当前位置: X-MOL 学术Front. Neurorobotics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment.
Frontiers in Neurorobotics ( IF 3.1 ) Pub Date : 2020-07-09 , DOI: 10.3389/fnbot.2020.00044
Hub Ali 1 , Dawei Gong 1 , Meng Wang 1 , Xiaolin Dai 1
Affiliation  

This approach has been derived mainly to improve quality and efficiency of global path planning for a mobile robot with unknown static obstacle avoidance features in grid-based environment. The quality of the global path in terms of smoothness, path consistency and safety can affect the autonomous behavior of a robot. In this paper, the efficiency of Ant Colony Optimization (ACO) algorithm has improved with additional assistance of A* Multi-Directional algorithm. In the first part, A* Multi-directional algorithm starts to search in map and stores the best nodes area between start and destination with optimal heuristic value and that area of nodes has been chosen for path search by ACO to avoid blind search at initial iterations. The path obtained in grid-based environment consist of points in Cartesian coordinates connected through line segments with sharp bends. Therefore, Markov Decision Process (MDP) trajectory evaluation model is introduced with a novel reward policy to filter and reduce the sharpness in global path generated in grid environment. With arc-length parameterization, a curvilinear smooth route has been generated among filtered waypoints and produces consistency and smoothness in the global path. To achieve a comfort drive and safety for robot, lateral and longitudinal control has been utilized to form a set of optimal trajectories along the reference route, as well as, minimizing total cost. The total cost includes curvature, lateral and longitudinal coordinates constraints. Additionally, for collision detection, at every step the set of optimal local trajectories have been checked for any unexpected obstacle. The results have been verified through simulations in MATLAB compared with previous global path planning algorithms to differentiate the efficiency and quality of derived approach in different constraint environments.

中文翻译:

利用改进的蚁群算法和MDP的移动机器人路径规划,在基于网格的环境中产生平滑的轨迹。

该方法主要用于提高基于网格环境中具有未知静态避障功能的移动机器人的全局路径规划的质量和效率。就平滑度,路径一致性和安全性而言,全局路径的质量会影响机器人的自主行为。本文在A *多方向算法的辅助下,提高了蚁群优化(ACO)算法的效率。在第一部分中,A *多向算法开始在地图中搜索,并以最佳试探值存储起点和终点之间的最佳节点区域,并且ACO选择了节点区域进行路径搜索,以避免在初始迭代时盲目搜索。在基于网格的环境中获得的路径由直角坐标中的点组成,这些点通过具有急剧弯曲的线段相连。因此,引入了具有新颖奖励策略的马尔可夫决策过程(MDP)轨迹评估模型,以过滤并降低网格环境中生成的全局路径的清晰度。通过弧长参数化,已过滤的航路点之间生成了一条曲线平滑路线,并在全局路径中产生了一致性和平滑度。为了实现机器人的舒适驾驶和安全性,已经利用横向和纵向控制沿参考路径形成了一组最佳轨迹,并将总成本降至最低。总成本包括曲率,横向和纵向坐标约束。此外,对于碰撞检测,在每一步中,都会检查最佳局部轨迹集是否存在任何意外障碍。与以前的全局路径规划算法相比,通过MATLAB中的仿真已验证了结果,以区分不同约束环境中派生方法的效率和质量。
更新日期:2020-07-09
down
wechat
bug