A Global Trajectory Planning Framework Based on Minimizing the Risk Index

Abstract

At present, autonomous mobile robots are widely used in industrial and commercial fields. However, although the global path searched by existing mobile robot path planning methods has no collision with obstacles, there is a problem in that the path is close to obstacles and is not smooth, and there is a collision safety risk when the robot is actually moving. To solve the above problems, this paper proposes a global path planning method based on minimizing the risk index. Firstly, the distance calculation method of the heuristic function of the traditional graph search algorithm is improved to reduce the number of nodes in the search space. Additionally, by selecting the appropriate search neighborhood, the search efficiency and path smoothness of the algorithm are improved. Thirdly, to increase the distance between the original search path and obstacles, the risk index path search strategy is proposed. Finally, the minimized snap trajectory smoothing method with a safe corridor is used to smooth the original waypoint. Both simulation and real robot experimental results show that the minimum distance between waypoints and obstacles is increased by 43.72% on average, and the number of trajectory inflection points are reduced by 75.12% on average after optimization. As such, the proposed method can fully guarantee safety and generate smooth mobile robot paths in global trajectory planning tasks.

Generation of Achievable Three-Dimensional Trajectories for Autonomous Wheeled Vehicles via Tracking Differentiators

“…Namely, the time functions χ 11 (t), χ 12 (t) are either continuous but non-smooth or piecewise continuous. On the continuity intervals, the derivatives of these functions do not exceed the allowed values (16). The number of special points (junctions or points of the discontinuity of the first kind) is bounded.…”

“…The result is the solution of discrete optimization problems in the form of a set of discrete waypoints. As mentioned, designing a smooth and achievable trajectory on their basis requires additional tools [16].…”

“…To generate a trajectory subject to the design constraints of the mobile robot, additional algorithmization or increasing the degree of the polynomial is required. For example, a polynomial curve of degree seven is used to constrain the first derivative in [16]. In addition, a smooth join of the separate fragments of the trajectory needs to be ensured.…”

A Hierarchical Lane-Changing Trajectory Planning Method Based on the Least Action Principle

“…Graph-based methods require the establishment of a grid-based environmental map of the driving space. Nodes are expanded based on a cost strategy to search for feasible paths within the grid-based environment map [7,8]. Representative graph-based path planning methods include Dijkstra, A*, D*, etc.…”

Multimodal Global Trajectory Planner for Autonomous Underwater Vehicles

“…In references [17][18][19], A* was used with graph-based methods for path planning of AUVs and autonomous surface vehicles (ASVs). A* trajectory planning approach based on minimising risk index was proposed in [20]. Unfortunately, despite the ability to plan a path close to the optimal one, the computation time is very large, especially for large-sized maps.…”