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.

Bridging Perspectives

People’s ability to change their social and economic circumstances may be constrained by various forms of social, cultural and political domination. Thus to consider a social actor’s particular lifeworld in which the research is embedded assists in the understanding of how and why different trajectories of change occur or are hindered and how those changes fundamentally affect livelihood opportunities and constraints. In seeking to fulfill this condition this thesis adopted an actor-oriented approach to the study of rural livelihoods. A comprehensive livelihoods study requires grasping how social reality is being historically constituted. That means to understand how the interaction of modes of production and symbolical reproduction produces the socio-space. Research is here integrated to action through the facilitation of farmer groups. The overall aim of the groups was to prompt agency, as essential conditions to build more resilient livelihoods. The smallholder farmers in the Mabalane District of Mozambique are located in a remote semi-arid area. Their livelihoods customarily depend at least as much on livestock as on (mostly) rain-fed food crops. Increased climate variability exerts pressure on the already vulnerable production system. An extensive 10-month duration of participant observation divided into 3 periods of fieldwork structured the situated multi-method approach that drew on a set of interview categories. The actor-oriented appraisal of livelihoods worked in building a mutually shared definition of the situation. This reflection process was taken up by the facilitation of the farmer groups, one in Mabomo and one in Mungazi, which used an inquiry iteratively combining individual interviews and facilitated group meetings. Integration of action and reflection was fundamental for group constitution as spaces for communicative action. They needed to be self-organized and to achieve understanding intersubjectively, as well as to base action on cooperation and coordination. Results from this approach focus on how learning as collaboratively generated was enabled, or at times hindered, in (a) selecting meaningful options to test; (b) in developing mechanisms for group functioning; and (c) in learning from steering the testing of options. The study of livelihoods looked at how the different assets composing livelihoods are intertwined and how the increased severity of dry spells is contributing to escalated food insecurity. The reorganization of the social space, as households moved from scattered homesteads to form settlements, further exerts pressure on the already scarce natural resource-based livelihoods. Moreover, this process disrupted a normative base substantiating the way that the use of resources is governed. Hence, actual livelihood strategies and response mechanisms turn to diversification through income-generating activities that further increase pressure on the resource-base in a rather unsustainable way. These response mechanisms are, for example, the increase in small-livestock keeping, which has easier conversion to cash, and charcoal production. The latter results in ever more precarious living and working conditions. In the majority of the cases such responses are short-term and reduce future opportunities in a downward spiral of continuously decreasing assets. Thus, by indicating the failure of institutions in the mediation of smallholders’ adaptive capabilities, the livelihood assessment in Mabomo and Mungazi sheds light on the complex underlying structure of present day social vulnerability, linking the macro-context to the actual situation. To assist in breaking this state of “subordination”, shaped by historical processes, weak institutions and food insecurity, the chosen approach to facilitation of farmer groups puts farmer knowledge at the center of an evolving process of intersubjective co-construction of knowledge towards emancipation.

Decision Making for Teams of Mobile Robots

In the past years, we could observe a significant amount of new robotic systems in science, industry, and everyday life. To reduce the complexity of these systems, the industry constructs robots that are designated for the execution of a specific task such as vacuum cleaning, autonomous driving, observation, or transportation operations. As a result, such robotic systems need to combine their capabilities to accomplish complex tasks that exceed the abilities of individual robots. However, to achieve emergent cooperative behavior, multi-robot systems require a decision process that copes with the communication challenges of the application domain. This work investigates a distributed multi-robot decision process, which addresses unreliable and transient communication. This process composed by five steps, which we embedded into the ALICA multi-agent coordination language guided by the PROViDE negotiation middleware. The first step encompasses the specification of the decision problem, which is an integral part of the ALICA implementation. In our decision process, we describe multi-robot problems by continuous nonlinear constraint satisfaction problems. The second step addresses the calculation of solution proposals for this problem specification. Here, we propose an efficient solution algorithm that integrates incomplete local search and interval propagation techniques into a satisfiability solver, which forms a satisfiability modulo theories (SMT) solver. In the third decision step, the PROViDE middleware replicates the solution proposals among the robots. This replication process is parameterized with a distribution method, which determines the consistency properties of the proposals. In a fourth step, we investigate the conflict resolution. Therefore, an acceptance method ensures that each robot supports one of the replicated proposals. As we integrated the conflict resolution into the replication process, a sound selection of the distribution and acceptance methods leads to an eventual convergence of the robot proposals. In order to avoid the execution of conflicting proposals, the last step comprises a decision method, which selects a proposal for implementation in case the conflict resolution fails. The evaluation of our work shows that the usage of incomplete solution techniques of the constraint satisfaction solver outperforms the runtime of other state-of-the-art approaches for many typical robotic problems. We further show by experimental setups and practical application in the RoboCup environment that our decision process is suitable for making quick decisions in the presence of packet loss and delay. Moreover, PROViDE requires less memory and bandwidth compared to other state-of-the-art middleware approaches.