Position Sensing and Situational Awareness for Robotic Vehicles

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 onshape-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 asfences), 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, groundresistance, 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 terrainbased 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 andsegmented-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.