Position Sensing and Situational Awareness for Robotic Vehicles

Period of Performance: 02/27/2001 - 12/01/2001


Phase 1 SBIR

Recipient Firm

Turing Assoc., Inc.
5220 Sutton Rd
Ann Arbor, MI 48105
Principal Investigator


The proposal is to develop low-cost technologies for enhanced perception and terrain understanding for robotic ground vehicle navigation. We propose to combine structured lighting with stereo vision, using innovative image processing based on shape-from-shading and shadow processing. This will provide robust ability to detect and segment negative obstacles (e.g., down steps), to estimate upcoming terrain slope, to improve object detection and segmentation (including porous obstacles such as fences), and improve texture characterization. We propose to use internal self-status sensors (e.g., inertial navigation sensors, current meters, load sensors) to collect data to characterize terrain trafficability (e.g., roughness, slope, ground resistance, traction limits, slip) for path planning. The mobile robot will exectute stylized maneuvers to measure terrain trafficability characteristics. We propose to use frequency analysis feature extraction and machine learning to classify terrain based on its trafficability (supporting landmark recognition and map region localization). We propose to train machine leaning systems to predict trafficability characteristics from structured lighting/stereo vision image texture metrics and segmented-region shape features. Preliminary experiments have demonstrated the feasiblity of key elements of the proposed approach.The research products will be applicable to DoD unmanned ground vehicle programs including the Future Combat Systems (FCS) vehicles, security robots, mine clearing and unexploded ordnance removal robots. The products will have potential applicability in commercial automotive intelligent vehicle development.