Car collision avoidance with velocity obstacle approach

The obstacle avoidance maneuver is required for an autonomous vehicle. It is essential to define the system's performance by evaluating the minimum reaction times of the vehicle and analyzing the probability of success of the avoiding operation. This paper presents a collision avoidance algorithm based on the velocity bstacle approach that guarantees collision-free maneuvers. The vehicle is controlled by an optimal feedback control named FLOP, designed to produce the best performance in terms of safety and minimum kinetic collision energy. Dimensionless accident evaluation parameters are proposed to compare different crash scenarios

Performance evaluation of Double Action Q-learning in moving obstacle avoidance problem

This paper describes the performance evaluation of Double-Action Q-learning in solving the moving obstacle avoidance problem. The evaluation is focused on two aspects: 1) obstacle avoidance, and 2) goal seeking; where four parameters are considered, namely, sum of rewards, no. of collisions, steps per episode, and obstacle density. Comparison is made between the new method and the traditional Q-learning method. Preliminary results show that the new method has the sum of rewards (negative) 29.4% and 93.6% less than that of the traditional method in an environment of 10 obstacles and 50 obstacles respectively. The mean no. of steps used in one episode is up to 26.0% lower than that of the traditional method. The new method also fares better as the number of obstacles increases. © 2005 IEEE.published_or_final_versio

Reactive Vision-Based Navigation Controller for Autonomous Mobile Agents

Initial results of an ongoing research in the field of reactive mobile autonomy are presented. The aim is to create a reactive obstacle avoidance method for mobile agent operating in dynamic, unstructured, and unpredictable environment. The method is inspired by the stimulus-response behavior of simple animals. An obstacle avoidance controller is developed that uses raw visual information of the environment. It employs reinforcement learning and is therefore capable of self-developing. This should result with obstacle avoidance behavior that is adaptable and therefore generalizes on various operational modalities. The general assumptions of the agent capabilities, the features of the environment as well as the initial result of the simulation are presented. The plans for improvement and suitable performance evaluation are suggested

Path selection system development and evaluation for a Martian roving vehicle

A path selection system evaluation test procedure has been developed to enhance the analysis capability of an existing digital computer simulation package. The procedure investigates the obstacle avoidance ability of a path selection system on a sequence of test terrains with and without random effects. Using the standard test procedure a proposed mid-range sensor system has been evaluated and recommendations directed at improving the performance of the system have been made. In addition, the initial development and evaluation of a short range sensor system has been undertaken

DEVELOPMENT OF AN ARDUINO-BASED OBSTACLE AVOIDANCE ROBOTIC SYSTEM FOR AN UNMANNED VEHICLE

The use of autonomous systems in the world to perform relevant and delicate task is fast growing. However, its application in various fields cannot be over emphasized. This paper presents an obstacle detection and avoidance system for an unmanned Lawnmower. The system consists of two (Infrared and Ultrasonic) sensors, an Arduino microcontroller and a gear DC motor. The ultrasonic and infrared sensors are implemented to detect obstacles on the robot’s path by sending signals to an interfaced microcontroller. The micro-controller redirects the robot to move in an alternate direction by actuating the motorsin order to avoid the detected obstacle. The performance evaluation of the system indicates an accuracy of 85% and 0.15 probability of failure respectively. In conclusion, an obstacle detection circuit was successfully implemented using infrared and ultrasonic sensors modules which were placed at the front of the robot to throw both light and sound waves at any obstacle and when a reflection is received, a low output is sent to the Arduino microcontroller which interprets the output and makes the robot to stop