Abstract
The ability to plan a multiple-target path that goes through places considered important is desirable for autonomous mobile robots that perform tasks in industrial environments. This characteristic is necessary for inspection robots that monitor the critical conditions of sectors in thermal, nuclear, and hydropower plants. This ability is also useful for applications such as service at home, victim rescue, museum guidance, land mine detection, and so forth. Multiple-target collision-free path planning is a topic that has not been very studied because of the complexity that it implies. Usually, this issue is left in second place because, commonly, it is solved by segmentation using the point-to-point strategy. Nevertheless, this approach exhibits a poor performance, in terms of path length, due to unnecessary turnings and redundant segments present in the found path. In this paper, a multiple-target method based on homotopy continuation capable to calculate a collision-free path in a single execution for complex environments is presented. This method exhibits a better performance, both in speed and efficiency, and robustness compared to the original Homotopic Path Planning Method (HPPM). Among the new schemes that improve their performance are the Double Spherical Tracking (DST), the dummy obstacle scheme, and a systematic criterion to a selection of repulsion parameter. The case studies show its effectiveness to find a solution path for office-like environments in just a few milliseconds, even if they have narrow corridors and hundreds of obstacles. Additionally, a comparison between the proposed method and sampling-based planning algorithms (SBP) with the best performance is presented. Furthermore, the results of case studies show that the proposed method exhibits a better performance than SBP algorithms for execution time, memory, and in some cases path length metrics. Finally, to validate the feasibility of the paths calculated by the proposed planner; two simulations using the pure-pursuit controlled and differential drive robot model contained in the Robotics System Toolbox of MATLAB are presented.
Highlights
Autonomous mobile robot is an entity capable of performing a wide variety of tasks that involve displacement in its workspace, such as home service, pickup and delivery assistance in offices, monitoring factories, and so forth
For a path planning process, the robot is considered as a point in the configuration space (Cspace ), which is the space generated by all feasible positions that it can reach [3,5,6]
In this box-and-whisker diagram, it can notice, that sampling-based planning algorithms (SBP) algorithms are in the order of seconds, while MTHPPM_VG and Homotopic Path Planning Method (HPPM) their time is in the order of milliseconds
Summary
Autonomous mobile robot is an entity capable of performing a wide variety of tasks that involve displacement in its workspace, such as home service, pickup and delivery assistance in offices, monitoring factories, and so forth. The robot must be capable to plan a path from an initial position to a target-position. Path planning is a geometric process in which an autonomous agent must find a collision-free path in its workspace, without considering its kinematics and dynamics restrictions [1,2,3,4]. Once a path is specified, another process is executed to calculate the motion plan using the kinodynamic properties of the robot. For a path planning process, the robot is considered as a point in the configuration space (Cspace ), which is the space generated by all feasible positions that it can reach [3,5,6]. Cspace is divided into free configuration space (Cfree ) for valid positions and obstacles space (Cobs ) for all forbidden configurations
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