A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

Abstract

Purpose Wheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments. Design/methodology/approach Given a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR. Findings The authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments. Originality/value In this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

Event-Triggered Predictive Control Algorithm for Multi-AUV Formation Modeling

“…MPC improved the safe distance between the robot and obstacles. In simulation experiments, this method helped the robot to avoid obstacles successfully [10]. MPC can also be used in path optimization for AUVs.…”

“…The real-time control effect of the method is poor [9] Accurately estimating errors in open-loop problems The application scope of the method is limited [10] Improved safety distance between robots and obstacles…”

Vision SLAM algorithm for wheeled robots integrating multiple sensors

“…The results indicated that this method met the application requirements of wheeled robots in unstructured environments. Additionally, the study optimized the model predictive controller by incorporating the distance between obstacles and robots, ensuring a safe distance [ 9 ]. A. Alhalabi et al introduced a drift compensation technology for a wheeled robot platform based on multi-sensor fusion.…”

“…Subsequently, the IMU acceleration measurement values are transformed, gravity vector is removed after transforming to the PLOS ONE world coordinate system, and the pose of the wheeled robot can be calculated. In the encoder preprocessing, the velocity and slip factor φ k of the wheeled robot are included, as expressed in Eq (9).…”

Along Similar Lines: Local Obstacle Avoidance for Long-Term Autonomous Path Following

“…We are not the first to try to combine sample-based planners with MPC. Zhou et al [28] run a variation of the sample-based planner Informed RRT* [29] to generate shortest-distance paths avoiding obstacles, and opt to track this path directly with MPC under the assumption that it will be collision free. Similar to this work, Al-Moadhen et al [30] use Batch Informed Trees (BIT*) as the sample-based motion planner with the addition of a B-Spline smoothing post-processing step to generate kinematically feasible paths to be tracked with MPC.…”