This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB-D camera, high-end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end-user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4-week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.

As robots become more and more capable and autonomous, there is an increasing need for humans to understand what the robots do and think. In this paper, we investigate what such understanding means and includes, and how robots can be designed to support understanding. After an in-depth survey of related earlier work, we discuss examples showing that understanding includes not only the intentions of the robot, but also desires, knowledge, beliefs, emotions, perceptions, capabilities, and limitations of the robot. The term understanding is formally defined, and the term communicative actions is defined to denote the various ways in which a robot may support a human’s understanding of the robot. A novel model of interaction for understanding is presented. The model describes how both human and robot may utilize a first or higher-order theory of mind to understand each other and perform communicative actions in order to support the other’s understanding. It also describes simpler cases in which the robot performs static communicative actions in order to support the human’s understanding of the robot. In general, communicative actions performed by the robot aim at reducing the mismatch between the mind of the robot, and the robot’s inferred model of the human’s model of the mind of the robot. Based on the proposed model, a set of questions are formulated, to serve as support when developing and implementing the model in real interacting robots.

This article discusses mechanisms and principles for assignment of moral responsibility to intelligent robots, with special focus on military robots. We introduce the concept autonomous power as a new concept, and use it to identify the type of robots that call for moral considerations. It is furthermore argued that autonomous power, and in particular the ability to learn, is decisive for assignment of moral responsibility to robots. As technological development will lead to robots with increasing autonomous power, we should be prepared for a future when people blame robots for their actions. It is important to, already today, investigate the mechanisms that control human behavior in this respect. The results may be used when designing future military robots, to control unwanted tendencies to assign responsibility to the robots. Independent of the responsibility issue, the moral quality of robots' behavior should be seen as one of many performance measures by which we evaluate robots. How to design ethics based control systems should be carefully investigated already now. From a consequentialist view, it would indeed be highly immoral to develop robots capable of performing acts involving life and death, without including some kind of moral framework.

A number of algorithms for path tracking are described in the robotics literature. Traditional algorithms, like Pure Pursuit and Follow the Carrot, use position information to compute steering commands that make a vehicle follow a predefined path approximately. These algorithms are well known to cut corners, since they do not explicitly take into account the actual curvature of the path. In this paper we present a novel algorithm that uses recorded steering commands to overcome this problem. The algorithm is constructed within the behavioral paradigm common in intelligent robotics, and is divided into three separate behaviors, each responsible for one aspect of the path tracking task. The algorithm is implemented both on a simulator for autonomous forest machines and a physical small-scale robot. The results are compared with the Pure Pursuit and the Follow the Carrot algorithms, and show a significant improvement in performance.

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