Real-time Obstacle Avoidance for Fast Mobile Robots 12 (original) (raw)

High-speed obstacle avoidance for mobile robots

Proceedings IEEE International Symposium on Intelligent Control 1988, 1989

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 W i n , and enables continuous motion o f the robot without stopping in front of obstacles. Experimental results from a mobile robot running at a maximum speed of 0.78 m/sec demonstrate the power of the proposed algorithm.

Real-time obstacle avoidance for fast mobile robots

1989

The method described, named the vector field histogram (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. A VFH-controlled mobile robot maneuvers quickly and without stopping among densely cluttered obstacles. The VFH method uses a two-dimensional Cartesian histogram grid as a world model. This world model is updated continuously and

Real-time obstacle avoidance of mobile robots

2007

This paper describes the formulation of the obstacle avoidance method to work in three-dimensional workspaces. We assume a spherical and omni-directional robot. The obstacle information is given in the form of points, which is the usual form in which sensor data is given. Obstacle avoidance methods are based on a real-time control process. Sensors collect information of the environment that is processed by the method to compute a motion command, the robot executes the motion and then the process restarts again. We show results that illustrate how this method successfully performs obstacle avoidance, and how it inherits the benefits of the original method being able to overcome the typical challenging problems of other obstacle avoidance methods but extrapolated to three dimensions.

Virtual force field based obstacle avoidance and agent based intelligent mobile robot

This paper presents a modified virtual force based obstacle avoidance approach suited for laser range finder. The modified method takes advantage of the polar coordinate based data sent by the laser sensor by mapping the environment in a polar coordinate system. The method also utilizes a Gaussian function based certainty values to detect obstacle. The method successfully navigates through complex obstacles and reaches target GPS waypoints.

Real-Time Obstacle Avoidance for Manipulators and Mobile Robots

The International Journal of Robotics Research, 1986

This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to ...

VFH+: reliable obstacle avoidance for fast mobile robots

Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), 1998

This paper presents further developments of the earlier Vector Field Histogram (VFH) method for realtime mobile robot obstacle avoidance. The enhanced method, called VFH+, offers several improvements that result in smoother robot trajectories and greater reliability. VFH+ reduces some of the parameter tuning of the original VFH method by explicitly compensating for the robot width. Also added in VFH+ is a better approximation of the mobile robot trajectory, which results in higher reliability.

Obstacles Avoidance Algorithm for Mobile Robots, Using the Potential Fields Method

Universal Journal of Electrical and Electronic Engineering

In this paper-a mobile robot guidance and control has been researched in the environment full of obstacles, by using the potential fields method. The mobile robot has 4-wheels configuration, electric drive on the rear vehicles, and is directed from the front wheels (Ackerman control algorithm). A known environment has been considered, where fixed potentials were assigned to the goal and the obstacles. When the obstacles are unknownthe potential fields have to be applied, as the robot moves and detect new obstacles. A potential field's method was applied with one attraction potential assigned to the goal point and fixed rejection points assigned to the obstacles. It moves successfully within different obstacle configurations (closely spaced obstacles), and it solves the problem with a local minimum occurrence. The simulation results showed small and stable tracking errors along 2 axes.

Simple, Real-Time Obstacle Avoidance Algorithm for Mobile Robots

This paper proposes a novel, reactive algorithm for real time obstacle avoidance, compatible with low cost sonar or infrared sensors, fast enough to be implemented on embedded microcontrollers. We called this algorithm " the bubble rebound algorithm ". According to this algorithm, only the obstacles detected within an area called " sensitivity bubble " around the robot are considered. The shape and size of the sensitivity bubble are dynamically adjusted, depending on the kinematics of the robot. Upon detection of an obstacle, the robot " rebounds " in a direction having the lowest density of obstacles, and continues its motion in this direction until the goal becomes visible, or a new obstacle is encountered. The performances and drawbacks of the method are described, based on the experimental results with simulators and real robots..

Path Planning with Real Time Obstacle Avoidance

International Journal of Computer Applications, 2013

One of the most important areas of research on mobile robots is that of their moving from one point in a space to the other and that too keeping aloof from the different objects in their path i.e. real time obstacle detection. This is the basic problem of path planning through a continuous domain. For this a large number of algorithms have been proposed of which only a few are really good as far as local and global path planning are concerned as to some extent a trade of has to be made between the efficiency of the algorithm and its accuracy. In this project an integrated approach for both local as well as global path planning of a robot has been proposed. The primary algorithm that has been used for path planning is the artificial Potential field approach [1] and a* search algorithm has been used for finding the most optimum path for the robot. Obstacle detection for collision avoidance (a high level planning problem) can be effectively done by doing complex robot operations in real time and distributing the whole problem between different control levels. This project proposes the artificial potential field algorithm not only for static but also for moving obstacles using real time potential field values by creating sub-goals which eventually lead to the main goal of the most optimal complete path found by the A* search algorithm. Apart from these scan line and convex hull techniques have been used to improve the robustness of the algorithm. To some extent the shape and size of a robot has also been taken into consideration. The effectiveness of these algorithms has been verified with a series of simulations.