[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-BI-RRT*":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"BI-RRT*","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":15,"published_at":18,"category":19,"cover_url":15,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":30,"tags":32,"search_phrases":39,"slug":42,"view_count":43,"doi":44,"paper":15,"created_at":45},246,"改进BI-RRT*的黄鳝投喂机械臂路径规划算法——张春燕教授团队《智慧农业(中英文)》2026年第2期","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020","上海工程技术大学机械与汽车工程学院张春燕教授团队联合上海市农业科学院生态环境保护研究所,在《智慧农业(中英文)》2026年第2期发表论文,提出一种基于改进BI-RRT*(Bidirectional Rapidly-Exploring Random Tree Star)的受限空间路径规划算法。针对工厂化立体式黄鳝养殖饵料投喂机械臂在受限空间内路径规划速度慢、轨迹冗余度大、避障成功率低等问题,在BI-RRT*双向扩展策略基础上引入目标偏置策略减少随机采样点,同时结合改进的人工势场法融入目标点和随机点引力概念,通过自适应调节引力系数引导路径向目标节点扩展,有效避免算法陷入局部最优。在Matlab平台上对RRT、APF-RRT、BI-RRT及改进BI-RRT算法进行仿真分析,验证其在二维和三维空间下简单、受限及复杂环境中的优异表现。",null,"**摘要：**\n\n**【目的\u002F意义】** 针对工厂化立体式黄鳝养殖饵料投喂机械臂在受限空间内路径规划速度慢、轨迹冗余度大、避障成功率低等问题，提出一种基于改进BI-RRT*（Bidirectional Rapidly-Exploring Random Tree Star）的受限空间路径规划算法。 **【方法】** 在BI-RRT*算法双向扩展策略基础上，引入目标偏置策略，减少随机采样点。同时结合改进的人工势场法，融入目标点和随机点引力概念，通过自适应调节引力系数引导路径向目标节点扩展，有效避免算法陷入局部最优；最后通过渐进优化策略得到最佳轨迹。 **【结果和讨论】** 在Matlab平台上，对RRT*算法、APF-RRT*（ Artificial Potential Fields Rapidly-Exploring Random Tree Star）算法、BI-RRT*算法及改进BI-RRT*算法进行了仿真分析，研究其在二维和三维空间下简单、受限，以及复杂环境中的表现。仿真数据显示，在不同环境运行中，改进BI-RRT*算法均展现出显著的性能优势。基于黄鳝养殖投喂场景，搭建机械臂机器人操作系统仿真环境并进行受限空间下投喂试验。结果表明，与BI-RRT*算法相比，改进BI-RRT*算法平均运行时间减少41.6%，平均路径长度降低2.3％，平均节点数减少37.9％，以及投喂成功率提高6％。 **【结论】** 试验验证改进BI-RRT*算法在投喂任务中展现出更优性能，为工厂化立体式黄鳝养殖高效投饵工作提供了参考。\n\n**关键词:**[投喂机械臂,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[路径规划,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[改进BI-RRT*算法,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[目标偏置策略,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[改进人工势场法](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)\n\n**Abstract:**\n\n**[Objective]** In the eel (_Monopterus albus_) farming system used in feed distribution research of mechanical arm, the challenges included slow path planning speeds, excessive trajectory redundancy, and suboptimal obstacle avoidance success rates within confined operational spaces. To mitigate these issues, an improved path planning algorithm, based on the bidirectional rapidly-exploring random tree star (BI-RRT*) algorithm was proposed. The primary aim was to significantly enhance the motion efficiency and task success rate of robotic arms operating in complex, constrained environments. **[Methods]** The proposed improved BI-RRT* algorithm integrated an adaptive goal-biased strategy with an enhanced artificial potential field (APF) method. The algorithm's framework comprises three core components: a high-quality sampling strategy, an efficient search strategy, and a path optimization algorithm. For the high-quality sampling strategy, an adaptive goal-biased approach was introduced to overcome the limitations of inefficient random sampling and slow convergence rates characteristic of traditional BI-RRT algorithms in complex environments. This strategy dynamically adjusted the generation of sampling points, moving beyond purely random selection. Instead, it prioritized sampling regions in the vicinity of the target, guided by the target direction and a predefined bias probability. This mechanism substantially augmented the growth propensity of the search tree towards the target area, effectively reducing the stochasticity of random sampling and consequently accelerating the path search process. To enhance search efficiency and prevent the algorithm from converging to local optima, an improved APF was incorporated into the node expansion process. The APF was refined to achieve superior integration with the BI-RRT framework. During each new node expansion, in addition to considering the inherent random exploration characteristics of BI-RRT, a directional attractive field was superimposed. This attractive field not only originated from the ultimate target point but also factored in the current growth orientation of the search tree and localized environmental information. Specifically, a composite attractive function was devised, which synergized the attractive force exerted by the target point on the current node with the attraction from potential \"guide points\". Concurrently, the computation of the repulsive field was optimized to more precisely delineate the geometry and proximity of obstacles, thereby circumventing common issues such as \"oscillation\" and \"deadlock\" prevalent in traditional APF. Through this methodology, the algorithm was able to more effectively steer the search tree to circumvent obstacles and rapidly converge towards the target region, significantly bolstering the directness of the search and successfully preventing the algorithm from becoming ensnared in suboptimal local solutions. For the path optimization algorithm, following the generation of an initial feasible path, a greedy optimization strategy was employed for path pruning and smoothing. This was executed to yield an optimal path characterized by reduced length, enhanced smoothness, and improved conformity with the kinematic properties of the robotic arm. Path pruning was initially applied to eliminate redundant nodes; if a collision-free direct connection existed between two non-adjacent nodes, intermediate nodes were excised, thereby substantially abbreviating the path length. Subsequently, path smoothing techniques, such as B-spline curves or cubic spline interpolation, were introduced to enable the robotic arm to execute movements with greater stability and efficiency during actual operation, mitigating impact and vibration. This two-stage optimization procedure ensured that the final generated path was not merely feasible but also optimal across metrics of length, smoothness, and motion efficiency. **[Results and Discussions]** To comprehensively validate the performance of the proposed algorithm, a two-stage experimental verification was conducted. Initially, comparative simulations were performed in both two-dimensional (2D) and three-dimensional (3D) environments utilizing the Matlab platform. These simulation scenarios were meticulously engineered to encompass three archetypal environments—simple, complex, and narrow passages—thereby emulating the diverse obstacle configurations potentially encountered in industrialized eel aquaculture. The results demonstrated that, concerning both path planning speed and quality, the improved BI-RRT* algorithm significantly surpassed RRT, APF-RRT*, and traditional BI-RRT* algorithms across all tested environments, substantiatingthe theoretical superiority and inherent robustness of the improved BI-RRT* algorithm proposed in this study across varying complex environments. To further ascertain the engineering applicability and practical potential of the algorithm, an eel feeding robotic arm simulation system was meticulously constructed based on the robot operating system and MoveIt frameworks. This system precisely emulated the kinematics, dynamics, and obstacle distribution pertinent to an industrialized eel aquaculture environment. During simulated continuous feeding tasks, the improved BI-RRT* algorithm consistently exhibited impressive and outstanding performance. Its average running time was merely 2.1 s, representing a substantial 41.6% reduction compared to the traditional BI-RRT*. The average length of the planned path was recorded at only 1 680 mm, with an average of 180 nodes, indicating a significant reduction in path redundancy. Furthermore, the algorithm achieved an impressive obstacle avoidance success rate of 96% in complex confined spaces. These empirical findings not only validated the algorithm's effectiveness but also underscored its immense potential for practical engineering applications. **[Conclusions]** The experimental results conclusively demonstrated that the improved BI-RRT* algorithm significantly enhanced the path planning efficiency and trajectory quality of robotic arms operating within confined spaces. It also exhibited exceptionally high reliability in obstacle avoidance, thereby effectively addressing the automated feeding requirements of industrialized eel aquaculture. The algorithmic framework possessed considerable generality, offering valuable theoretical insights and technical precedents for resolving analogous robotic arm path planning challenges in other agricultural automation contexts.\n\n**Key words:**[feeding robotic arm,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[path planning,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[improved BI-RRT* algorithm,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[goal-biased strategy,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[refined artificial potential field method](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)\n\n**中图分类号:**\n\n*   [S966.4](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)\n\n*   [TP242.3](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)","智慧农业(中英文) 2026, 8(2): 251-264","2026-08-03T00:00:00Z","论文",10,false,76,{"impact":24,"substance":25,"depth":26,"authority":27,"freshness":28,"relevant":7,"comment":29},15,22,18,14,7,"针对黄鳝养殖投喂机械臂的路径规划算法改进，方法新颖，数据详实，属于细分行业进展，但影响范围有限。",[31],{"name":17,"url":13},[33,34,35,4,36,37,38],"智慧农业","路径规划","上海市农科院","黄鳝投喂","机械臂","上海工程技术大学",[40,41],"上海工程技术大学 上海市农科院 智慧农业 路径规划","上海工程技术大学 上海市农科院","上海工程技术大学上海市农科院智慧农业路径规划-246",5,"10.12133\u002Fj.smartag.sa202509020","2026-08-05T23:53:02.427048Z"]