查看更多>>摘要：Flapping-wing rotor(FWR)is an innovative bio-inspired micro aerial vehicle capable of vertical take-off and landing.This unique design combines active flapping motion and passive wing rotation around a vertical central shaft to enhance aerody-namic performance.The research on FWR,though relatively new,has contributed to 6％of core journal publications in the micro aerial vehicle field over the past two decades.This paper presents the first comprehensive review of FWR,analysing the current state of the art,key advances,challenges,and future research directions.The review highlights FWR's distinctive kinematics and aerodynamic superiority compared to traditional flapping wings,fixed wings,and rotary wings,discuss-ing recent breakthroughs in efficient,passive wing pitching and asymmetric stroke amplitude for lift enhancement.Recent experiments and remote-controlled take-off and hovering tests of single and dual-motor FWR models have showcased their effectiveness.The review compares FWR flight performance with well-developed insect-like flapping-wing micro aerial vehicles as the technology readiness level progresses from laboratory to outdoor flight testing,advancing from the initial flight of a 2.6 g prototype to the current free flight of a 60-gram model.The review also presents ongoing research in bionic flexible wing structures,flight stability and control,and transitioning between hovering and cruise flight modes for an FWR,setting the stage for potential applications.
查看更多>>摘要：Most flapping-wing aircraft wings use a single degree of freedom to generate lift and thrust by flapping up and down,while relying on the tail control surfaces to manage attitude.However,these aircraft have certain limitations,such as poor accuracy in attitude control and inadequate roll control capabilities.This paper presents a design for an active torsional mechanism at the wing's trailing edge,which enables differential variations in the pitch angle of the left and right wings during flapping.This simple mechanical form significantly enhances the aircraft's roll control capacity.The experimental verification of this mechanism was conducted in a wind tunnel using the RoboEagle flapping-wing aerial vehicle that we developed.The study investigated the effects of the control strategy on lift,thrust,and roll moment during flapping flight.Additionally,the impact of roll control on roll moment was examined under various wind speeds,flapping frequencies,angles of attack,and wing flexibility.Furthermore,several rolling maneuver flight tests were performed to evaluate the agility of RoboEagle,utilizing both the elevon control strategy and the new roll control strategy.The results demonstrated that the new roll control strat-egy effectively enhances the roll control capability,thereby improving the attitude control capabilities of the flapping-wing aircraft in complex wind field environments.This conclusion is supported by a comparison of the control time,maximum roll angle,average roll angular velocity,and other relevant parameters between the two control strategies under identical roll control input.
查看更多>>摘要：To better understand the aerodynamic reasons for highly organized movements of flying organisms,the three-flapping wing system in tandem formation was studied numerically in this paper.Different from previous relevant studies on the multiple flapping wings that are equally spaced,this study emphasizes the impact of unequal spacing between individuals on the aerodynamics of each individual wing as well as the whole system.It is found that swapping the distance between the first and second wing with the distance between the second wing and the rearmost wing does not affect the overall aerodynamic performance,but significantly changes the distribution of aerodynamic benefits across each wing.During the whole flap-ping cycle,three effects are at play.The narrow channel effect and the downwash effect can promote and weaken the wing lift,respectively,while the wake capture effect can boost the thrust.It also shows that these effects could be manipulated by changing the spacing between adjacent wings.These findings provide a novel way for flow control in tandem formation flight and are also inspiring for designing the formation flight of bionic aircraft.
查看更多>>摘要：This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment,considering the constraints of UAV dynamics and prior environmental information.Firstly,using the target probability dis-tribution map,two strategies of information fusion and information diffusion are employed to solve the problem of environ-mental information inconsistency caused by different UAVs searching different areas,thereby improving the coordination of UAV groups.Secondly,the task region is decomposed into several high-value sub-regions by using data clustering method.Based on this,a hierarchical search strategy is proposed,which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft,thereby improving the search efficiency.Third,the Elite Dung Beetle Optimization Algorithm(EDBOA)is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain,where the mountain is considered as an obstacle to be avoided.Finally,the objective function for path optimization is formulated by considering factors such as coverage within the task region,smoothness of the search path,and path length.The effectiveness and superiority of the proposed schemes are verified by the simulation.
查看更多>>摘要：Modular continuum robots possess significant versatility across various scenarios;however,conventional assembling meth-ods typically rely on linear connection between modules.This limitation can impede the robotic interaction capabilities,especially in specific engineering applications.Herein,inspired by the assembling pattern between the femur and tibia in a human knee,we proposed a multidirectional assembling strategy.This strategy encompasses linear,oblique,and orthogonal connections,allowing a two-module continuum robot to undergo in-situ reconfiguration into three distinct initial configura-tions.To anticipate the final configuration resulting from diverse assembling patterns,we employed the positional formulation finite element framework to establish a mechanical model,and the theoretical results reveal that our customizable strategy can offer an effective route for robotic interactions.We showcased diverse assembling patterns for coping with interaction requirements.The experimental results indicate that our modular continuum robot not only reconfigures its initial profile in situ but also enables on-demand regulation of the final configuration.These capabilities provide a foundation for the future development of modular continuum robots,enabling them to be adaptable to diverse environments,particularly in unstructured surroundings.
查看更多>>摘要：The goal of this paper is to develop a unified online motion generation scheme for quadruped lateral-sequence walk and trot gaits based on a linear model predictive control formulation.Specifically,the dynamics of the linear pendulum model is formulated over a predictive horizon by dimensional analysis.Through gait pattern conversion,the lateral-sequence walk and trot gaits of the quadruped can be regarded as unified biped gaits,allowing the dynamics of the linear inverted pendulum model to serve quadruped motion generation.In addition,a simple linearization of the center of pressure constraints for these quadruped gaits is developed for linear model predictive control problem.Furthermore,the motion generation problem can be solved online by quadratic programming with foothold adaptation.It is demonstrated that the proposed unified scheme can generate stable locomotion online for quadruped lateral-sequence walk and trot gaits,both in simulation and on hardware.The results show significant performance improvements compared to previous work.Moreover,the results also suggest the linearly simplified scheme has the ability to robustness against unexpected disturbances.
查看更多>>摘要：In order to strike a balance between achieving desired velocities and minimizing energy consumption,legged animals have the ability to adopt the appropriate gait pattern and seamlessly transition to another if needed.This ability makes them more versatile and efficient when traversing natural terrains,and more suitable for long treks.In the same way,it is meaningful and important for quadruped robots to master this ability.To achieve this goal,we propose an effective gait-heuristic rein-forcement learning framework in which multiple gait locomotion and smooth gait transitions automatically emerge to reach target velocities while minimizing energy consumption.We incorporate a novel trajectory generator with explicit gait infor-mation as a memory mechanism into the deep reinforcement learning framework.This allows the quadruped robot to adopt reliable and distinct gait patterns while benefiting from a warm start provided by the trajectory generator.Furthermore,we investigate the key factors contributing to the emergence of multiple gait locomotion.We tested our framework on a closed-chain quadruped robot and demonstrated that the robot can change its gait patterns,such as standing,walking,and trotting,to adopt the most energy-efficient gait at a given speed.Lastly,we deploy our learned controller to a quadruped robot and demonstrate the energy efficiency and robustness of our method.
查看更多>>摘要：Legged robots show great potential for high-dynamic motions in continuous interaction with the physical environment,yet achieving animal-like agility remains significant challenges.Legged animals usually predict and plan their next locomotion by combining high-dimensional information from proprioception and exteroception,and adjust the stiffness of the body's skeletal muscle system to adapt to the current environment.Traditional control methods have limitations in handling high-dimensional state information or complex robot motion that are difficult to plan manually,and Deep Reinforcement Learn-ing(DRL)algorithms provide new solutions to robot motioncontrol problems.Inspired by biomimetics theory,we propose a perception-driven high-dynamic jump adaptive learning algorithm by combining DRL algorithms with Virtual Model Control(VMC)method.The robot will be fully trained in simulation to explore its motion potential by learning the factors related to continuous jumping while knowing its real-time jumping height.The policy trained in simulation is successfully deployed on the bio-inspired single-legged robot testing platform without further adjustments.Experimental results show that the robot can achieve continuous and ideal vertical jumping motion through simple training
查看更多>>摘要：In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors,this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot inspired by the inchworm's movement.The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion.To enhance the robot's movement efficiency and reduce wear on the adsorption device,a gait mimicking an inchworm's movement is planned,and foot trajectory planning is performed using a quintic polynomial function.Under velocity constraints,foot trajectory optimization is achieved using an improved Particle Swarm Optimization(PSO)algorithm,determining the quintic polynomial function with the best fitness through simulation.Finally,through comparative experiments,the climbing time of the robot closely matches the simulation results,validating the trajectory planning method's accuracy.
查看更多>>摘要：This paper presents a learning-based control framework for fast(＜1.5 s)and accurate manipulation of a flexible object,i.e.,whip targeting.The framework consists of a motion planner learned or optimized by an algorithm,Online Impedance Adaptation Control(OIAC),a sim2real mechanism,and a visual feedback component.The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning(DRL),a nonlinear optimization,and a genetic algorithm in learning generalization of motion planning.It can greatly reduce average learning trials(to＜20％of others)and maximize average rewards(to＞3 times of others).Besides,motion tracking errors are greatly reduced to 13.29％and 22.36％of constant impedance control by the OIAC of the proposed framework.In addition,the trajectory similarity between simulated and physical whips is 89.09％.The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a flexible object.
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