Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

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.

The boundary node method for multi‐robot multi‐goal path planning problems

“…Multi-goal planning has been widely used in many robotics applications, such as surveillance, manufacturing, autonomous inspection, and assembly. Moreover, the multi-goal path-planning is important for autonomous mobile robots that perform tasks in industrial environments (Diaz-Arango et al, 2020). The existing methods solve the MTP in a two-step process (Englot & Hover, 2011): First, constructing a path by optimizing the sequence of goal points in a robot working environment without considering obstacles; then, constructing a path (tour) when obstacles are introduced into the environment, in which the robot must reach all sequenced goal points and it visits each goal once.…”

“…The authors in Pandey et al, 2020;Patle et al, 2019 provide the various soft computing techniques applied by different researchers for mobile robot navigation. The main characteristics of the most used collision-free path planners are reported in Diaz-Arango et al, 2020. The MTP has been previously investigated, and proposed approaches are motivated by many practical problems, i.e. planning for a robotic arm (Saha et al, 2006;Spitz & Requicha, 2000), hexapod walking robot (Vaněk et al, 2014), Industrial manipulators (Zacharia et al, 2013), spot welding task (Saha et al, 2003;Wurll & Henrich, 2001), inspection planning (Faigl et al, 2011), search and rescue mission (Kulich et al, 2005), planetary exploration (Hongyun et al, 2013), inspection and surveillance applications (Englot & Hover, 2011), coordinate measuring machines (CMMs) (Spitz & Requicha, 2000), finding cracks in large structures (Danner & Kavraki, 2000), office-like environments to perform common tasks of picking up, and delivering things such as mail, goods, trash recycled paper, etc (Hernandez et al, 2017).…”

“…(b) Results of BNM&PEM to generate the collision-free path for linking the goal points, plotted in blue solid line In many applications, the computational cost of constructing a collision-free path over every goal-to-goal pairings in obstacle-filled environments with many goals is high (Englot & Hover, 2011;Faigl, 2016;Saha et al, 2006), because of both the dimensionality of the configuration space and the geometric complexity of the obstacles and the robot (Saha et al, 2006). In robotic planning, multiple-goal collision-free path planning has not been studied extensively in previous literature because of the complexity that it implies (Diaz-Arango et al, 2020). Depending on the problem complexity, finding the shortest collision-free path between goal points as well as optimizing the sequence of goal points will be very difficult (Wurll et al, 1999), and computationally hard problems (Saha et al, 2003).…”

Sensors and Sensing for Intelligent Vehicles

“… Vehicle localization (3): [ 33 , 34 , 35 ]. Path planning (1): [ 36 ] Smart regenerative braking systems for electric vehicles (2): [ 37 , 38 ]. Physical intelligence in sensors and sensing (3): [ 39 , 40 , 41 ] Driver assistance systems and automatic vehicle operation (3) Advanced driver assistance systems (1): [ 42 ].…”

“…Diaz-Arango et al propose in [ 36 ] a multiple-target collision-free path planning based on homotopy continuation capable to calculate a collision-free path in a single execution for complex environments. The method exhibits better performance, both in speed and efficiency, and robustness compared to the original Homotopic Path Planning Method (HPPM).…”