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dynamic obstacle avoidance planning for multi-robot suspension system based on SDBO-IDWA algorithm and force-position cooperative optimization

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第4期12卷 55-76页

作者： Zhao, Xiangtang Zhao, Zhigang Su, Cheng Meng, Jiadong Xie, Junjie Sang, Hutang Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou 730070 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China

To address dynamic obstacle avoidance planning in multi-robot coordinated suspension systems (MCSS), this study proposes a hybrid method integrating an enhanced stable dung beetle optimization (SDBO) algorithm with an improved dynamic window approach (IDWA). dynamic obstacles are addressed through IDWA-based trajectory prediction, while the SDBO-IDWA algorithm optimizes obstacle avoidance trajectories for suspended objects. Furthermore, leveraging force-position cooperative optimization, the method resolves coupled kinematic and dynamic constraints inherent in MCSS. Simulation and experimental results demonstrate that the SDBO-IDWA algorithm outperforms traditional approaches, achieving a 19.95% reduction in minimum trajectory length and a 57.77% decrease in runtime for suspended objects. For towing robots, it reduces optimal trajectory length by 9.52% and fitness values by 9.44%. The findings advance planning theory and enable safe, stable multi-robot suspension systems for diverse towing applications.

关键词： suspension system dynamic obstacle avoidance stable dung beetle optimization (SDBO) improved dynamic window approach (IDWA) force-position cooperative optimization

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dynamic obstacle avoidance of UAV Using Chance Constrained Model Predictive Control

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OPTIMAL CONTROL APPLICATIONS & METHODS 2025年

作者： Cao, Lichao Chi, Haihong Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

Addressing the challenge of dynamic obstacle avoidance for drones in uncertain environments, this paper introduces an innovative algorithm known as Velocity and Position Chance Constrained Model Predictive Control with Control Barrier Functions (CCVP-MPC-CBF). This algorithm comprehensively takes into account the probabilistic constraints of relative positions and velocities between drones and obstacles, introducing the concept of collision probability and establishing corresponding thresholds. Furthermore, by incorporating Control Barrier Functions (CBF) as constraint conditions, the algorithm ensures the safety and stability of the system. This strategy effectively overcomes the limitations that traditional MPCs may encounter when dealing with uncertainties in real-world systems, maintaining the invariance of the system's set while achieving optimal performance under the premise of ensuring system safety. Additionally, the method has been extended to enable collaborative obstacle avoidance among multiple drones. Finally, through comprehensive simulation experiments, it has been verified that even in complex environments, this algorithm enables drones to effectively avoid collisions with multiple dynamic obstacles, demonstrating the effectiveness and reliability of this approach.

关键词： CBF chance-constrained dynamic obstacle avoidance MPC relative position and relative velocity

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dynamic obstacle avoidance with Enhanced Social Force Model and DWA Algorithm Using SparkLink

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SENSORS 2025年第4期25卷 992-992页

作者： Yi, Hang Lin, Ruliang Wang, Hao Wang, Yifang Ying, Cunchao Wang, Dong Feng, Lihang Beijing Aerosp Wanyuan Sci & Technol Co Ltd Beijing 100083 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 210096 Peoples R China

In the context of Industry 4.0, addressing the challenge of dynamic obstacle avoidance for Automated Guided Vehicles (AGVs) in complex industrial environments, this paper proposes an algorithm that integrates an enhanced social force model (SFM) and an improved dynamic window approach (DWA), leveraging SparkLink communication technology to enhance data transmission speed and reliability. The introduction of SparkLink technology significantly improves the environmental perception capabilities of AGVs, optimizing their dynamic obstacle-avoidance performance. Experimental results demonstrate that this method effectively increases the efficiency of AGVs in dynamic obstacle avoidance, offering significant practical value.

关键词： SparkLink AGV dynamic obstacle avoidance social force model dynamic window method

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dynamic obstacle avoidance strategy for autonomous vehicles based on LSTM model trajectory prediction under urban roads

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年

作者： Feng, Shaoxiang Liu, Fei Zhou, Sheng Wong, Xiaofeng Mai, Jiacheng Shanghai Univ Engn Sci Sch Mech & Automot Engn 333 Long Teng Rd Shanghai 201620 Peoples R China Tongji Univ Sch Automot Studies Shanghai Peoples R China

Studying obstacle avoidance strategies for self-driving cars is important research direction. We propose a LSTM-based model for predicting the trajectories of "obstacles" around the self-driving vehicle that have an impact on its motion state, so that the self-driving vehicle can carry out less risky path planning in advance to improve the safety and stability of the self-driving vehicle. First, the LSTM network is trained using the NGSIM dataset to obtain a model that can more accurately predict obstacle motion information. Then, the obstacle risk function is used as a sequential judgment index for obstacle trajectory prediction by constructing a prediction level range model as an obstacle predictable region. According to the obstacle prediction information to derive its movement trajectory in a period of prediction time domain, the self-driving car can predict the movement trajectory of the obstacle in advance, and the planning layer plans the path that can avoid the obstacle, to ensure that the vehicle travels stably and safely. Finally, the predicted trajectory is combined with the planning layer to ensure safe obstacle avoidance by the vehicle using multi-constraint OCF-MPC controller. Experiments show that the use of trajectory prediction scheme can reduce the risk of self-driving vehicle traveling.

关键词： Self-driving vehicle dynamic obstacle avoidance LSTM model trajectory prediction risk region model OCF-MPC

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dynamic obstacle avoidance and Path Planning for Mobile Robots Integrating Improved Rapidly-Exploring Random Tree-Star and Improved dynamic Window Approach dynamic obstacle avoidance and Path Planning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第3期16卷 803-812页

作者： Wei, Xianyong Si, Hongying Shangqiu Polytech Shangqiu 476000 Peoples R China Shangqiu Normal Univ Sch Math & Stat Shangqiu 476000 Peoples R China

With the application and popularization of artificial intelligence and intelligent robots in daily life, the autonomous navigation and flexible operation capabilities of mobile robots have become particularly critical. Mobile robots perform well in regular environments, but face problems such as low accuracy in dynamic obstacle avoidance and weak adaptability to complex terrains. This study proposes to enhance the adaptability of the Rapidly-exploring Random Tree Star algorithm and integrate it with the A-Star algorithm, the dynamic Window Approach, and visual sensor to construct an obstacle avoidance model. The objective is to enable the improved model to recognize various terrain features and enhance the accuracy of the path planning algorithm. The proposed model performed well in obstacle avoidance, with a success rate of 95.78% after ten training epochs and no more than four collisions within 4 minutes. In the experiment, as the obstacle increased every minute, the response speed of the proposed model remained below 25 seconds. The above results indicate that the quality of the planned path is higher than that of the other three models. The path optimization improvement combined with the A* algorithm is effective and has high real-time and accuracy, which can make mobile robots widely used in industries such as services, navigation, and logistics.

关键词： Rapidly-exploring random tree-star dynamic window approach A-star algorithm dynamic obstacle avoidance path planning mobile robot

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A cascaded dynamic obstacle avoidance strategy for cable-driven continuum robots based on kinematics and dynamics

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NONLINEAR dynamicS 2025年第11期113卷 13415-13435页

作者： Qin, Yanan Chen, Qi Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

To ensure the feasibility and safety of cable-driven continuum robots (CDCRs) in complex application scenarios with dynamic obstacles and disturbances, a novel cascaded trajectory replanning and tracking controller is proposed. The cascaded controller consists of a kinematic algorithm based on model predictive control (MPC) for trajectory replanning and a dynamic algorithm based on recursive integral terminal sliding mode control (RITSMC) for trajectory tracking. The MPC based trajectory replanning algorithm updates the trajectory of CDCRs to avoid collision with obstacles. The utilization of soft constraint optimization strategy in MPC guarantees the safety of CDCRs during obstacle avoidance, as well as preventing the issue of infeasible solutions that could arise from hard constraints. Furthermore, a super-twisting observer (STO) is integrated into MPC (STO-MPC) to estimate the state of dynamic obstacles and ensure the feasibility of dynamic obstacle avoidance. The RITSMC guarantees fast and accurate trajectory tracking control without imposing significant computational burden compared to MPC. Additionally, an adaptive disturbance observer (ADO) is introduced into RITSMC (ADO-RITSMC) to approximate disturbances and make a compensation, enhancing robustness and accuracy of trajectory tracking control. Experiment cases demonstrate that the cascaded controller exhibits superior performance in trajectory replanning and tracking control for the CDCR with dynamic obstacles and disturbances.

关键词： Cable-driven continuum robots dynamic obstacle avoidance Model predictive control Recursive integral terminal sliding mode control Trajectory replanning Trajectory tracking

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dynamic obstacle avoidance control based on a novel dynamic window approach for agricultural robots

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COMPUTERS IN INDUSTRY 2025年 167卷

作者： Wang, Jichun Yang, Liangliang Cen, Haiyan He, Yong Liu, Yufei Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Peoples R China Kitami Inst Technol Fac Engn Kitami 0908507 Japan

With the ongoing advancements in autonomous navigation technology, agricultural robots are increasingly being deployed across various sectors of agriculture. Among the critical components of this technology, dynamic obstacle avoidance in complex agricultural environments serves as the foundation for enhancing the autonomy and safety of these robots. The dynamic Window Approach (DWA) is a widely recognized method for achieving local obstacle avoidance. It operates by sampling the robot's velocity space and then evaluating the sampled trajectories using a value function to determine the optimal velocity pair. However, a significant limitation of the traditional DWA method lies in its fixed weights for the value function, which restricts its performance to manual tuning and renders it less adaptable to intricate and dynamic obstacle environments. To address this limitation, we introduced an innovative approach by integrating the Twin Delayed Deep Deterministic Policy Gradient (TD3) method into the weight determination process of the DWA algorithm's value function. This integration enabled the weight coefficients to adapt dynamically in response to environmental variations, thereby enhancing the algorithm's flexibility and effectiveness. Our extensive simulation and field testing revealed that while the traditional DWA algorithm struggled to navigate complex dynamic obstacle environments, the proposed TD3DWA algorithm achieved a success rate of over 90 % in obstacle avoidance. This outcome underscored the algorithm's adaptability and robustness, positioning it as a reliable solution for ensuring safe and efficient navigation in agricultural robotics.

关键词： dynamic obstacle avoidance Reinforcement learning Agricultural robot DWA TD3

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Gaussian belief propagation for dynamic obstacle avoidance and formation control in second-order multi-agent systems

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INFORMATION SCIENCES 2025年 707卷

作者： Huang, Zexin Liu, Zhi Tan, Meijian Chen, C. L. Philip Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Guangdong Univ Technol Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

This paper proposes a control strategy that combines Gaussian Belief Propagation (GBP) with Artificial Potential Field (APF) method, enabling Multi-Agent Systems (MASs) to achieve consensus in formation control while flexibly responding to dynamic obstacles within the based framework. Existing APF-based methods are difficult to cope with fast-moving obstacles exceeding the speed threshold, while the adaptive formation control and stochastic dynamic obstacle avoidance methods proposed in this paper effectively address this challenge. By utilizing the control method proposed in this paper, the MASs are able to accurately predict the position of such obstacles and pre-plan the obstacle avoidance path. In addition, they are able to seamlessly return to the desired formation trajectory after effectively solving the collision avoidance challenge, which proves the generalizability of our newly proposed method in various dynamic scenarios. This research offers novel insights and approaches for adaptive control dynamic obstacle avoidance in MASs.

关键词： Multi-agent systems Gaussian belief propagation dynamic obstacle avoidance Nonlinear analysis Adaptive control

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dynamic obstacle avoidance model of autonomous driving with attention mechanism and temporal residual block

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ALEXANDRIA ENGINEERING JOURNAL 2024年 105卷 538-548页

作者： Chi, Xinrui Guo, Zhanbin Cheng, Fu Heilongjiang Bayi Agr Univ Sch Engn Daqing 163319 Peoples R China

dynamic obstacle avoidance is crucial in autonomous driving, ensuring vehicle safety by preventing collisions and enhancing driving efficiency. dynamic obstacle avoidance algorithms have made significant progress due to deep learning. However, video-based target detection methods can suffer from missed or false detections when processing consecutive frames, especially for high-speed moving targets or in complex dynamic scenes. Multitarget tracking methods require intricate algorithm designs for target initialization and occluded object recovery, which can be compromised by tracker performance, leading to unstable tracking or target loss. To address the issue of target loss in multi-target tracking, we designed the novel YTCN model that infuses time series information through temporal convolution and enhances the sensitivity of the receptive field with Spatial Pyramid Pooling, Feature Concatenate and Spatial Convolution(SPPFCSPC), enhancing the model's feature extraction capability. Simultaneously, we developed the novel Global Attention Mechanism(GAM) and Double Attention (DA) mechanisms that merge channel and spatial features to enhance feature representation. Finally we design the Temporal Residual Block(TRB) to model the temporal obstacles. Experimental results show that our method achieves 82.4% of mAP_0.5 on the BDD 100 K dataset, which gets 1.3% higher than the previous methods.

关键词： dynamic obstacle avoidance TRB SPPFCSPC Global attention mechanism Double attention

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dynamic obstacle avoidance for manipulators using distance calculation and discrete detection

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2018年 49卷 98-104页

作者： Han, Dong Nie, Hong Chen, Jinbao Chen, Meng Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China Aerosp Syst Engn Shanghai Shanghai Peoples R China

In order to avoid dynamic obstacle timely during manufacturing tasks performed by manipulators, a novel method based on distance calculation and discrete detection is proposed. The nearest distances between the links of a manipulator and the convex hull of an arbitrarily-shaped dynamic obstacle obtained from Kinect-V2 camera in real-time are calculated by Gilbert Johnson Keerthi algorithm, and the minimum one is defined as the closest distance between the manipulator and the obstacle. When the closest distance is less than a safe value, whether the dynamic obstacle is located in the global path of the manipulator is determined by improved discrete collision detection, which can adjust detection step-size adaptively for accuracy and efficiency. If the obstacle will collide with the manipulator, set a local goal and re-plan a local path for the manipulator. The proposed method is implemented in Robot Operating System (ROS) using C++. The experiments indicate that the proposed method can perform safe and timely dynamic avoidance for redundant manipulators in human-robot interaction. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： dynamic obstacle avoidance Distance calculation Collision detection Redundant manipulators Human-robot interaction

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