** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THE U.S. BERRY INDUSTRY HAS EXPERIENCED RAPID GROWTH IN THE PAST SEVERAL DECADES AND ACCOUNTED FOR 22.1% OF THE FRUIT MARKET WITH A TOTAL VALUE OF $7.5 BILLION IN 2019. CANEBERRIES, SOMETIMES CALLED BRAMBLES, ARE PARTS OF THE RUBUS FAMILY AND REFER TO A NUMBER OF BERRIES WITH DELICATE DRUPELETS ON THE OUTER SURFACE. AMONG THEM, BLACKBERRIES AND RASPBERRIES ACCOUNT FOR $697 MILLION AND $1.1 BILLION OF THE MARKET VALUE, RESPECTIVELY. WHAT IS MORE, THE CANEBERRY INDUSTRY IS NOT STAGNANT, RATHER, IT IS GROWING, ESPECIALLY THE FRESH-MARKET INDUSTRY. WITH THE INCREASED DEMAND AND NEW CULTIVARS, THE TOTAL BLACKBERRY MARKET VALUE HAS GROWN BY 7.3% BETWEEN 2019 AND 2020, AND THE TOTAL RASPBERRY VALUE HAS GROWN BY 12.3% IN THE SAME PERIOD. THE GLOBAL MARKET VALUE FOR THE FRESH-MARKET CANEBERRY INDUSTRY SPECIFICALLY IS PROJECTED TO GROW 11.3% BETWEEN 2019 AND 2025.THERE IS A GROWING INTEREST TO CHOOSE ROBOTICS AS THE ALTERNATIVES FOR AGRICULTURE HARVESTING TASKS. COMPARED TO CONVENTIONAL MANUAL APPROACH, ROBOT-ASSISTED HARVESTING HAS BEEN SHOWN TO REDUCE THE COST OF PRODUCTION, IMPROVE THE SUPPLY CHAIN, INCREASE LABOR EFFICIENCY, AND REDUCE WASTAGE, ETC. DESPITE THESE COMPELLING ADVANTAGES, ROBOT-ASSISTED HARVESTING METHODS FOR FRESH-MARKET CANEBERRIES HAVE NOT YET BEEN EXPLORED WITH THE FOLLOWING TECHNICAL CHALLENGES. FIRST, CONVENTIONAL ROBOTIC HARVESTING METHODS RELY ON ROUGH HANDLING OF FRUIT BY EITHER (1) CUTTING THE STEM, (2) SHAKING THE FRUIT OFF OF THE PLANT, OR (3) PICKING THE FRUIT WITH VARIOUS GRIPPERS. HOWEVER, THESE CONVENTIONAL HANDLING METHODS WILL INEVITABLY DAMAGE THE SURFACE OF DELICATE FRUITS, SUCH AS CANEBERRIES IN THIS PROJECT, LIMITING THE POST-HARVESTING QUALITY FOR FRESH MARKET. SECOND, FROM THE AGRICULTURAL FIELD ACCESSIBILITY PERSPECTIVE, DEPLOYING WHEELED OR TRACKED ROBOTIC SYSTEMS MANIFEST AN EVIDENT DISADVANTAGE IN TERMS OF MOBILITY AND VERSATILITY COMPARED TO HUMAN LABOR. IN ADDITION, THE BERRIES GROW AT DIFFERENT LOCATIONS WITHIN AND ON THE OUTSIDE OF DENSE WOODY AND LEAFY CANOPIES. THESE FACTS POSE SIGNIFICANT CONSTRAINTS ON THE REACHABILITY SPACE OF THE ROBOT GRIPPER (E.G., BEING POSITIONED TOO FAR FROM THE PLANT) IF INTEGRATED WITH A WHEELED OR TRACKED MOBILE PLATFORM. THIRD, CONVENTIONAL DNN-BASED PERCEPTION METHODS ARE ABLE TO PROVIDE ACCURATE FEEDBACK FOR LARGE FRUITS (E.G., APPLE, CITRUS, KIWIFRUIT), BUT UNFORTUNATELY CANNOT PROVIDE SUFFICIENT INFORMATION FOR PRECISE ROBOTIC CANEBERRY HARVESTING. THE PERCEPTION APPROACH FOR DETECTING AND LOCALIZING SMALL SIZE FRUIT LIKE CANEBERRIES NEEDS TO BE FURTHER INVESTIGATED USING A DUAL-CAMERA SYSTEM FOR AN IMPROVED LOCALIZATION ACCURACY FOR OPTIMAL SELECTIVE HARVESTING IN ORCHARDS.THE IDEAL CANEBERRY HARVESTING ROBOTIC PLATFORM IS EXPECTED TO HAVE THE FOLLOWING CORE FEATURES: 1) HARVESTING MODULE. IT SHOULD DEXTEROUSLY MANIPULATE THE GRIPPERS AROUND THE CANOPIES AND ENABLE COMPLIANT CONTACT BETWEEN BERRY-GRIPPER TO ACHIEVE PREMIUM POST-HARVESTING QUALITY; 2) LOCOMOTION PLATFORM.,IT SHOULD BE ADAPTIVE TO DIFFICULT TERRAIN CONDITIONS AND DRIVE THE HARVESTING MODULE TO DIFFERENT LOCATIONS THAT ARE CLOSE TO THE TRELLIS OR WITHIN THE CANOPIES; AND 3) ROBOT PERCEPTION. IT WILL PROVIDE ACCURATE PERCEPTIONS TO GUIDE THE HARVESTING MODULE AND LOCOMOTION PLATFORM IN COMPLICATED AGRICULTURE SCENARIOS. THE EXISTING ROBOTIC UNITS CAN ONLY PROVIDE PART OF THESE CORE FEATURES, BUT NOT ALL OF THEM. FOR EXAMPLE, OUR SOFT ROBOTIC GRIPPER IS ABLE TO HARVEST THE BLACKBERRIES WITH MINIMAL TO NO RDR, BUT LACKS THE CAPABILITY TO MANIPULATE THE SOFT GRIPPER TOWARDS THE TARGET LOCATIONS WITH REAL-TIME PERCEPTION FEEDBACK.IN THIS PROPOSAL, WE AIM TO INTEGRATE SOFT ROBOT, BIPEDAL ROBOT LOCOMOTION, AND ROBOTIC PERCEPTION TO CREATE A NOVEL OSTRICH-LIKE ROBOTIC PLATFORM AND ITS SYSTEM INTEGRATION FOR FRESH MARKET CANEBERRY HARVESTING. OUR PROPOSED WORK CONSISTS OF FOUR RESEARCH TASKS: (1) SOFT ROBOT DESIGN, MODELING, AND CONTROL, WHERE WE WILL FIRST DEVELOP A MULTI-OBJECTIVE, DATA-DRIVEN OPTIMAL DESIGN STRATEGY TO CREATE THE SOFT ROBOTIC HARDWARE. WE WILL THEN INVESTIGATE SOFT ROBOT MODELING, AND DEVELOP CONTROL ALGORITHM FOR ACCURATE MOTION. (2) ENABLING VERSATILE BIPEDAL LOCOMOTION OVER HIGHLY UNSTRUCTURED AGRICULTURAL FIELDS AND ACCOMPLISHING FRUIT HARVESTING, WHERE WE WILL DESIGN REAL-TIME, TERRAIN-AWARE TRAJECTORY OPTIMIZATION FOR BIPEDAL ROBOT VERSATILE LOCOMOTION TO COOPERATE WITH THE SOFT ROBOTIC MANIPULATION AND ROBOT PERCEPTION TO ACHIEVE AGRICULTURAL TASKS. (3) DEEP NEURAL NETWORKS (DNNS)-DRIVEN ROBOTIC PERCEPTION, WHERE WE WILL ADOPT, ADAPT, AND OPTIMIZE DNNS ARCHITECTURE TO PROCESS THE IMAGING FEEDBACK TO OBTAIN THE BERRY LOCATION AND RIPENESS PERCEPTIONS FOR CLOSED-LOOP CONTROL. (4) INTEGRATING SOFT ROBOT, BIPEDAL LOCOMOTION PLANNING AND DECISION-MAKING, AND PERCEPTION TO CREATE AN INNOVATIVE AGRICULTURAL ROBOTIC SYSTEM, AND PERFORM EVALUATIONS IN BOTH LABORATORY SETTINGS AND COMMERCIAL ORCHARDS.