Tool Positioning Algorithm Based on Smooth Tool Paths for 5-axis Machining of Sculptured Surfaces

The current research of the 5-axis tool positioning algorithm mainly focuses on searching the local optimal tool position without gouging and interference at a cutter contact（CC） point,while not considering the smoothness and continuity of a whole tool path.When the surface curvature varies significantly,a local abrupt change of tool paths will happen.The abrupt change has a great influence on surface machining quality.In order to keep generated tool paths smooth and continuous,a five-axis tool positioning algorithm based on smooth tool paths is presented.Firstly,the inclination angle,the tilt angle and offset distance of the tool at a CC point are used as design variables,and the machining strip width is used as an objective function,an optimization model of a local tool positioning algorithm is thus established.Then,a vector equation of tool path is derived by using the above optimization model.By analyzing the equation,the main factors affecting the tool path quality are obtained.Finally,a new tool position optimization model is established,and the detailed process of tool position optimization is also given.An experiment is conducted to machine an aircraft turbine blade by using the proposed algorithm on a 5-axis blade grinding machine,and the machined blade surface is measured with a coordinate measuring machine（CMM）.Experimental and measured results show that the proposed algorithm can ensure tool paths are smooth and continuous,improve the tool path quality,avoid the local abrupt change of tool paths,and enhance machining quality and machining efficiency of sculptured surfaces.

Path Planning Based on the Improved RRT^(∗) Algorithm for the Mining Truck

Planning a reasonable driving path for trucks in mining areas is a key point to improve mining efficiency.In this paper,a path planning method based on Rapidly-exploring Random Tree Star(RRT∗)is proposed,and several optimizations are carried out in the algorithm.Firstly,the selection process of growth target points is optimized.Secondly,the process of selecting the parent node is optimized and a Dubins curve is used to constraint it.Then,the expansion process from tree node to random point is optimized by the gravitational repulsion field method and dynamic step method.In the obstacle detection process,Dubins curve constraint is used,and the bidirectional RRT∗algorithm is adopted to speed up the iteration of the algorithm.After that,the obtained paths are smoothed by using the greedy algorithm and cubic B-spline interpolation.In addition,to verify the superiority and correctness of the algorithm,an unmanned mining vehicle kinematic model in the form of frontwheel steering is developed based on the Ackermann steering principle and simulated for CoppeliaSim.In the simulation,the Stanley algorithm is used for path tracking and Reeds-Shepp curve to adjust the final parking attitude of the truck.Finally,the experimental comparison shows that the improved bidirectional RRT∗algorithm performs well in the simulation experiment,and outperforms the common RRT∗algorithm in various aspects.

基于改进型AAF-RRT算法的AUV双层路径规划器

As autonomous underwater vehicles(AUVs)merely adopt the inductive obstacle avoidance mechanism to avoid collisions with underwater obstacles,path planners for underwater robots should consider the poor search efficiency and inadequate collision-avoidance ability.To overcome these problems,a specific two-player path planner based on an improved algorithm is designed.First,by combing the artificial attractive field(AAF)of artificial potential field(APF)approach with the random rapidly exploring tree(RRT)algorithm,an improved AAF-RRT algorithm with a changing attractive force proportional to the Euler distance between the point to be extended and the goal point is proposed.Second,a twolayer path planner is designed with path smoothing,which combines global planning and local planning.Finally,as verified by the simulations,the improved AAF-RRT algorithm has the strongest searching ability and the ability to cross the narrow passage among the studied three algorithms,which are the basic RRT algorithm,the common AAF-RRT algorithm,and the improved AAF-RRT algorithm.Moreover,the two-layer path planner can plan a global and optimal path for AUVs if a sudden obstacle is added to the simulation environment.

A multiple heterogeneous UAVs reconnaissance mission planning and re-planning algorithm

Reconnaissance mission planning of multiple unmanned aerial vehicles(UAVs)under an adversarial environment is a discrete combinatorial optimization problem which is proved to be a non-deterministic polynomial(NP)-complete problem.The purpose of this study is to research intelligent multiUAVs reconnaissance mission planning and online re-planning algorithm under various constraints in mission areas.For numerous targets scattered in the wide area,a reconnaissance mission planning and re-planning system is established,which includes five modules,including intelligence analysis,sub-mission area division,mission sequence planning,path smoothing,and online re-planning.The intelligence analysis module depicts the attribute of targets and the heterogeneous characteristic of UAVs and computes the number of sub-mission areas on consideration of voyage distance constraints.In the sub-mission area division module,an improved K-means clustering algorithm is designed to divide the reconnaissance mission area into several sub-mission areas,and each sub-mission is detected by the UAV loaded with various detective sensors.To control reconnaissance cost,the sampling and iteration algorithms are proposed in the mission sequence planning module,which are utilized to solve the optimal or approximately optimal reconnaissance sequence.In the path smoothing module,the Dubins curve is applied to smooth the flight path,which assure the availability of the planned path.Furthermore,an online re-planning algorithm is designed for the uncertain factor that the UAV is damaged.Finally,reconnaissance planning and re-planning experiment results show that the algorithm proposed in this paper are effective and the algorithms designed for sequence planning have a great advantage in solving efficiency and optimality.

Global path planning of unmanned vehicle based on fusion of A*algorithm and Voronoi field

Purpose–Since many global path planning algorithms cannot achieve the planned path with both safety and economy,this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles.Design/methodology/approach–First,combining satellite image and the Voronoi field algorithm(VFA)generates rasterized environmental information and establishes navigation area boundary.Second,establishing a hazard function associated with navigation area boundary improves the evaluation function of the A*algorithm and uses the improved A*algorithm for global path planning.Finally,to reduce the number of redundant nodes in the planned path and smooth the path,node optimization and gradient descent method(GDM)are used.Then,a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained.Findings–The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries.The node reduction rate is between 33.52%and 73.15%,and the smoothness meets the navigation requirements.This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’autonomous obstacle avoidance decision-making.Originality/value–This study establishes navigation area boundary for the environment based on the VFA and uses the improved Aalgorithm to generate a navigation path that takes into account both safety and economy.This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method.The proposed global path planning method solves the requirements of path safety and smoothness.

Multi-UAV Route Re-Generation Method Based on Trajectory Data

A large quantity of unmanned aerial vehicle(UAV)trajectory data related to air traffic information has important value in engineering fields.However,the cost of data and trajectory processing limits the applications,and as the number of UAVs increases rapidly,future UAVs'path data will be very large.Therefore,this paper designs a multi-UAV route re-generation method based on trajectory data,which can realize the UAVs'path data compression,de-aggregation,and regeneration tasks.Based on the trajectory data,the three-dimensional Douglas-Peucker algorithm is used to compress the trajectory data to reduce the storage space.The improved B-spline path smoothing algorithm based on the reversing control point is used to depolymerize and smooth the path.Simulation experiments show that the above multi-UAV route re-generation algorithm can obtain a more optimized path while maintaining the important characteristics of the original path.