Introduction
Real-time obstacle avoidance is one of the key issues to successful applications of mobile robot systems. All mobile robots feature some kind of collision avoidance, ranging from primitive algorithms that detect an obstacle and stop the robot short of it in order to avoid a collision, through sophisticated algorithms, that enable the robot to detour obstacles. The latter algorithms are much more complex, since they involve not only the detection of an obstacle, but also some kind of quantitative measurements concerning the obstacle's dimensions. Once these have been determined, the obstacle avoidance algorithm needs to steer the robot around the obstacle and resume motion toward the original target. Autonomous navigation represents a higher level of performance, since it applies obstacle avoidance simultaneously with the robot steering toward a given target.
A more general and commonly employed method for obstacle avoidance is based on edge detection. In this method, the algorithm tries to determine the position of the vertical edges of the obstacle and consequently attempts to steer the robot around either edge. The line connecting the two edges is considered to represent one of the obstacle's boundaries. This method was used in our own previous research,as well as in several other research projects.A disadvantage with obstacle avoidance based on edge detecting is the need of the robot to stop in front of an obstacle in order to allow for a more accurate measurement.
Speed Control
The intuitive way to control the speed of a mobile robot in the VFF environment is to set it proportional to the magnitude of the sum of all forces.
Thus, if the path was clear, the robot would be subjected only to the target force and would move toward the target, at its maximum speed. Repulsive forces from obstacles, naturally opposed to the direction of Ft (with disregard to the damping effect discussed above), would reduce the magnitude of the resultant R, thereby effectively reducing the robot"s speed in the presence of obstacles.
Recovery From "Local Minimum Traps"
One problem inherent to the basic VFF method is the possibility for the robot to get "Trapped." This situation may occur when the robot runs into a dead end (e.g., inside a U shaped obstacle). Traps can be created by a variety of different obstacle configurations, and different types of traps can be distinguished. This section presents a comprehensive set of heuristic rules to recover from different trap conditions. Chattergy [10] presented some heuristic local path planning solutions for various obstacle configurations (and trap conditions), based on distance measurements to the obstacle.
Trap-state Detection
In an ideal, non-inertial system, simply simply monitoring the speed of the robot may discover trap-states. If caught in a trap, the robot’s speed will become zero as the robot converges to the equilibrium position with R = 0. In a dynamic system, however, the robot overshoots the equilibrium position and will either oscillate or run in a closed loop, as shown in Fig. 3a for an actual run. Therefore, it is impractical to monitor the magnitude of the resultant force |R| for trap-state detection.
Abstract
A new real-time obstacle avoidance approach for mobile robots has been developed and implemented. This approach permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advancing toward the target. The novelty of this approach, entitled the Virtual Force Field, lies in the integration of two known concepts: Certainty Grids for obstacle representation, and Potential Fields for navigation. This combination is especially suitable for the accommodation of inaccurate sensor data (such as produced by ultrasonic sensors) as well as for sensor fusion, and enables continuous motion of the robot without stopping in front of obstacles.
Conclusions
A comprehensive obstacle avoidance approach for fast-running mobile robots, denoted as the VFF method, has been developed and tested on our experimental mobile robot CARMEL. The VFF method is based on the following principles:
1. A Certainty Grid for representation of (inaccurate) sensory data about obstacles provides a robust real-time world model.
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