This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
The broad-leaved Korean pine(Pinus koraiensis) forest is one of the most biodiverse zonal communities in the North Temperate Zone and an important habitat for many endangered species.Broad-leaved Korean pine forests(B...The broad-leaved Korean pine(Pinus koraiensis) forest is one of the most biodiverse zonal communities in the North Temperate Zone and an important habitat for many endangered species.Broad-leaved Korean pine forests(BKPFs) are shrinking quickly due to deforestation and rapid urbanization. Thus, scientific protection strategies are urgently needed to change this status. Changbai Mountains contains one of the largest BKPFs and is considered a priority biodiversity conservation area in China. Guided by systematic conservation planning(SCP) methods and procedures, we chose representative species and communities in BKPFs ecosystem as priority conservation objects, and set quantitative conservation target, which is in the light of the biodiversity characteristic of BKPFs. The watershed area is used as planning unit. We used CPlan software to calculate the irreplaceability(Ir)value of each planning unit and the contribution value(Ti) of each conservation object to(1) assess the conservation efficiency;(2) identify the conservation gap of the existing conservation network. Then wecalculated a human disturbance index(HDI) for planning units in the conservation gaps and combine this with the Ir value to design three conservation scenarios to optimize the conservation network.Results show that planning units with high conservation value 14.16% of the total area, with3084.36 km2 were covered by the existing conservation network. 79.28% of planning units with high conservation value have not been protected which were concentrated mainly in the eight gap areas.Only 25.3% of protection objects achieved their conservation target with the existing conservation network. Conservation efficiency is low. Three conservation scenarios are constituted, each prioritizing a different aim:(1) ecological value;(2)species rescue; and(3) economical avoidance. The three conservation schemes potentially enable 93%,88% and 51% of conservation objects, respectively, to achieve identified conservation targets, thereby improving conservation efficiency significantly.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent systems.First,in order to improve the efficiency and safety of quadruped robots navigating in com...This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent systems.First,in order to improve the efficiency and safety of quadruped robots navigating in complex environments,this paper proposes a new planning method that combines the dynamic model of quadruped robots and a gradient-optimized obstacle avoidance strategy without Euclidean Signed Distance Field.The framework is suitable for both static and slow dynamic obstacle environments,aiming to achieve multiple goals of obstacle avoidance,minimizing energy consumption,reducing impact,satisfying dynamic constraints,and ensuring trajectory smoothness.This approach differs in that it reduces energy consumption throughout the movement from a new perspective.Meanwhile,this method effectively reduces the impact of the ground on the robot,thus mitigating the damage to its structure.Second,we combine the dynamic control barrier function and the virtual leader-follower model to achieve efficient and safe formation control through model predictive control.Finally,the proposed algorithm is validated through both simulations and real-world scenarios testing.展开更多
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
基金supported by the 12th fiveyear National Science and Technology plan of China (2012BAC01B03)
文摘The broad-leaved Korean pine(Pinus koraiensis) forest is one of the most biodiverse zonal communities in the North Temperate Zone and an important habitat for many endangered species.Broad-leaved Korean pine forests(BKPFs) are shrinking quickly due to deforestation and rapid urbanization. Thus, scientific protection strategies are urgently needed to change this status. Changbai Mountains contains one of the largest BKPFs and is considered a priority biodiversity conservation area in China. Guided by systematic conservation planning(SCP) methods and procedures, we chose representative species and communities in BKPFs ecosystem as priority conservation objects, and set quantitative conservation target, which is in the light of the biodiversity characteristic of BKPFs. The watershed area is used as planning unit. We used CPlan software to calculate the irreplaceability(Ir)value of each planning unit and the contribution value(Ti) of each conservation object to(1) assess the conservation efficiency;(2) identify the conservation gap of the existing conservation network. Then wecalculated a human disturbance index(HDI) for planning units in the conservation gaps and combine this with the Ir value to design three conservation scenarios to optimize the conservation network.Results show that planning units with high conservation value 14.16% of the total area, with3084.36 km2 were covered by the existing conservation network. 79.28% of planning units with high conservation value have not been protected which were concentrated mainly in the eight gap areas.Only 25.3% of protection objects achieved their conservation target with the existing conservation network. Conservation efficiency is low. Three conservation scenarios are constituted, each prioritizing a different aim:(1) ecological value;(2)species rescue; and(3) economical avoidance. The three conservation schemes potentially enable 93%,88% and 51% of conservation objects, respectively, to achieve identified conservation targets, thereby improving conservation efficiency significantly.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金supported in part by Shanghai Rising-Star Program under Grant No.22QA1409400in part by National Natural Science Foundation of China under Grant Nos.62473287 and 62088101in part by Shanghai Municipal Science and Technology Major Project under Grant No.2021SHZDZX0100.
文摘This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent systems.First,in order to improve the efficiency and safety of quadruped robots navigating in complex environments,this paper proposes a new planning method that combines the dynamic model of quadruped robots and a gradient-optimized obstacle avoidance strategy without Euclidean Signed Distance Field.The framework is suitable for both static and slow dynamic obstacle environments,aiming to achieve multiple goals of obstacle avoidance,minimizing energy consumption,reducing impact,satisfying dynamic constraints,and ensuring trajectory smoothness.This approach differs in that it reduces energy consumption throughout the movement from a new perspective.Meanwhile,this method effectively reduces the impact of the ground on the robot,thus mitigating the damage to its structure.Second,we combine the dynamic control barrier function and the virtual leader-follower model to achieve efficient and safe formation control through model predictive control.Finally,the proposed algorithm is validated through both simulations and real-world scenarios testing.
