Edan, Y., Agricultural Engineering Institute, Agricultural Research Organization, Department of Agricultural Engineering, Purdue University, P.O.B. 6, Bet-Dagan 50250, Israel, West Lafayette, IN 47907, United States, Department of Applied Mathematics and Computer Sciences, The Weizmann Institute of Science, Rehovot, 76100, Israel, Faculty of Agricultural Engineering, Technion, Israel Institute of Technology, Haifa, 32000, Israel
Peiper, U.M., Agricultural Engineering Institute, Agricultural Research Organization, P.O.B. 6, Bet-Dagan, 50250, Israel
Sarig, Y., Agricultural Engineering Institute, Agricultural Research Organization, P.O.B. 6, Bet-Dagan, 50250, Israel

A near-minimum-time task-planning algorithm for fruit- harvesting robots having to pick fruits at N given locations is presented. For the given kinematic and inertial parameters of the manipulator, the algorithm determines the near-optimal sequence of fruit locations through which the arm should pass and finds the near-minimum-time path between these points. The sequence of motions was obtained by solving the Traveling Salesman Problem (TSP) using the distance along the geodesics in the manipulator’s inertia space, between every two fruit locations, as the cost to be minimized. The algorithm presented here was applied to define the motions of a citrus-picking robot and was tested for a cylindrical robot on fruit position data collected from 20 trees. Significant reduction in the required computing time was achieved by dividing the volume containing the fruits into subvolumes and estimating the geodesic distance rather than calculating it. Nevertheless, in most cases the solution of the TSP, based on the estimated geodesic distance, produced nearly the same fruit sequence as the one resulting from the use of the exact geodesic distance between the fruit locations. Results of simulation tests enabled us to assess the influence of the robot’s kinematic and dynamic parameters and of the spatial distribution of fruits on the motion sequence being selected. The proposed algorithm can help in selecting the most efficient robot design for any robot having to perform a sequence of tasks at N known locations. © 1991 IEEE