The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the prop...The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation, two test scenarios of static environment with different number obstacles were adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.展开更多
In this paper, robot path planning in globally unknown environments is studied. Using the rolling optimization concept in predictive control for reference, a new strategy of path planning for a mobile robot, based on ...In this paper, robot path planning in globally unknown environments is studied. Using the rolling optimization concept in predictive control for reference, a new strategy of path planning for a mobile robot, based on rolling windows, is proposed. The method makes full use of the real-time local environmental information detected by the robot and the on-line path planning is carried on in a rolling style. Optimization and feedback are combined in a reasonable way. The convergence of the planning algorithm is also discussed.展开更多
Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path ...Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.展开更多
Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the conf...Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.展开更多
The honey badger algorithm(HBA),as a new swarm intelligence(SI)optimization algorithm,has shown certain effectiveness in its applications.Aiming at the problems of unsatisfactory initial population distribution of HBA...The honey badger algorithm(HBA),as a new swarm intelligence(SI)optimization algorithm,has shown certain effectiveness in its applications.Aiming at the problems of unsatisfactory initial population distribution of HBA,poor ability to avoid local optimum,and slow convergence speed,this paper proposes a multi-strategy improved HBA based on periodical mutation and t-distribution perturbation,called MHBA.Firstly,a good point set population initialization is introduced to get a uniform initial population.Secondly,periodic mutation and t-distribution perturbation are successively used to improve the algorithm’s ability to avoid local optimum.Finally,the density factor is improved for balancing exploration and exploitation.By comparing MHBA with HBA and 7 other SIs on 6 benchmark functions,it is evident that the performance of MHBA is far superior to HBA.In addition,by applying MHBA to robot path planning,MHBA can identify the shortest path more quickly and consistently compared with competitors.展开更多
The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equili...The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium.Despite its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population diversity.To alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called SCEO.Firstly,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation.Next,an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity.Finally,the Cauchy mutation is utilized to prevent the loss of population diversity during searching.To validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite.The outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic techniques.The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors.In addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic approaches.The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.展开更多
Obstacle avoidance is quite an important issue in the field of legged robotic applications, such as rescuing and detecting in complicated environment. Most related researchers focused on the legged robot’s gait gener...Obstacle avoidance is quite an important issue in the field of legged robotic applications, such as rescuing and detecting in complicated environment. Most related researchers focused on the legged robot’s gait generation after ssuming that obstacles have been detected and the walking path has been given. In this paper we propose and validate a novel obstacle avoidance framework for a six-legged walking robot Hexapod-III in unknown environment. Throughout the paper we highlight three themes: (1) The terrain map modeling and the obstacle detection; (2) the obstacle avoidance path planning method; (3) motion planning for the legged robot. Concretely, a novel geometric feature grid map (GFGM) is proposed to describe the terrain. Based on the GFGM, the obstacle detection algorithm is presented. Then the concepts of virtual obstacles and safe conversion pose are introduced. Virtual obstacles restrict the robot to walk on the detection terrain. A safe path based on Bezier curves, passing through safe conversion poses, is obtained by minimizing a penalty function taking into account the path length subjected to obstacle avoidance. Thirdly, motion planning for the legged robot to walk along the generated path is discussed in detail. At last, we apply the proposed framework to the Hexapod-III robot. The experimental result shows that our methodology allows the robot to walk safely without encountering with any obstacles in unknown environment.展开更多
In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring safety.Human Autonomy Teaming(HAT)is crucial for promoting safe and e...In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring safety.Human Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor environments.This paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such settings.Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key waypoints.An integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and performance.Furthermore,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the system.Human insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes.Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of HAT.This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.展开更多
基金Project(61173032)supported by the National Natural Science Foundation of ChinaProject(20090406)supported by the Tianjin Scientific and Technological Development Fund of Higher Education of China
文摘The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation, two test scenarios of static environment with different number obstacles were adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.
基金the National 973 Plan (Grant No. G1998030415) and the National Natural Science Foundation of China (Grant No. 69774004) and the National 863 Program (Grant No. 9805-18).
文摘In this paper, robot path planning in globally unknown environments is studied. Using the rolling optimization concept in predictive control for reference, a new strategy of path planning for a mobile robot, based on rolling windows, is proposed. The method makes full use of the real-time local environmental information detected by the robot and the on-line path planning is carried on in a rolling style. Optimization and feedback are combined in a reasonable way. The convergence of the planning algorithm is also discussed.
文摘Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.
基金This work was partially supported by National Key R&D Program of China(2019YFB1312400)Shenzhen Key Laboratory of Robotics Perception and Intelligence(ZDSYS20200810171800001)+1 种基金Hong Kong RGC GRF(14200618)Hong Kong RGC CRF(C4063-18G).
文摘Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.
基金Supported by the National Key Research and Development Program of China(No.2022ZD0119001)。
文摘The honey badger algorithm(HBA),as a new swarm intelligence(SI)optimization algorithm,has shown certain effectiveness in its applications.Aiming at the problems of unsatisfactory initial population distribution of HBA,poor ability to avoid local optimum,and slow convergence speed,this paper proposes a multi-strategy improved HBA based on periodical mutation and t-distribution perturbation,called MHBA.Firstly,a good point set population initialization is introduced to get a uniform initial population.Secondly,periodic mutation and t-distribution perturbation are successively used to improve the algorithm’s ability to avoid local optimum.Finally,the density factor is improved for balancing exploration and exploitation.By comparing MHBA with HBA and 7 other SIs on 6 benchmark functions,it is evident that the performance of MHBA is far superior to HBA.In addition,by applying MHBA to robot path planning,MHBA can identify the shortest path more quickly and consistently compared with competitors.
基金support from the National Natural Science Foundation of China[Grant Nos.61461053,61461054,and 61072079]Yunnan Provincial Education Department Scientific Research Fund Project[2022Y008].
文摘The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium.Despite its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population diversity.To alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called SCEO.Firstly,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation.Next,an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity.Finally,the Cauchy mutation is utilized to prevent the loss of population diversity during searching.To validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite.The outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic techniques.The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors.In addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic approaches.The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.
基金supported by the National Basic Research Program of China (Grant No. 2013CB035501)
文摘Obstacle avoidance is quite an important issue in the field of legged robotic applications, such as rescuing and detecting in complicated environment. Most related researchers focused on the legged robot’s gait generation after ssuming that obstacles have been detected and the walking path has been given. In this paper we propose and validate a novel obstacle avoidance framework for a six-legged walking robot Hexapod-III in unknown environment. Throughout the paper we highlight three themes: (1) The terrain map modeling and the obstacle detection; (2) the obstacle avoidance path planning method; (3) motion planning for the legged robot. Concretely, a novel geometric feature grid map (GFGM) is proposed to describe the terrain. Based on the GFGM, the obstacle detection algorithm is presented. Then the concepts of virtual obstacles and safe conversion pose are introduced. Virtual obstacles restrict the robot to walk on the detection terrain. A safe path based on Bezier curves, passing through safe conversion poses, is obtained by minimizing a penalty function taking into account the path length subjected to obstacle avoidance. Thirdly, motion planning for the legged robot to walk along the generated path is discussed in detail. At last, we apply the proposed framework to the Hexapod-III robot. The experimental result shows that our methodology allows the robot to walk safely without encountering with any obstacles in unknown environment.
基金supported by the Mississippi Space Grant Consortium under NASA EPSCoR RID grant.
文摘In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring safety.Human Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor environments.This paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such settings.Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key waypoints.An integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and performance.Furthermore,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the system.Human insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes.Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of HAT.This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.
