Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots

Abstract

In this work, we present a complete hybrid navigation system for a two-wheel differential drive mobile robot that includes static-environment- global-path planning and dynamic environment obstacle-avoidance tasks. By the given map, we propose a multi-agent A-heuristic algorithm for finding the optimal obstacle-free path. The result is less time-consuming and involves fewer changes in path length when dealing with multiple agents than the ordinary A-heuristic algorithm. The obtained path was smoothed based on curvature-continuous piecewise cubic Bézier curve (C2 PCBC) before being used as a trajectory by the robot. In the second task of the robot, we supposed any unforeseen obstacles were recognized and their moving frames were estimated by the sensors when the robot tracked on the trajectory. In order to adapt to the dynamic environment with the presence of constant velocity obstacles, a weighted-sum model (WSM) was employed. The 2D LiDAR data, the robot’s frame and the detected moving obstacle’s frame were collected and fed to the WSM during the movement of the robot. Through this information, the WSM chose a temporary target and a C2 PCBC-based subtrajectory was generated that led the robot to avoid the presented obstacle. Experimentally, the proposed model responded well in existing feasible solution cases with fine-tuned model parameters. We further provide the re-path algorithm that helped the robot track on the initial trajectory. The experimental results show the real-time performance of the system applied in our robot.

Global path guided vehicle obstacle avoidance path planning with artificial potential field method

“…For future work arrangements, future research should focus on the kinematics and dynamic constraints of the vehicle [36][37][38] and the contribution of deep learning to dynamic obstacle avoidance [39][40][41]. Because the car is regarded as a particle simulation in this paper, the speed is unchanged, and the rotation angle is not limited, which is not beneficial to trajectory tracking in the later stage.…”

Optimization of Motion Planning and Control for Automatic Machines, Robots and Multibody Systems

“…The application of obstacle avoidance is also investigated for the case of mobile robots in [12], to meet the ever-growing use of such systems. This paper proposes some real-time algorithms for the navigation of an autonomous service robot in an indoor environment in the presence of moving obstacles.…”

Path planning using A* algorithm and DWA

“…Consequently, the fundamental technology that allows a robot to perform autonomous navigation is path planning [2] . It can navigate an obstacle-filled environment and find the best route from the starting point to the destination while avoiding all obstacles [3][4] . Furthermore, path planning is separated into two categories: local planning and global planning [5] .…”