Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment

Abstract

The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with the repulsive force from the obstacle, such that the robot cannot escape from this situation and reach the target. Moreover, the moving object detection and avoidance is still a challenging problem with the current artificial potential field method. In this paper, we present an improved version of the artificial potential field method, which uses a dynamic window approach to solve the local minimum problem and define a danger index in the speed field for moving object avoidance. The new danger index considers not only the relative distance between the robot and the obstacle, but also the relative velocity according to the motion of the moving objects. In this way, the robot can find an optimized path to avoid local minimum and moving obstacles, which is proved by our experimental results.

A Dynamic Motion Analysis of a Six-Wheel Ground Vehicle for Emergency Intervention Actions

“…Planning implies optimization procedures of the time (and velocities) to select the geometrical paths in real-time to avoid obstacles [ 18 , 19 , 20 , 21 ]. Since every obstacle creates a risk level for the UGV, introducing proportional integrative derivative (PID) controllers and fuzzy logic methods, which classify the objects around the vehicle based on their level of risk, allows generating some predictions regarding the capacity to avoid fixed or moving obstacles (the velocity obstacle (VO) approach), which means that, virtually, a space that defines the respective object should be generated [ 22 , 23 , 24 ]. The mission of a UGV agrees with path planning through adopting some algorithms that can generate optimal behavior and, each time discrepancies from the initial map appear, these data are updated.…”

Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

“…This experiment is used to seek the appropriate pheromone parameter a, heuristic information parameter b, pheromone evaporation parameter r to make the algorithm obtain high success rate, and fewer number of iterations. The obstacle environment is shown in Figure 5, the number of ants is 20, the number of iterations is 100, the start point is (5,18), and the end point is (19,0). The initial parameters a ¼ 1.0, b ¼ 18, and r ¼ 0.5 are specified.…”

“…In addition, the success rate of the shortest path is 96%. point is (0,0) and the end point is (19,19), the experiment runs 100 times. The planned shortest paths are shown with the straight line in Figure 7.…”

“…The starting point of robot R 1 is (19,10) and the ending point is (1,10). The starting point of robot R 2 is (1, 10) and the ending point is (19,10). The priority of R 1 is higher than that of R 2 .…”

Special Issue on Mobile Robots Navigation

“…Sun et al [7] set their sights on the path planning problem, more concisely, on Artificial Potential Field (APF) approaches. They are an efficient alternative for motion planning in mobile robotics, but they are often limited by the presence of local minima in which the robot may get trapped.…”