Abstract
To guarantee safe motion planning, the underlying path planning algorithm must consider motion uncertainties and uncertain state information related to static, and dynamic obstacles. This paper proposes novel hybrid A* (HA*) algorithms that consider the uncertainty in the motion of a mobile robot, position uncertainty of static obstacles, and position and velocity uncertainty of dynamic obstacles. Variants of the HA* algorithm are proposed wherein a soft constraint is used in the cost function instead of chance constraints for probability guarantees. The proposed algorithm offers a tradeoff between the traveling distance and safety of paths without pruning additional nodes. Furthermore, this paper introduces a method for considering the shape of a mobile robot for probabilistic safe path planning. The proposed algorithms are compared with existing path planning algorithms and the performance of the algorithms is evaluated using the Monte Carlo simulation. Compared with the related probabilistic robust path planning algorithms, the proposed algorithms significantly improved safety without excessively increasing travel distance and computational time. The results also showed that dynamic obstacles were safely avoided, which is in contrast to the conventional HA* algorithm that has a high probability of collision. In addition, considering the shape of the robot in the proposed probabilistic approach led to safer paths overall.
Highlights
Self-driving cars face with many challenges in terms of perception, localization, and control
The above proposed algorithms are compared with existing approaches in a clustered, static environment, and an environment with a dynamic obstacle
The conventional A* [6], the HA* algorithm [2], [4], ASR HA* considering the shape of the robot using the approach of [28], the rapidly-exploring random tree (RRT) algorithm [12], the closed-loop RRT (CLRRT) algorithm [1], [36], the heuristic arrival time field-biased random tree (HeAT-RT) [14] as well as the probabilistic approaches CCRRT and online CCRRT (CCxRRT) [19], [24], which are based on CLRRT, are all implemented
Summary
Self-driving cars face with many challenges in terms of perception, localization, and control. The vehicle is required to find a feasible path from its starting pose to the desired goal pose without collision For this demand, many different path planning algorithms have been developed (e.g., [1]–[3]). Recent research has enabled the planner to re-plan [7], [8] or use any angle for the piecewise linear path [9]. These algorithms can find optimal and feasible paths to a desired goal position, none of them consider the uncertainty of the environment or robot.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
