Path planning and obstacle avoidance hybrid system using a connectionist network. (Final report). Master's thesis

Technical Report · Fri Jun 01 00:00:00 EDT 1990

OSTI ID:6472571

Schuster, C E

Automated path planning and obstacle avoidance has been the subject of intensive research in recent times. Most efforts in the field of semiautonomous mobile-robotic navigation involve using Artificial Intelligence search algorithms on a structured environment to achieve either good or optimal paths. Other approaches, such as incorporating Artificial Neural Networks, have also been explored. By implementing a hybrid system using the parallel-processing features of connectionist networks and simple localized search techniques, good paths can be generated using only low-level environmental sensory data. This system can negotiate structured two- and three-dimensional grid environments, from a start position to a goal, while avoiding all obstacles. Major advantages of this method are that solution paths are good in a global sense and path planning can be accomplished in real time if the system is implemented in customized parallel-processing hardware. This system has been proven effective in solving two- and three-dimensional maze-type environments.

• Research Organization: Rice Univ., Houston, TX (USA)
• OSTI ID: 6472571
• Report Number(s): AD-A-223806/1/XAB
• Resource Relation: Other Information: Thesis
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ROBOTS
OPTIMIZATION
ALGORITHMS
ARRAY PROCESSORS
ARTIFICIAL INTELLIGENCE
AUTOMATION
HYBRID COMPUTERS
NAVIGATION
NEURAL NETWORKS
PARALLEL PROCESSING
PLANNING
POSITION SENSITIVE DETECTORS
PROGRESS REPORT
REAL TIME SYSTEMS
THREE-DIMENSIONAL CALCULATIONS
COMPUTERS
DOCUMENT TYPES
MATHEMATICAL LOGIC
MEASURING INSTRUMENTS
PROGRAMMING
RADIATION DETECTORS
990200* - Mathematics & Computers
990300 - Information Handling