Local path planning for autonomous mobile robots by integrating modified dynamic‐window approach and improved follow the gap method

Abstract

Mobile robots need to automatically generate a safe, goal‐oriented, and fast collision‐free trajectory in real‐time during the movement in an indoor/outdoor environment. A planned trajectory must be adaptable and drivable with environmental changes where various static and moving obstacles may be present. The ultimate goal of a robot is to reach the destination without hitting any obstacles, therefore, a reactive local path planning algorithm is needed. In this paper, a novel local algorithm is proposed by integrating dynamic window approach (DWA) and improved follow the gap method (IFGM) to generate a collision‐free trajectory for a mobile robot which is capable to avoid any moving obstacles presenting in the surrounding environment. In this proposed method, first, a safety distance is maintained according to the relative position of obstacles and the robot. Moreover, find a feasible gap to direct the robot toward the desired goal. Besides, the heading angle is calculated to change the direction of the robot for avoiding collision with nearby obstacles. After that, calculate the appropriate velocity for the robot. Finally, a robust, safe, and goal‐directed trajectory is generated which does not suffer from global convergence and local minima problems. The performance and effectiveness of this proposed algorithm are evaluated by experimental results.

Parallel Sensor-Space Lattice Planner for Real-Time Obstacle Avoidance

“…Although considered a legacy approach, DWA is still a very popular method. Recent works using DWA include [27], which proposes a new method for dynamic obstacles avoidance that looks for free gaps in between the obstacles to compute a trajectory and applies the DWA to safely follow such a trajectory assuming that the obstacles can move. More related to our work, assuming that a global vector field gives the desired velocity of the vehicle, the dynamic window was used to detect if the field can be followed safely [28].…”

Efficient Motion Planning With Minimax Objectives: Synergizing Interval Prediction and Tree-Based Planning

“…Nevertheless, many practical tasks do not simply involve stability issues but also pay attention to safety, particularly those involving obstacle avoidance. Such traditional obstacle avoidance techniques as the Dynamic Window Approach (DWA) [23], [24], [25] and the Timed Elastic Band (TEB) [26], [27] are primarily designed for static obstacle avoidance. Consequently, maneuvering around dynamic obstacles poses a unique challenge, requiring the necessary information about obstacles through object detection solutions to adapt effectively.…”

Efficient obstacle avoidance and path planning for mobile agent through complicated environments

“…Xiao [11] et al designed an improved DWA algorithm and combined the advantages of the positive feedback mechanism of the ant colony algorithm to improve path smoothing for multi-objective path planning. Hossain [12] et al guided the robot toward the desired goal by finding an appropriate distance and designed a hybrid algorithm of DWA and the following gap method to generate collision-free running trajectories for mobile agents. Zou [13] et al extracted global path nodes as sub-target points according to the mayfly optimization algorithm and combined the DWA algorithm for path planning, which effectively improved real-time obstacle avoidance.…”