Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

Abstract

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.

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

“…As shown in FIGURE 4, MDP includes the environment, Agent, Action, Reward and State [59] . The Agent represents the mobile robot; The Environment refers to the map information of the task.The State is the state space of the mobile robot.…”

Machining Path Optimization of 3C Locking Robots Using Adaptive Ant Colony Optimization

“…Holes. In order to solve the multiregion floating hole motion smoothness-machining path optimization model, this paper divides the floating hole region into grid division and takes each grid intersection as the target point for each optimization, so as to finally find the shortest path that meets the accuracy requirements [24][25][26], as shown in Figure 3. e size of the grid is related to the size of the screw hole.…”

Research on Multi-AGVs Path Planning and Coordination Mechanism

“…In the original ACO algorithm, the ants in the search process may fall into the local optimum, there are numerous researches on the ACO algorithm and other algorithms about how to jump out of the local optimum. Hub et al [13] propose the maximum and minimum ant strategy, the pheromone concentration on the map is limited between the maximum pheromone concentration and the minimum pheromone concentration. Wang et al [19] propose a new dual-operator and dual-population ant colony optimization (DODPACO) algorithm, the load operators are adopted to limit the accumulation of the pheromone on the path in the early search path, and adjust the pheromone concentration on the path to avoid falling into the local optimum.…”

“…The ACO algorithm is a heuristic optimization method based on positive feedback mechanism, in which the pheromone concentration on the path has a heuristic effect on searching for the next node [12]. In the initial stage when the ACO algorithm is used to search the path from the starting node to the target node, the pheromone is evenly distributed on the map, the search process is blind and many nodes are traversed, so the search efficiency is low [13]. With the increase of the number of iterations, the probability of subsequent ants choosing the optimal path increases as the concentration of pheromone on the optimal path increases, thereby the ACO algorithm obtain the optimal path through positive feedback based on pheromone.…”