. Ant colony algorithm has a wide range of applications in robot path planning, but there is also a problem of performance overly relying on initial parameter selection. In order to solve this problem and improve the performance of mobile robot path planning, an improved ant colony algorithm based on firefly algorithm was studied and designed in a two-dimensional environment. In order to further explore the performance of ant colony algorithm in solving robot coordinated path planning problems, an improved ant colony algorithm based on heuristic function was also designed. In a three-dimensional environment, an improved ant colony algorithm based on the improved artificial potential field method was designed. The research results show that the maximum running time of the improved ant colony algorithm based on the firefly algorithm in different grid environments is 819.36 s, 847.01 s, and 811.54 s, respectively. The average running time of the improved ant colony algorithm based on heuristic function in different grid environments is 5.19 s, 5.97 s, and 9.09 s, with average path lengths of 29.90 cm, 31.08 cm, and 37.01 cm, and path length variances of 0.35, 0.87, and 2.21, respectively. The ant colony algorithm based on the improved artificial potential field method has a running time of 1.930 s, 3.182 s, and 4.662 s in different grid environments, and a path length of 29.275 cm, 49.447 cm, and 67.057 cm, respectively. The ant colony algorithm for research and design optimization has good performance. The contribution of the research lies in the design of three path planning methods for mobile robots, including two-dimensional path planning and three-dimensional path planning, which improves the time of path planning and shortens the average path length. The novelty of the research is reflected in the design of a path planning method for mobile robots in two-dimensional and three-dimensional environments, which improves the ant colony algorithm through firefly algorithm and heuristic function, and combines the ant colony algorithm with the improved artificial potential field method. The method designed by the research institute can provide technical support for path planning of mobile robots.

Interference aware path planning for mobile robots under joint communication and sensing in mmWave networks

The beyond fifth-generation (B5G) and emerging sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). In this paper, we consider mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations, whilst utilizing high directivity beamformers in millimeter wave (mmWave) small cells for joint communication and sensing. In such scenarios, the potential close proximity of MRs connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate and network service degradation. To mitigate this effect an interference aware path planning algorithm is proposed by explicitly taking into account both achievable communication and sensing performance. More specifically, the proposed heuristic scheme aims to find paths with minimal interfering time whilst improving the overall joint communication and sensing quality of service. A wide set of numerical investigations reveal that the proposed heuristic path planning scheme for the mmWave networked mobile robots can improve the overall achievable communication throughput and the sensing mutual information by up to 35.6% and 23.6% respectively compared with an interference oblivious scheme. Most importantly, those gains are attained without penalizing noticeably the total travel time of the MRs.

Obtaining the optimal shortest path between two points on a quasi-developable Bézier-type surface using the Geodesic-based Q-learning algorithm

One of the most important problems is the computation of the shortest path between two points, because the search for the shortest path is very important in various fields, such as path planning of autonomous mobile robots. With this goal in mind, this study presents a novel method for determining the most efficient route connecting two locations on a surface. This approach relies on the geodesic properties of the surface points, the surface’s ability to be unfolded without distortion, and the utilization of the Q-learning algorithm.The first part of the proposed method constructs a quasi-developable Bézier-type surface from two boundary cubic trigonometric Bézier-type curves using the computed optimal four shape parameters. The subsequent stage of the introduced technique identifies the best and most direct route between two designated points on the derived quasi-developable Bézier-style surface. This is achieved through the utilization of the Geodesic-based Improved Epsilon GreedyQ-Learning (IEGQL) algorithm.Additionally, we have used the proposed algorithm in a mobile application developed for the elderly. This application involves offering a mobile game designed to ascertain the shortest path, with the goal of enhancing cognitive engagement among the elderly population as a preventive measure against Alzheimer’s disease. Moreover, the proposed method is compared with Dijkstra’s algorithm and A-Star algorithm, which are the best known algorithms for the shortest path problem.

Path Planning of Autonomous Mobile Robot in Comprehensive Unknown Environment Using Deep Reinforcement Learning

Path Planning of Autonomous Mobile Robot in Comprehensive Unknown Environment Using Deep Reinforcement Learning

Research on mobile robot path planning in complex environment based on DRQN algorithm

A deep reinforcement Q learning algorithm (DRQN) based on radial neural network is proposed to achieve path planning and obstacle avoidance for mobile robots in complex ground environments with different types of obstacles, including static and dynamic obstacles. Firstly, the path planning problem is represented as a partially-observed Markov decision process. Steering angle, running characteristics, and other elements are introduced into the state-action decision space and the greedy factor is dynamically adjusted using a simulated annealing algorithm, which improves the mobile robot’s environment exploration and action selection accuracy. Secondly, the Q-learning algorithm is improved by replacing the Q-table structure with an RBF neural network to enhance the approximation ability of the algorithm’s function values, and the parameters of the implicit layer and the weights between the implicit and the output layer are trained using the dynamic clustering and least-mean methods respectively, which improves the convergence speed and enhances the ability of mobile robots to handle large-scale computation. Lastly, the double reward mechanism is set up to prevent the mobile robot from blind searching in unknown environments, which enhances the learning ability and improves path planning safety and flexibility at the same time. Different types of scenarios are set up for simulation experiments, and the results verified the superiority of the DQRN algorithm. Taking the 30 * 30 complex scene as an example, using the DQRN algorithm for path planning reduces the values of distance, turning angle, and planning time by 27.04%, 7.76%, and 28.05%, respectively, compared to the average values of Q-learning, optimized Q-learning, deep Q-learning, and DDPG algorithms, which can effectively improve the path planning efficiency for mobile robots in complex environments.

Quadruped Robot Control: An Approach Using Body Planar Motion Control, Legs Impedance Control and Bézier Curves.

In robotics, the ability of quadruped robots to perform tasks in industrial, mining, and disaster environments has already been demonstrated. To ensure the safe execution of tasks by the robot, meticulous planning of its foot placements and precise leg control are crucial. Traditional motion planning and control methods for quadruped robots often rely on complex models of both the robot itself and its surrounding environment. Establishing these models can be challenging due to their nonlinear nature, often entailing significant computational resources. However, a more simplified approach exists that focuses on the kinematic model of the robot's floating base for motion planning. This streamlined method is easier to implement but also adaptable to simpler hardware configurations. Moreover, integrating impedance control into the leg movements proves advantageous, particularly when traversing uneven terrain. This article presents a novel approach in which a quadruped robot employs impedance control for each leg. It utilizes sixth-degree Bézier curves to generate reference trajectories derived from leg velocities within a planar kinematic model for body control. This scheme effectively guides the robot along predefined paths. The proposed control strategy is implemented using the Robot Operating System (ROS) and is validated through simulations and physical experiments on the Go1 robot. The results of these tests demonstrate the effectiveness of the control strategy, enabling the robot to track reference trajectories while showing stable walking and trotting gaits.

A Novel Foot Placement Planning of Biped Robots Based on A* Algorithm and Kinematic Manipulability

In motion planning of biped robots, the place where the swing foot lands on the ground is crucial. Stability, energy consumption, smoothness of movement as well as naturalness of motion can be depending on the selected foot placement. In this paper, a novel method based on the A* algorithm and kinematic manipulability for foot placement planning is presented. In this method, by calculating the magnitude and direction of the maximum manipulability for the candidate foot placements, one is selected that leads to the most dexterous and agile walking while maintaining stability. The advantage of this approach is avoiding singularities and being ready to create a higher potential for foot velocities. Therefore, the possibility of performing maneuvers to maintain walking stability would be enhanced. The mentioned algorithm has been successfully implemented on a simulated three-dimensional biped robot.

Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.

Citrus fruits are extensively cultivated fruits with high nutritional value. The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. However, challenges arise in the identification of citrus fruit ripeness due to the similarity in color between green unripe citrus fruits and tree leaves, leading to an omission in identification. Additionally, the resemblance between partially ripe, orange-green interspersed fruits and fully ripe fruits poses a risk of misidentification, further complicating the identification of citrus fruit ripeness. This study proposed the YOLO-CIT (You Only Look Once-Citrus) model and integrated an innovative R-LBP (Roughness-Local Binary Pattern) method to accurately identify citrus fruits at distinct ripeness stages. The R-LBP algorithm, an extension of the LBP algorithm, enhances the texture features of citrus fruits at distinct ripeness stages by calculating the coefficient of variation in grayscale values of pixels within a certain range in different directions around the target pixel. The C3 model embedded by the CBAM (Convolutional Block Attention Module) replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-CIT model. Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. The experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. The YOLO-CIT model combined with the R-LBP algorithm has a Precision of 88.13%, a Recall of 93.16%, an F1 score of 90.89, a mAP@0.5 of 85.88%, and 6.1ms of average detection speed for citrus fruit ripeness identification in complex environments. The model demonstrates the capability to accurately and swiftly identify citrus fruits at distinct ripeness stages in real-world environments, effectively guiding the determination of picking targets and path planning for harvesting robots.

Collision avoidance strategy based on virtual body deformation for path planning of serial industrial robot

Collision avoidance strategy based on virtual body deformation for path planning of serial industrial robot

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Abstract During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in the planning process, and failure to avoid dynamic obstacles in time. To solve these problems, an improved hybrid indoor path planning algorithm was proposed, wherein the improved global path planning algorithm was effectually integrated with improved local path planning algorithm. Firstly, the heuristic factor of traditional A-Star algorithm was optimized, search range and nodes were reduced, and then the path generated by traditional A-Star algorithm for path planning was smoothed using the angle bisector tangent point method. Secondly, combining path and environment information, local path planning was undertaken by utilizing the improved artificial potential field algorithm, and the unreachable target points problem was addressed by adjusting the repulsive field parameters. Additionally, dynamic potential field function was constructed to make it have the ability to resolve dynamic obstacles. Finally, in the part of actual environment verification, a comparison was made in this paper to assess the performance of the traditional hybrid algorithm against the improved algorithm in terms of path planning. The consequences showed that, by the hybrid algorithm proposed in this paper, the path planning length was reduced by 10.3%, the running time was decreased by 12.5%, and 34 redundant nodes were eliminated. The consequences indicated that the hybrid algorithm can effectively address the indoor unstructured and complex path planning problems.

Path planning of mobile robot based on improved TD3 algorithm in dynamic environment

This paper proposes an improved TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm to address the flaws of low success rate and slow training speed, when using the original TD3 algorithm in mobile robot path planning in dynamic environment. Firstly, prioritized experience replay and transfer learning are introduced to enhance the learning efficiency, where the probability of beneficial experiences being sampled in the experience pool is increased, and the pre-trained model is applied in an obstacle-free environment as the initial model for training in a dynamic environment. Secondly, dynamic delay update strategy is devised and OU noise is added to improve the success rate of path planning, where the probability of missing high-quality value estimate is reduced through changing the delay update interval dynamically, and the correlated exploration of the mobile robot inertial navigation system in the dynamic environment is temporally improved. The algorithm is tested by simulation where the Turtlebot3 robot model as a training object, the ROS melodic operating system and Gazebo simulation software as an experimental environment. Meanwhile, the result shows that the improved TD3 algorithm has a 16.6 % increase in success rate and a 23.5 % reduction in algorithm training time. A generalization experiment was designed finally, and it indicates that superior generation performance has been acquired in mobile robot path planning with continuous action spaces through the improved TD3 algorithm.

Indoor Localization and Path Planning of Mobile Disinfection and Epidemic Prevention Robot

Introduction: In response to the challenge of autonomous localization and navigation for mobile disinfection robots in unknown environments during the COVID-19 pandemic based on the existing patent technology of mobile robots, a localization and navigation approach combining the Cartographer-Simultaneous Localization and Mapping (SLAM) algorithm, an improved A* algorithm, and the Dynamic Window Approach (DWA) has been proposed, enabling effective disinfection and prevention work. Method: First, a comprehensive analysis of two laser SLAM algorithm frameworks, Gmapping- SLAM and Cartographer-SLAM, was conducted, which determined the Cartographer algorithm as the optimal SLAM algorithm for the mobile disinfection robot due to its superior performance. Second, an improved A* algorithm was developed based on its principles and integrated with the DWA algorithm for optimal path planning. Result: Simulation and prototype experiments demonstrated that the proposed approach effectively solved the autonomous localization and navigation challenges faced by mobile disinfection robots in unknown environments, enabling successful disinfection and prevention tasks. Conclusion: The proposed approach has the potential to lead to new patent applications in this field.

Trajectory Planning Research Based on Rapidly-exploring Random Trees (RRT) Algorithm and Modified Forms

With the rapid advancement of computer technology, robots have become indispensable in various fields. Trajectory planning plays a crucial role in optimizing robot performance by enhancing work efficiency and reducing operational costs. While the widely used Rapidly-exploring Random Trees (RRT) algorithm is effective in path planning, it often generates non-optimal paths with poor smoothness. By incorporating elements from RRT-Connect and reverse optimization, the algorithm can efficiently navigate through intricate and dynamic surroundings. Furthermore, the utilization of cubic polynomial interpolation in optimizing path curves adds a layer of sophistication to the trajectory planning process. This interpolation technique allows for the creation of smoother and more natural path trajectories, reducing jerky movements and enhancing overall motion control precision. The paths produced by the enhanced RRT algorithm demonstrate increased continuity and visual attractiveness, crucial for tasks necessitating accurate and elegant robotic motions. The improved algorithm showcases reduced path length and time consumption, as well as enhanced path smoothness. Particularly, relative to the traditional RRT approach, the time consumption has dropped by 69.15% and the median duration of the paths has fallen by 21.11%. These improvements signify a substantial advancement in trajectory planning for robots, offering more efficient and smoother paths for robotic operations across various applications.

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.

Research on Gait Trajectory Planning of Wall-Climbing Robot Based on Improved PSO Algorithm

Research on Gait Trajectory Planning of Wall-Climbing Robot Based on Improved PSO Algorithm

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm.

Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Quicker Path planning of a collaborative dual-arm robot using Modified BP-RRT* algorithm

Path-planning of an industrial robot is an important task to reduce the overall operation time. In industrial tasks, path planning is executed with lead-through programming, where in most cases the robot faces singulated object configurations. Cluttered environments demand path-planning algorithms, which are sensor driven, rather than pre-programmed. Path-planning algorithms, like RRT, and RRT* and their variants have inherent problems like the duration of a search and the creation of several node samples which may lead to longer path lengths. Back Propagation-Rapidly exploring Random Tree* (BP-RRT*) algorithm was a leap forward when an obstacle is enveloped with a sphere. Due to the spherical envelope of the obstacle, this method evaluates the connection between the path and obstacle in space with a spherical envelope using the triangular function and identifies the non-collision path in 3D space. This predicts the best non-collision path in the 3D workspace. The current state-of-the-art of BP-RRT* is limited to single-arm robots. A collaborative dual-arm robot faces more problems in path planning than a single-arm robot like inter-collision of manipulator arms apart from avoiding obstacles. A Modified BP-RRT* algorithm is proposed for the dual-arm collaborative robot has a pre-stage partition of grids that makes the computation faster, efficient, and collision-free compared to the traditional path planning algorithms namely RRT, RRT*, Improved RRT* and BP-RRT*. The algorithm is implemented in simulation as well as in physical implementation for ABB YuMi dual-arm collaborative robot and the typical length of the path of the proposed modified BP-RRT* method has reduced by 53.8% from the traditional RRT method, 6.95% from the RRT* method, 7.77% from improved RRT* method and 6.83% from the BP-RRT* method. Also, the average time to grasp has reduced by 17.84%, the typical duration for search has decreased by 33.45%, the number of node samples created has reduced by 14.79% from BP-RRT* algorithm.

Trajectory planning and simulation of upper limb rehabilitation robot based on NSGA2

In order to increase the stability and comfort of hemiplegic rehabilitation training, this paper integrates B-spline curves with a non-dominated sorting genetic algorithm (NSGA2) for trajectory planning of a 5-DOF upper limb exoskeleton rehabilitation robot, the structural characteristics of the robot are analyzed. Subsequently, the forward kinematics equation is derived by the D-H method, and its correctness is verified by Adams simulation. Subsequently, the robot workspace is calculated, and the outcomes show that it satisfies the patient’s demand for activity. Finally, the trajectory curve is constructed using the 5th-order B-splines. Under kinematic constraints, the NAGA2 is used to discover the ideal time interval sequence for time, impact and energy, and then complete the trajectory planning. The simulation findings demonstrate the suggested trajectory planning method produces continuous, smooth, and high-performance motion trajectories, providing good rehabilitation training for patients.