In this paper, we propose a ROS software package for planning and control of robotic systems with a human-in-the-Ioop focus. The software uses temporal logic specifications, specifically Linear Temporal Logic, for a language-based method to develop correct-by-design high level robot plans. The approach is structured to allow a human to adjust the high-level plan online. A human may also take control of the robot (in a low-level control fashion), but the software prevents the human from implementing dangerous behaviour that would violate the high-level task specification. Finally, the planner is able to learn human-preferred high-level tasks by tracking human low-level control inputs in an inverse learning framework. The proposed approach is demonstrated in a warehouse setting with multiple robot agents to showcase the efficacy of the proposed solution.

Combined organic (molecular adsorption) and inorganic (TiO2 passivation) modifications for enhancing water splitting efficiency of porous bismuth vanadate electrodes are tested. The catalytic activity of BiVO4 is increased after adsorption of a newly prepared ruthenium catalyst. TiO2 passivation and sensitization with RuP dye does not show straightforward improvements to the complex photocatalytic behaviour depending on the configuration of the (two- or three-electrode) photoelectrochemical cell, type of the experiment and sample aging. The time constant for electron transport in BiVO4 electrodes (in the range of seconds, revealed by electrochemical impedance measurements) was found to correlate with the stable photocurrent of the cells. The femtosecond transient absorption studies confirm the negligible effects of RuP on the population of the photoexcited carriers in BiVO4. The transient absorption studies also show that the processes responsible for the differences in photocurrents of the modified BiVO4 samples occur on a time scale longer than the first nanoseconds.

Current control applications necessitate in many cases the consideration of systems with multiple interconnected components. These components/agents may need to fulfill high-level tasks at a discrete planning layer and also coupled constraints at the continuous control layer. Toward this end, the need for combined decentralized control at the continuous layer and planning at the discrete layer becomes apparent. While there are approaches that handle the problem in a top-down centralized manner, decentralized bottom-up approaches have not been pursued to the same extent. We present here some of our results for the problem of combined, hybrid control and task planning from high-level specifications for multi-agent systems in a bottom-up manner. In the first part, we present some initial results on extending the necessary notion of abstractions to multi-agent systems in a distributed fashion. We then consider a setup where agents are assigned individual tasks in the form of linear temporal logic (LTL) formulas and derive local task planning strategies for each agent. In the last part, the problem of combined distributed task planning and control under coupled continuous constraints is further considered.

This paper considers the motion control and task planning problem of mobile robots under complex high-level tasks and human initiatives. The assigned task is specified as Linear Temporal Logic (LTL) formulas that consist of hard and soft constraints. The human initiative influences the robot autonomy in two explicit ways: with additive terms in the continuous controller and with contingent task assignments. We propose an online coordination scheme that encapsulates (i) a mixed-initiative continuous controller that ensures all-time safety despite of possible human errors, (ii) a plan adaptation scheme that accommodates new features discovered in the workspace and short-term tasks assigned by the operator during run time, and (iii) an iterative inverse reinforcement learning (IRL) algorithm that allows the robot to asymptotically learn the human preference on the parameters during the plan synthesis. The results are demonstrated by both realistic human-in-the-loop simulations and experiments.

We consider a multiagent system that consists of heterogeneous groups of homogeneous agents. Instead of defining a global task for the whole team, each agent is assigned a local task as syntactically cosafe linear temporal logic formulas that specify both motion and action requirements. Interagent dependence is introduced by collaborative actions, of which the execution requires multiple agents' collaboration. To ensure the satisfaction of all local tasks without central coordination, we propose a bottom-up motion and task coordination strategy that contains an off-line initial plan synthesis and an online coordination scheme based on real-time exchange of request and reply messages. It facilitates not only the collaboration among heterogeneous agents but also the task swapping between homogeneous agents to reduce the total execution cost. It is distributed as any decision is made locally by each agent based on local computation and communication within neighboring agents. It is scalable and resilient to agent failures as the dependence is formed and removed dynamically based on agent capabilities and their plan execution status, instead of preassigned agent identities. The overall scheme is demonstrated by a simulated scenario of 20 agents with loosely coupled local tasks.

We propose a distributed control and coordination strategy for multi-agent systems where each agent has a local task specified as a Linear Temporal Logic (LTL) formula and at the same time is subject to relative-distance constraints with its neighboring agents. The local tasks capture the temporal requirements on individual agents' behaviors, while the relative-distance constraints impose requirements on the collective motion of the whole team. The proposed solution relies only on relative-state measurements among the neighboring agents without the need for explicit information exchange. It is guaranteed that the local tasks given as syntactically co-safe or general LTL formulas are fulfilled and the relative-distance constraints are satisfied at all time. The approach is demonstrated with computer simulations.

In this paper, we propose a distributed hybrid control strategy for multi-agent systems where each agent has a local task specified as a Linear Temporal Logic (LTL) formula and at the same time is subject to relative-distance constraints with its neighboring agents. The local tasks capture the temporal requirements on individual agents' behaviors, while the relative-distance constraints impose requirements on the collective motion of the whole team. The proposed solution relies only on relative-state measurements among the neighboring agents without the need for explicit information exchange. It is guaranteed that the local tasks given as syntactically co-safe or general LTL formulas are fulfilled and the relative-distance constraints are satisfied at all time. The approach is demonstrated with computer simulations.

We propose a distributed and cooperative motion and task control scheme for a team of mobile robots that are subject to dynamic constraints including inter-robot collision avoidance and connectivity maintenance of the communication network. Moreover, each agent has a local high-level task given as a Linear Temporal Logic (LTL) formula of desired motion and actions. Embedded graph grammars (EGGs) are used as the main tool to specify local interaction rules and switching control modes among the robots, which is then combined with the model-checking-based task planning module. It is ensured that all local tasks are satisfied while the dynamic constraints are obeyed at all time. The overall approach is demonstrated by simulation and experimental results.

We propose a bottom-up motion and task coordination scheme for loosely-coupled multi-agent systems under dependent local tasks. Instead of defining a global task for the whole team, each agent is assigned locally a task as syntactically co-safe linear temporal logic formulas that specify both motion and action requirements. Inter-agent dependency is introduced by collaborative actions of which the execution requires multiple agents' collaboration. The proposed solution contains an offline initial plan synthesis, an on-line request-reply messages exchange and a real-time plan adaptation algorithm. It is distributed in that any decision is made locally based on local computation and local communication within neighboring agents. It is scalable and resilient to agent failures as the dependency is formed and removed dynamically based on the plan execution status and agent capabilities, instead of pre-assigned agent identities. The overall scheme is demonstrated by a simulated scenario.

Autonomous robots like household service robots, self-driving cars and dronesare emerging as important parts of our daily lives in the near future. They need tocomprehend and fulfill complex tasks specified by the users with minimal humanintervention. Also they should be able to handle un-modeled changes and contingentevents in the workspace. More importantly, they shall communicate and collaboratewith each other in an efficient and correct manner. In this thesis, we address theseissues by focusing on the distributed and hybrid control of multi-robot systemsunder complex individual tasks.

We start from the nominal case where a single dynamical robot is deployed in astatic and fully-known workspace. Its local tasks are specified as Linear TemporalLogic (LTL) formulas containing the desired motion. We provide an automatedframework as the nominal solution to construct the hybrid controller that drives therobot such that its resulting trajectory satisfies the given task. Then we expand theproblem by considering a team of networked dynamical robots, where each robot hasa locally-specified individual task also as LTL formulas. In particular, we analyzefour different aspects as described below.

When the workspace is only partially known to each robot, the nominal solutionmight be inadequate. Thus we first propose an algorithm for initial plan synthesis tohandle partially infeasible tasks that contain hard and soft constraints. We designan on-line scheme for each robot to verify and improve its local plan during runtime, utilizing its sensory measurements and communications with other robots. Itis ensured that the hard constraints for safety are always fulfilled while the softconstraints for performance are improved gradually.

Secondly, we introduce a new approach to construct a full model of both robotmotion and actions. Based on this model, we can specify much broader robotic tasksand it is used to model inter-robot collaborative actions, which are essential for manymulti-robot applications to improve system capability, efficiency and robustness.Accordingly, we devise a distributed strategy where the robots coordinate theirmotion and action plans to fulfill the desired collaboration by their local tasks.

Thirdly, continuous relative-motion constraints among the robots, such as collision avoidance and connectivity maintenance, are closely related to the stability,safety and integrity of multi-robot systems. We propose two different hybrid controlapproaches to guarantee the satisfaction of all local tasks and the relative-motionconstraints at all time: the first one is based on potential fields and nonlinear controltechnique; the second uses Embedded Graph Grammars (EGGs) as the main tool.

At last, we take into account two common cooperative robotic tasks, namelyservice and formation tasks. These tasks are requested and exchanged among therobots during run time. The proposed hybrid control scheme ensures that the real-time plan execution incorporates not only local tasks of each robot but also thecontingent service and formation tasks it receives.

Some of the theoretical results of the thesis have been implemented and demonstrated on various robotic platforms.

Denna avhandling fokuserar på distribuerad och hybridstyrning av multi-robot-system för komplexa, lokala och tidsberoende uppgifter. Dessa uppgifter specificerasav logiska formler rörande robotens rörelser och andra ageranden. Avhandlingenbehandlar ett tvärvetenskapligt område som integrerar reglering av nätverkaderobotsystem och planering baserad på formella metoder. Ett ramverk för hybridstyrning av flera dynamiska robotar med lokalt specificerade uppgifter presenteras.Fyra huvudscenarier betraktas: (1) robot-planering med motstridiga arbetsuppgifterinom ett delvis okänt arbetsområde; (2) beroende uppgifter för en grupp heterogenaoch samverkande robotar; (3) relativa rörelsebegränsningar hos varje robot; samt(4) robotar med uppgifter som begärs och bekräftas under körning. Numeriskasimuleringar och experiment visas för att validera de teoretiska resultaten.