一种点焊机器人在C空间中的智能轨迹规划算法

针对点焊机器人在C空间中的轨迹规划问题,提出一种基于蚁群算法的智能轨迹规划参数自适应蚁群算法(parameter adaptive ant colony algorithm,PAACA),以期改进蚁群算法易早熟、收敛速度慢的问题,在PAACA中构建一种复合线性适应度函数,此函数可以智能控制算法中信息素的作用强度,从而提高寻优能力。通过MATLAB进行仿真测试证明了PAACA的优越性,并将智能轨迹规划应用在工业机器人实体中,用KEBA控制器进行3D建模仿真示教和机器人本体运行,验证了C空间中的智能轨迹规划PAACA具有很强的实际应用价值。

基于多目标搜索的无人机协同轨迹智能规划

由于路径规划问题,无人机起始点与目标点的距离在5 000-5 500 m内,导致飞行耗时较长。因此,本文提出一种基于多目标搜索的无人机协同轨迹智能规划方法。该方法将双坐标系做为量化基准,构造无人机动力学模型;通过蚁群算法,规划无人机运行的初始路径;进行多目标搜索,实现无人机协同轨迹智能规划。经对比实验,设置威胁源,获取相应的飞行耗时数据。对比数据可知,该方法的飞行耗时低于原有方法,实现了性能上的突破。

Trajectory planning and yaw rate tracking control for lane changing of intelligent vehicle on curved road

The lateral control for lane changing of intelligent vehicle on curved road in automatic highway systems was studied. Based on trapezoidal acceleration profile, considering the curvature difference between starting lane and target lane, a new virtual trajectory planning method for lane changing on curved road was presented, and the calculating formulas for ideal states of vehicle in the inertial coordinate system during a lane changing maneuver were established. Applying the predetermined trajectory, the reference yaw angle and yaw rate for lane changing were generated. On the assumption that the information on yaw rate of vehicle can be measured with on-board sensors and based on the lateral dynamical model of vehicle, the yaw-rate-tracking control law was designed by applying nonsingular terminal sliding mode technology. Based on Lyapunov function method, the finite-time convergence property of the system was obtained from the phase-plane analysis. Simulation results showed that if the curvature difference between starting lane and target lane was not considered, then at the finishing time of lane changing, it was impossible to avoid the deviation of the virtual trajectory panned from the target lane, which increased with the decrease of curvature radius. With the trajectory planning method and yaw rate-tracking control law proposed in this paper and considering the curvature difference between the starting lane and target lane, the desired virtual trajectory for lane changing without deviation was obtained and the expected tracking performance was also verified by the simulation.

Intelligent computing budget allocation for on-road tra jectory planning based on candidate curves

In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution(OODE). The proposed algorithm is named IOODE with ‘I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution(DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.