The present study proposes a novel approach to Coverage Path Planning for unmanned aerial vehicle (UAV) inspired by the Orienteering Problem. The main goal is to develop an algorithm suitable for Search and Rescue Missions, which can produce a search pattern with dynamical constrains, that is not limited to the traditional back-and-forth motion or spiral patterns. This method leads to a more flexible and diverse coverage of the Area of Interest. In order to generate dynamically correct trajectories, we utilize cubic splines as motion primitives to solve the Orienteering Problem. To accomplish this, we implement and test three different types of cubic splines, namely Catmull-Rom, Freya, and B-splines. To determine the coverage of the search area, the sensor's projection (footprint) is evaluated along the spline trajectory onto a costmap. This method accounts for the footprint's orientation and size, which depend on the UAV's attitude to some extent. This version of the Orienteering Problem using splines for dynamical control and calculating coverage, we call the Mapping Motion Orienteering Problem (MMOP). \\The heuristic method Simulated Annealing is used to address the combinatorial challenges of the MMOP, and two cost functions are tested for optimization. The study shows that the choice of spline has a significant impact on the algorithm's efficacy, and B-splines are the most effective in generating dynamic and adaptable trajectories. However, the study also shows that the Simulated Annealing algorithm with identical settings produced varied resulting paths. Finally, further research is needed to solve the challenges faced with the computational time, which heavily depends on factors such as the sampling rate for the footprint along the path and the resolution of the costmap and footprint itself.

<p>Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. We propose a comprehensive learning-based framework for multi-agent object rearrangement planning, addressing the challenges of task sequencing and path planning in complex environments. The proposed method iteratively selects objects, determines their relocation regions, and pairs them with available robots under kinematic feasibility and task reachability for execution to achieve the target arrangement. Our experiments on a diverse range of environments demonstrate the effectiveness and robustness of the proposed framework. Furthermore, results indicate improved performance in terms of traversal time and success rate compared to baseline approaches. The videos and supplementary material are available at https://sites.google.com/view/maner-supplementary</p>

Previous methods for planning clothoid based continuous curvature paths aim at minimizing path length. However, minimal length paths are not always smooth, natural, and drivable. A method of generating clothoid-based trajectories is discussed using constructive polylines. The goal of the motion planner is to create a path for a large car-like vehicle in human driving environments. Thus, the trajectories generated by the motion planner must be smooth, drivable, and natural such that the vehicle can follow the planned path on human roadways. Several examples are shown of trajectories developed for a DARPA Urban Challenge vehicle and a method of testing the motion planner and the vehicle controller is described.

This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices have emerged as a promising avenue for providing assistance to individuals with hand disabilities, especially those who may not achieve full recovery through surgical interventions. Nevertheless, prevailing exoskeleton glove systems encounter a multitude of challenges spanning design, control, and human-machine interaction. These challenges have given rise to limitations, such as unwieldy bulkiness, an absence of precise force control algorithms, limited portability, and an imbalance between lightweight construction and the essential functionalities required for everyday activities. To address these challenges, this research undertakes a comprehensive exploration of various dimensions within the exoskeleton glove system domain. This includes the intricate design of the finger linkage mechanism, meticulous kinematic analysis, strategic kinematic synthesis, nuanced dynamic modeling, thorough simulation, and adaptive control. The development of two distinct types of series elastic actuators, coupled with the creation of two diverse exoskeleton glove designs based on differing mechanisms, constitutes a pivotal aspect of this study. For the exoskeleton glove integrated with series elastic actuators, a sophisticated dynamic model is meticulously crafted. This endeavor involves the formulation of a mathematical framework to address backlash and the subsequent mitigation of friction forces. The pursuit of accurate force control culminates in the proposition of a data-driven model-free force predictive control policy, compared with a dynamic model-based force control methodology. Notably, the efficacy of the system is validated through meticulous clinical experiments. Meanwhile, the low-profile exoskeleton glove design with a novel mechanism engages in a further reduction of size and weight. This is achieved through the integration of a rigid coupling hybrid mechanism, yielding pronounced advancements in wearability and comfortability. A deep reinforcement learning approach is adopted for the real-time force planning control policies. A simulation environment is built to train the reinforcement learning agent. In summary, this research endeavors to surmount the constraints imposed by existing exoskeleton glove systems. By virtue of advancing mechanism design, innovating control strategies, enriching perception capabilities, and enhancing wearability, the ultimate goal is to augment the functionality and efficacy of these devices within the realm of assistive applications. / Doctor of Philosophy / This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices hold significant promise as valuable aids for patients with hand disabilities who may not achieve full recuperation through surgical interventions. However, the present iteration of exoskeleton glove systems encounters notable limitations in terms of design, control mechanisms, and human-machine interaction. Specifically, prevailing systems often suffer from bulkiness, lack of portability, and an inadequate equilibrium between lightweight construction and the essential functionalities imperative for daily tasks. To address these challenges, this research undertakes a comprehensive exploration of diverse facets within the exoskeleton glove system domain. This encompasses a detailed focus on mechanical design, control strategies, and human-machine interaction. To address wearability and comfort, two distinct exoskeleton glove variations are devised, each rooted in different mechanisms. An innovative data-driven model-free force predictive control policy is posited to enable accurate force regulation. Rigorous clinical experiments are conducted to meticulously validate the efficacy of the system. Furthermore, a novel mechanism is seamlessly integrated into the design of a new low-profile exoskeleton glove, thereby augmenting wearability and comfort by minimizing size and weight. A deep reinforcement learning based control agent, which is trained within a simulation environment, is devised to facilitate real-time autonomous force planning. In summary, the overarching objective of this research lies in rectifying the limitations inherent in existing exoskeleton glove systems. By spearheading advancements in mechanical design, control methodologies, perception capabilities, and wearability, the ultimate aim is to substantially enhance the functionality and overall efficacy of these devices within the sphere of assistive applications.

Nowadays, more and more flexible and efficient processes are required in modern industrial applications. In this field, robots are a key technoligy. In this paper a application is considered, where a 6-axis-industrial robot has to pick-and-place objects time efficiently in a constantly changing environment. Therefore, a concept for automated motion planning is presented, which is composed of two steps which are path planning and trajectory generation. In this paper suitable and established model-based methods are analyzed and chosen. Eventually, the suitability of the presented concept for the considered task is shown by implementing the concept in Matlab and applying it to a 6-axis articulated robot arm.

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This project presents a motion planning algorithmcapable of generating a quadrotor UAV trajectory between aninitial state and a goal state in an obstacle-cluttered environment.This trajectory is dynamically feasible, collision free and optimalby minimizing a cost function in jerk. The algorithm consistsof incrementally expanding a set of concatenated trajectoriesdefined as motion primitives, stored in a tree data structure, untila feasible high-level trajectory is found. TheA∗sorting algorithmis utilized to sort the tree by least cost, hence ensuring the optimaltrajectory is found. In case of narrow spaces requiring someangled UAV attitude, aggressive maneuvering can be attemptedto achieve a feasible trajectory. Two scenarios are introducedfor which feasible trajectories are calculated. These scenariosare also virtually simulated by coupling a UAV dynamic modelwith a feedback controller, for which the feasible trajectories areachieved despite introducing artificial disturbances in the controlinputs. Limitations of the implemented methods are mentioned,together with suggestions to areas of improvement, at the end ofthe report. / Detta projekt presenterar en rörelseplaneringsalgoritm som genererar en bana mellanett initialtillstånd och ett slutmålstillstånd för en fyrmotorigUAV i ett rum med hinder. Denna bana är dynamiskgenomförbar, kollisionsfri samt optimal genom att minimeraen kostnadsfunktion i ryck. Algoritmen består av att stegvis utöka en uppsättning av hoplänkade banor definierade sommotion primitives, som lagras i ett träd (datastruktur), tills engenomförbar bana hittas. SorteringsalgoritmenA∗användsför att sortera trädet efter minst kostnad, vilket säkerställer att den optimala banan hittas. I fallet med små utrymmen,som kräver en vinklad orientering hos UAV:n, kan aggressivamanövrar utföras för att försöka hitta en genomförbar bana.Två scenarion presenteras där genomförbara banor beräknasfram. Dessa scenarion simuleras också virtuellt genom attkoppla UAV:ns dynamiska modell med ettåterkopplingssystem,där banorna genomförs trots att artificiella störningar införsi kontrollsignalerna. Begränsningar i metoden och förslag på förbättringar diskuteras i slutet på rapporten. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Multi-agent planning and control is an active and increasingly studied topic of research, with many practical applications, such as rescue missions, security, surveillance, and transportation. More specifically, cases that involve complex manipulator-endowed systems  deserve extra attention due to potential complex cooperative manipulation tasks and their interaction with the environment. This thesis addresses the problem of cooperative motion- and task-planning of multi-agent and multi-agent-object systems under complex specifications expressed as temporal logic formulas. We consider manipulator-endowed robotic agents that can coordinate in order to perform, among other tasks, cooperative object manipulation/transportation. Our approach is based on the integration of tools from the following areas: multi-agent systems, cooperative object manipulation, discrete abstraction design of multi-agent-object systems, and formal verification. More specifically, we divide the main problem into three different parts.The first part is devoted to the control design for the formation control of a team of rigid-bodies, motivated by its application to cooperative manipulation schemes. We propose decentralized control protocols such that desired position and orientation-based formation between neighboring agents is achieved. Moreover, inter-agent collisions and connectivity breaks are guaranteed to be avoided. In the second part, we design continuous control laws explicitly for the cooperative manipulation/transportation of an object by a team of robotic agents. Firstly, we propose robust decentralized controllers for the trajectory tracking of the object's center of mass.  Secondly, we design model predictive control-based controllers for the transportation of the object with collision and singularity constraints. In the third part, we design discrete representations of multi-agent continuous systems and synthesize hybrid controllers for the satisfaction of complex tasks expressed as temporal logic formulas. We achieve this by combining the results of the previous parts and by proposing appropriate trajectory tracking- and potential field-based continuous control laws for the transitions of the agents among the discrete states. We consider teams of unmanned aerial vehicles and mobile manipulators as well as multi-agent-object systems where the specifications of the objects are also taken into account.Numerical simulations and experimental results verify the claimed results. / <p>QC 20180219</p>