**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** HARVESTING IS ONE OF THE MOST LABOR-INTENSIVE OPERATIONS IN FRESH-MARKET FRUIT PRODUCTION, INCURRING HIGH COST AND DEPENDENCE ON A LARGE SEASONAL SEMI-SKILLED WORKFORCE, WHICH IS BECOMING LESS AVAILABLE. EXISTING MECHANICAL HARVESTING METHODS - SUCH AS TRUNK OR CANOPY SHAKING - RESULT IN UNACCEPTABLE FRUIT DAMAGE AND CANNOT BE USED TO HARVEST FRUITS AT A GIVEN RIPENING STAGE OR SIZE SELECTIVELY. ROBOTIC HARVESTING IS STILL AT A PRE-COMMERCIAL STAGE, DESPITE ACTIVE RESEARCH FOR AT LEAST 30 YEARS. STATE-OF-THE- ART ROBOTS CAN HARVEST FRUITS FROM TREES WITH THIN, ALMOST TWO-DIMENSIONAL CANOPIES THAT HAVE BEEN METICULOUSLY PRUNED AND THINNED TO OFFER VERY HIGH FRUIT VISIBILITY AND REACHABILITY. STILL, ROBOTIC FRUIT PICKING EFFICIENCY AND SPEED ARE INADEQUATE FOR COST- EFFECTIVE OPERATION.THE PRIMARY GOAL OF THIS PROJECT IS TO SIGNIFICANTLY IMPROVE THE FRUIT PICKING EFFICIENCY AND PICKING CYCLE OF ROBOTIC HARVESTERS AND EXPAND THEIR OPERATION TO BROADER CLASSES OF ORCHARDS, WITH TRELLISEDAND HEDGED TREES WITH DEEPER CANOPIES AND LESS STRINGENT PRUNING AND THINNING REQUIREMENTS. OUR APPROACH DEVIATES SIGNIFICANTLY FROM THE ESTABLISHED PARADIGM IN ROBOTIC FRUIT HARVESTING IN TWO SIGNIFICANT WAYS. FIRST, INSTEAD OF RELYING ON A FEW, VERY FAST ARMS WITH MANY DEGREES-OF-FREEDOM THAT ARE TOO EXPENSIVE TO DEPLOY IN LARGE NUMBERS, ROBOT-ARM ARRAYS OF MANY INEXPENSIVE LINEAR ARMS WILL BE USED TO REACH FRUITS. SECOND, IT IS RECOGNIZED THAT FRUIT VISIBILITY IS KEY TO FRUIT DETECTION, AND IT IS PROPOSED THAT TREE FOLIAGE BE AGITATED VIA AIRFLOW IN A TARGETED AND CONTROLLED FASHION, AND THAT IMAGE SEQUENCES FROM CAMERAS AT MULTIPLE VIEWPOINTS BE UTILIZED, IN CONJUNCTION WITH DEEP LEARNING, TO DRASTICALLY INCREASE FRUIT VISIBILITY AND DETECTION. A PROTOTYPE ROBOT THAT EMBODIES THE ABOVE APPROACHES WILL BE BUILT AND EVALUATED IN HIGH-DENSITY APPLE AND PEAR ORCHARDS.INTELLECTUAL MERIT.EXISTING APPROACHES FOR SCHEDULING AND PLANNING COORDINATED MOTIONS FOR LARGE NUMBERS OF ROBOT ARMS THATREPORT DATE NEED TO OPTIMALLY REACH A LARGE SET OF POINTS ASSUME THAT ALL POINTS ARE FIXED AND ACCURATELY KNOWN IN ADVANCE. IN ROBOTIC FRUIT HARVESTING, CHANGES IN FRUIT VISIBILITY AND ROBOT-CANOPY INTERACTIONS CAUSE A CONSTANT CHANGE IN THE SET OF FRUITS TO BE PICKED (IN TERMS OF THE NUMBER OF FRUITS, THEIR POSITIONS, SYSTEM CONFIDENCE IN THEIR PRESENCE). ADDITIONALLY, ARMS HAVE LIMITED TIME TO REACH FRUITS BECAUSE THE HARVESTER MOVES. FINALLY, INTER-ROBOT GEOMETRIC CONSTRAINTS MUST BE RESPECTED. THEREFORE A NEW APPROACH IS NEEDED FOR SCHEDULING IN REAL TIME THE PICKING MOTIONS OF MULTI-ARM FRUIT HARVESTERS. WE PLAN TO ADDRESS THIS INTELLECTUAL CHALLENGE BY ESTABLISHING CORRESPONDENCES BETWEEN THIS PROBLEM AND STOCHASTIC DYNAMIC VEHICLE ROUTING PROBLEMS AND BY EXPLOITING RECENT ADVANCES IN THEIR SOLUTION THAT UTILIZE PARALLEL HARDWARE (GRAPHICS PROCESSOR UNITS - GPUS) TO COMPUTE NEAR- OPTIMAL ROBUST SOLUTIONS THAT INCORPORATE UNCERTAINTY. ALSO, RESOLVING FRUIT PRESENCE AND POSITION AMBIGUITIES IN DEEP AND VIGOROUS TREE CANOPIES NECESSITATES SIGNIFICANTLY IMPROVED FRUIT DETECTION IN INDIVIDUAL IMAGES AND INTEGRATION OF A LARGE NUMBER OF VIEWPOINTS. OUR COMBINED APPROACH OF FOLIAGE AGITATION AND DEEP LEARNING MULTI-VIEW DETECTION ADDRESSES THE FIRST CHALLENGE. PLACING SEVERAL CAMERAS ON THE HARVESTER CREATES AN IMAGING ARRAY; PROCESSING AND INTEGRATING THE RESULTING IMAGES - IN THE PRESENCE OF MOVING ARMS - CONSTITUTES AN INTELLECTUAL AND TECHNICAL CHALLENGE THAT WILL BE ADDRESSED VIA PROBABILISTIC MERGING OF MULTIPLE FRUIT PROJECTIONS FROM NEIGHBORING CAMERAS AND UTILIZATION OF PARALLEL GRAPHICS PROCESSORS.BROADER IMPACT.COST-EFFICIENT ROBOTIC FRUIT HARVESTERS WILL INCREASE THE COMPETITIVENESS AND SUSTAINABILITY OF THE FRUIT INDUSTRY AND BENEFIT GROWERS AND THEIR FAMILIES DIRECTLY. AUTOMATION ENABLES INCREASED PRODUCTION OF LOW-COST, HIGH-QUALITY FRUITS, LEADING TO MORE YEAR-ROUND JOBS FOR FARM WORKERS; HIGHER-PAID OPERATOR JOBS AND INCREASED LABOR DEMAND AT THEPOSTHARVEST STAGE, AND INCREASED HEALTH AND NUTRITION FOR CONSUMERS, ESPECIALLY FOR LOW-INCOME FAMILIES. LOCATION DATA ACCOMPANYING PICKED FRUIT CAN LEAD TO YIELD MAPS FOR BETTER MANAGEMENT OF WATER AND NUTRIENTS LEADING TO ECONOMIC AND ENVIRONMENTAL BENEFITS, AND TO INCREASED TRACEABILITY FOR CONSUMER SAFETY. FURTHERMORE, THE PROJECT'S EDUCATIONAL AGENDA SPANS THE GRADUATE, UNDERGRADUATE, AND K-12 LEVELS AND UTILIZES PROJECT-BASED LEARNING AND FIELDWORK TO CROSS-POLLINATE AMONG DISCIPLINES. THE RESEARCHERS WILL LEVERAGE THE THEMES OF THE PROJECT AND THE INCREASING AWARENESS AND CONCERN FOR SUSTAINABLE AND HEALTHY FOOD PRODUCTION TO ENGAGE K-12 STUDENTS IN STEM-PROMOTING ACTIVITIES.