Selective tasks such as harvesting or spraying of single crops are a promising research topic in agricultural automation. Inspired by industrial production, an obvious approach is to use robot manipulators in greenhouses and orchards. To exploit the potential of redundant manipulators in particular, advanced motion planning algorithms are needed. While harvesting, a new trajectory for every fruit has to be planned. Although the scenario is similar for every fruit, it is unique for each harvesting sequence. In this paper we present an efficient online planning approach which takes advantage of a simplified environment model. However, the generated trajectory is not optimal in general w.r.t. joint velocities or might even be unfeasible. Thus, we introduce an optional global offline optimization scheme which is able to find optimal trajectories in a few seconds and takes advantage of the heuristic planning as initial guess. We apply the proposed scheme to a 9-DOF agricultural manipulator for sweet-pepper harvesting and evaluate our method by extensive tests with fruit positions based on real measurements.

The exploitation of new fields of application in addition to traditional industrial production for robot manipulators (e.g. agriculture, human areas) requires extensions to the sensor as well as to the planning capabilities. Motion planning solely based on visual information performs poorly in cluttered environments since contacts with obstacles might be inevitable and thus a distinction between hard and soft objects has to be made. In our contribution we present a novel intrinsic tactile sensing module mounted on a multipurpose 9 DOF agricultural manipulator. With its innovative sensor arrangement we consider it to be a low-cost, easily manageable and efficient solution with a reasonable abstraction layer in comparison to complex torque sensing or tactile skins. The sensor provides information about the resulting force and torque. In the second part of our paper, the tactile information is used for minimizing contact forces while pursuing the end-effector tasks as long as reasonable. Hence, we present robust and efficient extensions to Resolved Motion Rate Control for real-time application. We introduce a general formulation providing control inputs in task-space, joint-space and nullspace. Thus, we design a suitable controller by feedback linearization and feed-forward terms. Results from real-world experiments show the potential of our approach. A discussion of the different control schemes completes the paper.

The drive mechanism of many robot joints are composed of an electrical actuator and a gear transmission. Besides actuator dynamics and gear elasticity, friction effects are of particular importance for accurate dynamic modeling. This paper presents the design and development of a modular testbed for experimental friction identification in modular robot drives. We have used this testbed to investigate modules that were developed at our institute for the humanoid robot Lola and an agricultural manipulator. We discovered that a friction law, similar to a law proposed in the literature, can be very accurately fitted to our measurements.

Determining the optimal solution for the inverse kinematics of redundant robots has been the focus of much previous research. Instantaneous approaches are computationally efficient, but may cause high joint velocities due to their local character. In this paper, we present an efficient implementation of a global approach following Pontryagin's Minimum Principle for online calculation. Within a moving horizon, we exploit the decoupled structure of the resulting optimal control problem by using the conjugate gradient method for solving the nonlinear dynamic problem. Different examples of cost functionals are presented and the real-time capability is shown by applying this approach to a 9-DOF redundant manipulator.