Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

Abstract

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's "'tactile sensor." This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.

Trim State Discovery with Physical Constraints

“…An artificial potential field (APF) path-planning algorithm [19][20][21][22][23][24] is adopted for incremental TSD exploration due to its ability to incrementally (locally) build motion plans that can adapt to real-time changes in obstacle (envelope boundary) constraints. Let F A represent the attractive potential force at current trim state s t due to goal s app , and let the obstacle apply repulsive force F P on s t .…”

“…Next, we describe aircraft model preliminaries, followed by definitions of progressively sophisticated algorithms for trim state discovery. An artificial potential field path-planning [19][20][21][22][23][24] method was adopted to guide the TSD process, augmented with an edgefollowing algorithm to avoid being trapped in local minima. An initial two-dimensional (2-D) formulation over turn rate and climb rate is extended to a three-dimensional (3-D) algorithm that also includes airspeed.…”

Bernstein Polynomial-Based Method for Solving Optimal Trajectory Generation Problems

“…One common closed-loop technique that originally stemmed from maze solving algorithms is the bug algorithm. The bug algorithm, e.g., [9,10], uses local knowledge of the environment and a global goal to either follow a wall or move in a straight line towards the goal. This algorithm can be implemented on very simple devices due to typically requiring only two tactile sensors.…”

Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows

“…To improve real-time performance, some studies have proposed local navigation algorithms. Currently, the simplest local navigation algorithms are bug-based algorithms, e.g., bug1, bug2 [7], and tangent bug [8]. When the robot encounters an obstacle, the robot Appl.…”