Short-term effects of human urine fertiliser and wood ash on soil pH and electrical conductivity

The fertiliser value of human urine has been examined on several crops, yet little is known about its effects on key soil properties of agronomic significance. This study investigated temporal soil salinization potential of human urine fertiliser (HUF). It further looked at combined effects of human urine and wood ash (WA) on soil pH, urine-NH_3 volatilisation, soil electrical conductivity (EC), and basic cation contents of two Acrisols (Adenta and Toje series) from the coastal savannah zone of Ghana. The experiment was a factorial design conducted in the laboratory for 12 weeks. The results indicated an increase in soil pH by 1.2 units for Adenta series and 1 unit for Toje series after one week of HUF application followed by a decline by about 2 pH units for both soil types after twelve weeks. This was attributed to nitrification of ammonium to nitrate leading to acidification. The EC otherwise increased with HUF application creating slightly saline conditions in Toje series and non-saline conditions in Adenta series. When WA was applied with HUF, both soil pH and EC increased. In contrast, the HUF alone slightly salinized Toje series, but both soils remained non-saline whenWA and HUF were applied together. The application ofWA resulted in two-fold increase in Ca, Mg, K, and Na content compared to HUF alone. Hence, WA is a promising amendment of acid soils and could reduce the effect of soluble salts in human urine fertilizer, which is likely to cause soil salinity.

Optimization-Based Robotic Manipulation for Safe Interaction with Human Operators

This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming. The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex optimization problem is approximated by a mixed-integer program (MIP). It is further effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased. Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold. The developed methods have been successfully applied to a developed demonstrator, where an industrial robot works in the same space as a human operator. The task of the industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.