智能机器人技术：自主性与适应性研究
本专题深入探讨了当前机器人技术领域的前沿研究方向，涵盖了从机器人能源驱动、路径规划技术到智能搜救和生态圈自主任务决策等多个关键议题。这不仅推动了机器人技术的创新和发展，而且对于提高机器人在搜索救援、环境监测、军事侦察等应用场景中的效率和适应性具有重要意义。
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2023, 45(3):76-85.
DOI: 10.11887/j.cn.202303009
Abstract:
Micro-soft robots with high compliance, low energy consumption and high energy density have wide application prospects in complex environments such as pipeline maintenance and battle field reconnaissance. Energy and actuator play a decisive role in the motion modes and performances of micro-soft robots. In order to make more researchers understand the research progresses of the existing flexible driving technology and their energy sources, the typical driving methods based on physical energy driving, chemical energy driving and biological hybrid driving were summarized and analyzed. The shortcomings and future development of the existing flexible driving technology and their energy sources were discussed and summarized, which can provide reference for the development and improving performance of the flexible drive technology of soft robots in the future.
Micro-soft robots with high compliance, low energy consumption and high energy density have wide application prospects in complex environments such as pipeline maintenance and battle field reconnaissance. Energy and actuator play a decisive role in the motion modes and performances of micro-soft robots. In order to make more researchers understand the research progresses of the existing flexible driving technology and their energy sources, the typical driving methods based on physical energy driving, chemical energy driving and biological hybrid driving were summarized and analyzed. The shortcomings and future development of the existing flexible driving technology and their energy sources were discussed and summarized, which can provide reference for the development and improving performance of the flexible drive technology of soft robots in the future.
2021, 43(1):127-138.
DOI: 10.11887/j.cn.202101017
Abstract:
As an important carrier of group intelligence, robot clusters have also received a lot of research and development and more and more applications. Robot cluster path planning technology has also developed rapidly as a core key technology. The technical development status of robot cluster path planning was comprehensively and deeply researched. The basic computing architecture applying to different cluster sizes, scalability requirements, communication requirements, and algorithms of the requirements was innovatively summarized. The basic computing architecture consists of redundant computing architecture, distributed computing architecture, and hierarchical computing architecture. From the perspective of scalability and applicability, the path planning method which takes advantage of the most suitable robot clusters, including bionics, artificial potential field method, geometric method, classical search method and evolutionary learning method, was classified. For the cluster simulation verification research, it provided seven free downloadable or open source robot cluster simulation verification platforms.
As an important carrier of group intelligence, robot clusters have also received a lot of research and development and more and more applications. Robot cluster path planning technology has also developed rapidly as a core key technology. The technical development status of robot cluster path planning was comprehensively and deeply researched. The basic computing architecture applying to different cluster sizes, scalability requirements, communication requirements, and algorithms of the requirements was innovatively summarized. The basic computing architecture consists of redundant computing architecture, distributed computing architecture, and hierarchical computing architecture. From the perspective of scalability and applicability, the path planning method which takes advantage of the most suitable robot clusters, including bionics, artificial potential field method, geometric method, classical search method and evolutionary learning method, was classified. For the cluster simulation verification research, it provided seven free downloadable or open source robot cluster simulation verification platforms.
2023, 45(6):132-142.
DOI: 10.11887/j.cn.202306015
Abstract:
In order to solve the configuration planning problem for intelligent search and rescue robots with assisted flippers to achieve autonomous obstacle crossing, a novel method for planning robot configuration during obstacle crossing was proposed that can be applied to complex terrains. The core of the proposed method is an adaptable and efficient robot pose prediction algorithm. By representing the terrain as a series of discrete point sets, a mathematical model for predicting the one-sided pose of the tracked robot was established; further, a fast solver for this model was proposed, which can predict 1000~1500 poses per second. Based on this, the evaluation metrics of the robot′s state and action in the obstacle-crossing process were established, and an optimization-based real-time flippers action planner was realized by using the dynamic programming algorithm and rolling optimization. The simulation and real-robot experiments show that the proposed approach enables the robot to control the flippers to cross rough terrains autonomously. It performs more smoothly than the reinforcement-learning method and manual operation when crossing obstacles.
In order to solve the configuration planning problem for intelligent search and rescue robots with assisted flippers to achieve autonomous obstacle crossing, a novel method for planning robot configuration during obstacle crossing was proposed that can be applied to complex terrains. The core of the proposed method is an adaptable and efficient robot pose prediction algorithm. By representing the terrain as a series of discrete point sets, a mathematical model for predicting the one-sided pose of the tracked robot was established; further, a fast solver for this model was proposed, which can predict 1000~1500 poses per second. Based on this, the evaluation metrics of the robot′s state and action in the obstacle-crossing process were established, and an optimization-based real-time flippers action planner was realized by using the dynamic programming algorithm and rolling optimization. The simulation and real-robot experiments show that the proposed approach enables the robot to control the flippers to cross rough terrains autonomously. It performs more smoothly than the reinforcement-learning method and manual operation when crossing obstacles.
2023, 45(1):208-214.
DOI: 10.11887/j.cn.202301024
Abstract:
The amphibious bionic robot is an unmanned system which can work both underwater and on land, which has been widely used in the fields of disaster rescue, environmental detection and resource exploration. An amphibious robot compounded with wheel and fin with the ability of self-adaptive climbing was proposed in this paper. The kinematic and dynamic mechanics of the self-adaptive climbing process was analyzed. The torque required of the critical obstacle crossing point was set as the objective function, and the optimized design structural and operational parameters were obtained by applying the genetic algorithm. Meanwhile, the climbing ability of the amphibious robot in this work was compared with others. The results illustrate that the required torque of the amphibious robot was reduced by 718.4 N·mm. The robot compounded with wheel and fin can climb the vertical obstacle of a larger height. The self-adaptive climbing process of the optimized robot was simulated. The simulation results illustrate that the variation of the propulsive velocity, the displacement and the torque in the processes of moving forward and climbing the obstacles. The experiments of the obstacle climbing was investigated for verifying the structural and operational parameters design.
The amphibious bionic robot is an unmanned system which can work both underwater and on land, which has been widely used in the fields of disaster rescue, environmental detection and resource exploration. An amphibious robot compounded with wheel and fin with the ability of self-adaptive climbing was proposed in this paper. The kinematic and dynamic mechanics of the self-adaptive climbing process was analyzed. The torque required of the critical obstacle crossing point was set as the objective function, and the optimized design structural and operational parameters were obtained by applying the genetic algorithm. Meanwhile, the climbing ability of the amphibious robot in this work was compared with others. The results illustrate that the required torque of the amphibious robot was reduced by 718.4 N·mm. The robot compounded with wheel and fin can climb the vertical obstacle of a larger height. The self-adaptive climbing process of the optimized robot was simulated. The simulation results illustrate that the variation of the propulsive velocity, the displacement and the torque in the processes of moving forward and climbing the obstacles. The experiments of the obstacle climbing was investigated for verifying the structural and operational parameters design.
2022, 44(5):209-219.
DOI: 10.11887/j.cn.202205023
Abstract:
Based on the typical characteristics of natural ecosystems, the concept of robot ecosystem was proposed. Through the intelligent coordination and complex evolution of cluster robots, life features such as self-sustaining, self-replication and self-evolution emerged, enabling them to achieve long-term survival, reproduction and evolution under unmanned conditions, and perform specific tasks. According to the requirements of autonomous task decision-making in typical task scenarios of robot ecosystem, the characteristics of different machine learning task decision-making methods were analyzed, and the decision tree model and neural network model of autonomous task decision-making in robot ecosystem were established. The analysis shows that the accuracy of the two models is 80%~90%, and both have good stability. The results show that the autonomous task decision-making problem of robot ecosystem can be well solved by machine learning methods such as decision tree and neural network, so as to provide technical support for task application in unmanned scenes.
Based on the typical characteristics of natural ecosystems, the concept of robot ecosystem was proposed. Through the intelligent coordination and complex evolution of cluster robots, life features such as self-sustaining, self-replication and self-evolution emerged, enabling them to achieve long-term survival, reproduction and evolution under unmanned conditions, and perform specific tasks. According to the requirements of autonomous task decision-making in typical task scenarios of robot ecosystem, the characteristics of different machine learning task decision-making methods were analyzed, and the decision tree model and neural network model of autonomous task decision-making in robot ecosystem were established. The analysis shows that the accuracy of the two models is 80%~90%, and both have good stability. The results show that the autonomous task decision-making problem of robot ecosystem can be well solved by machine learning methods such as decision tree and neural network, so as to provide technical support for task application in unmanned scenes.
2021, 43(6):85-94.
DOI: 10.11887/j.cn.202106011
Abstract:
Aiming at the urgent need for human and remote unmanned devices to cooperate precisely and accurately, a remote situational awareness system of human-machine integration was proposed based on the robot operating system, and experiments and analysis were carried out. Based on visual positioning technology, with the integration of human-machine perception as the breakthrough point, through real-time 3D scene reconstruction technology and scene consistency fusion method, the environment and target information detected by unmanned equipment were 3D reconstructed. The result was consistent and fused with the human visual information, and displayed by the augmented reality device, realizing the coordinated positioning between the remote unmanned device and the augmented reality device worn by the person without GPS. The experimental results show that the system has better performance at close range. The accuracy of human-machine coordinated positioning is gradually reduced as the distance increases. The proposed system makes the unmanned device an extension of the human eye, realizing the ability to penetrate obstacles, and crosses the sight distance without interfering with the normal movement of personnel. It can play an important role in future information operations.
Aiming at the urgent need for human and remote unmanned devices to cooperate precisely and accurately, a remote situational awareness system of human-machine integration was proposed based on the robot operating system, and experiments and analysis were carried out. Based on visual positioning technology, with the integration of human-machine perception as the breakthrough point, through real-time 3D scene reconstruction technology and scene consistency fusion method, the environment and target information detected by unmanned equipment were 3D reconstructed. The result was consistent and fused with the human visual information, and displayed by the augmented reality device, realizing the coordinated positioning between the remote unmanned device and the augmented reality device worn by the person without GPS. The experimental results show that the system has better performance at close range. The accuracy of human-machine coordinated positioning is gradually reduced as the distance increases. The proposed system makes the unmanned device an extension of the human eye, realizing the ability to penetrate obstacles, and crosses the sight distance without interfering with the normal movement of personnel. It can play an important role in future information operations.
2022, 44(4):52-59.
DOI: 10.11887/j.cn.202204006
Abstract:
When the robot is running in a complex environment with densely distributed obstacles, the DWA (dynamic window approach) algorithm is prone to obstacle avoidance failure or unreasonable planning. In this regard, an improved DWA planning algorithm based on MOPSO(multi-objective particle swarm optimization) was proposed. Based on the establishment of multi obstacle environment coverage model, a method was put forward for judging obstacle-dense areas in complex environments. And the original DWA algorithm was improved by optimizing the sub-evaluation functions. On these basis of the improved MOPSO algorithm, the adaptive change of DWA weight coefficients were transformed into a multi-objective optimization problem. According to the requirements of path planning, the safety distance and speed can be set as the optimization goals, moreover, the corresponding multi-objective optimization model was established. The results of a series of simulations show that this method enables the robot to effectively pass through the dense area of obstacles while taking account of the safety and speed of operation, and has better path planning effect.
When the robot is running in a complex environment with densely distributed obstacles, the DWA (dynamic window approach) algorithm is prone to obstacle avoidance failure or unreasonable planning. In this regard, an improved DWA planning algorithm based on MOPSO(multi-objective particle swarm optimization) was proposed. Based on the establishment of multi obstacle environment coverage model, a method was put forward for judging obstacle-dense areas in complex environments. And the original DWA algorithm was improved by optimizing the sub-evaluation functions. On these basis of the improved MOPSO algorithm, the adaptive change of DWA weight coefficients were transformed into a multi-objective optimization problem. According to the requirements of path planning, the safety distance and speed can be set as the optimization goals, moreover, the corresponding multi-objective optimization model was established. The results of a series of simulations show that this method enables the robot to effectively pass through the dense area of obstacles while taking account of the safety and speed of operation, and has better path planning effect.
