This paper focuses on a bionic ray-inspired amphibious robot(BRAR), which is modeled through the differential steering approach. A nonlinear model predictive control(NMPC) obstacle avoidance method is proposed for tra...This paper focuses on a bionic ray-inspired amphibious robot(BRAR), which is modeled through the differential steering approach. A nonlinear model predictive control(NMPC) obstacle avoidance method is proposed for trajectory planning. First, a BRAR's differential drive system is designed, followed by kinematic and dynamic modeling. Subsequently, an NMPC-based obstacle avoidance trajectory planning method is developed to constitute safe trajectories in complex workspaces.Further, a dead zone compensation method is proposed to improve control precision. Finally, the effectiveness of the proposed method is validated through both simulations and experiments. Simulation and experimental results demonstrate the feasibility and effectiveness of the proposed methods.展开更多
Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations dur...Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.展开更多
基金supported by the National Natural Science Foundation of China under Grants 52205019 and 62373198the Tianjin Science Fund for Distinguished Young Scholars under Grant 22JCJQJC00140+1 种基金the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515012669the Fundamental Research Funds for the Central Universities under Grant 078-63243157
文摘This paper focuses on a bionic ray-inspired amphibious robot(BRAR), which is modeled through the differential steering approach. A nonlinear model predictive control(NMPC) obstacle avoidance method is proposed for trajectory planning. First, a BRAR's differential drive system is designed, followed by kinematic and dynamic modeling. Subsequently, an NMPC-based obstacle avoidance trajectory planning method is developed to constitute safe trajectories in complex workspaces.Further, a dead zone compensation method is proposed to improve control precision. Finally, the effectiveness of the proposed method is validated through both simulations and experiments. Simulation and experimental results demonstrate the feasibility and effectiveness of the proposed methods.
文摘Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.
