Obstacle avoidance strategy based on adaptive potential fields generated by an electronic stick

In our previous work, an obstacle avoidance algorithm, which used potential fields and a similar strategy to that adopted by a blind person to avoid obstacles whilst walking, was proposed. The problem analyzed consists of an AGV (Autonomous Guided Vehicle) which moves within an office environment with a known floor plan and uses an ”electronic stick” made up of infrared sensors to detect unknown obstacles in its path. Initially, a global potential navigation function, defined for each room in the floor plan, incorporates information about the dimensions of the room and the position of the door which the AGV must use to leave the room. Whilst the AGV moves, this global potential navigation function is properly modified to incorporate information about any newly detected obstacle. The main interesting aspect of the proposed approach is that the potential function adaptation involves very low computational burden allowing for the use of Ultra-fast AGVs. Other distinctive features of the algorithm are that it is free from local minima, the obstacles can have any shape, low cost sensors can be used to detect obstacles and an appropriate balance is achieved between the use of the global and the local approaches for collision avoidance. Our present work is a refinement of this strategy that allows for an automatic real time adaptation of the algorithm’s parameters. Now, the algorithm’s functioning requires only that the minimum distance at which the AGV can approach an obstacle (i.e. the closest it can get to any obstacle) is defined a priori. Aspects of the real implementation of the algorithm are also discussed

Application of a blind person strategy for obstacle avoidance with the use of potential fields

This paper proposes a new obstacle avoidance algorithm for the CONTROLAB AGV which uses a similar strategy adopted by a blind person to avoid obstacles while walking. The AGV moves within an office environment with a known floorplan and uses an "electronic stick" consisting of infraredsensors to detect unknown obstacles. Initially a global potential field function is defined for each floorplan room. While the AGV is moving, the original potential function is modified each time an obstacle is detected by the infrared sensors. This modification is simply performed by the addition of previously calculated potentlal field values on a grid which represents the room working area. The interestlng aspects of the proposed approach are that the potential function adaptation involves very low computational burden, the algorithm is free from local minima, the obstacles can have any shape and low cost sensors can be used to detect obstacles

CONTROLAB MUFA: a multi-level fusion architecture for intelligent navigation of a telerobot

This paper proposes a MUlti-level Fusion Architecture (MUFA) for controlling the navigation of a tele-commanded Autonomous Guided Vehicle (AGV). The architecture combines ideas derived from the fundamental concepts of sensor fusion and distributed intelligence. The focus of the work is the development of an intelligent navigation system for a tricycle drive AGV with the ability to move autonomously within any office enviromnent, following instructions issued by client stations connected to the office network and reacting accordingly to different situations found in the real world. The modules which integrate the MUFA architecture are discussed and results of some simulation experiments are presented

Parallelizing Black Oil Reservoir Simulation Systems for SMP Machines

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CONTROLAB: Integration of Intelligent Systems for the Control of a Robot Arm

ABSTRACT CONTROLAB integrates intelligent systems and control algorithms aiming at applications in the area of robotics. This paper focuses on the analysis of the word recognition and the trajectory definition systems considering an application in which a robot arm is commanded by voice to pick up a specific tool placed on a table among other tools and obstacles. Neural network architectures based on the backpropagation and the recursive models are proposed for the implementation of a speaker-independent word recognition system. The robustness of the system using the backpropagation network has been verified in totally uncontrolled environments such as large public halls for the exhibition of new technology products. Experimental results with the recursive network show that a carefully designed network structure is able to overcome the false alarm problem faced by the backpropagation network. The trajectory to be followed by the robot arm is determined through the analysis of image information and the use of the VGRAPH algorithm to avoid obstacles. The algorithm performance is analysed and compared with that achieved by the PFIELD algorithm