Visibility maintenance via controlled invariance for leader-follower Dubins-like vehicles

The paper studies the visibility maintenance problem (VMP) for a leader-follower pair of Dubins-like vehicles with input constraints, and proposes an original solution based on the notion of controlled invariance. The nonlinear model describing the relative dynamics of the vehicles is interpreted as linear uncertain system, with the leader robot acting as an external disturbance. The VMP is then reformulated as a linear constrained regulation problem with additive disturbances (DLCRP). Positive D-invariance conditions for linear uncertain systems with parametric disturbance matrix are introduced and used to solve the VMP when box bounds on the state, control input and disturbance are considered. The proposed design procedure is shown to be easily adaptable to more general working scenarios. Extensive simulation results are provided to illustrate the theory and show the effectiveness of our approachComment: 17 pages, 24 figures, extended version of the journal paper of the authors submitted to Automatic

A reconfigurable hybrid intelligent system for robot navigation

Soft computing has come of age to o er us a wide array of powerful and e cient algorithms that independently matured and in uenced our approach to solving problems in robotics, search and optimisation. The steady progress of technology, however, induced a ux of new real-world applications that demand for more robust and adaptive computational paradigms, tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms and neural networks. As noted in the literature, they are signi cantly more powerful than individual algorithms, and therefore have been the subject of research activities in the past decades. There are problems, however, that have not succumbed to traditional hybridisation approaches, pushing the limits of current intelligent systems design, questioning their solutions of a guarantee of optimality, real-time execution and self-calibration. This work presents an improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search algorithm and the Voronoi diagram generation algorithm

Nowhere to Go: Benchmarking Multi-robot Collaboration in Target Trapping Environment

Collaboration is one of the most important factors in multi-robot systems. Considering certain real-world applications and to further promote its development, we propose a new benchmark to evaluate multi-robot collaboration in Target Trapping Environment (T2E). In T2E, two kinds of robots (called captor robot and target robot) share the same space. The captors aim to catch the target collaboratively, while the target will try to escape from the trap. Both the trapping and escaping process can use the environment layout to help achieve the corresponding objective, which requires high collaboration between robots and the utilization of the environment. For the benchmark, we present and evaluate multiple learning-based baselines in T2E, and provide insights into regimes of multi-robot collaboration. We also make our benchmark publicly available and encourage researchers from related robotics disciplines to propose, evaluate, and compare their solutions in this benchmark. Our project is released at https://github.com/Dr-Xiaogaren/T2E

Sensor-Based Topological Coverage And Mapping Algorithms For Resource-Constrained Robot Swarms

Coverage is widely known in the field of sensor networks as the task of deploying sensors to completely cover an environment with the union of the sensor footprints. Related to coverage is the task of exploration that includes guiding mobile robots, equipped with sensors, to map an unknown environment (mapping) or clear a known environment (searching and pursuit- evasion problem) with their sensors. This is an essential task for robot swarms in many robotic applications including environmental monitoring, sensor deployment, mine clearing, search-and-rescue, and intrusion detection. Utilizing a large team of robots not only improves the completion time of such tasks, but also improve the scalability of the applications while increasing the robustness to systems’ failure. Despite extensive research on coverage, mapping, and exploration problems, many challenges remain to be solved, especially in swarms where robots have limited computational and sensing capabilities. The majority of approaches used to solve the coverage problem rely on metric information, such as the pose of the robots and the position of obstacles. These geometric approaches are not suitable for large scale swarms due to high computational complexity and sensitivity to noise. This dissertation focuses on algorithms that, using tools from algebraic topology and bearing-based control, solve the coverage related problem with a swarm of resource-constrained robots. First, this dissertation presents an algorithm for deploying mobile robots to attain a hole-less sensor coverage of an unknown environment, where each robot is only capable of measuring the bearing angles to the other robots within its sensing region and the obstacles that it touches. Next, using the same sensing model, a topological map of an environment can be obtained using graph-based search techniques even when there is an insufficient number of robots to attain full coverage of the environment. We then introduce the landmark complex representation and present an exploration algorithm that not only is complete when the landmarks are sufficiently dense but also scales well with any swarm size. Finally, we derive a multi-pursuers and multi-evaders planning algorithm, which detects all possible evaders and clears complex environments

A Contextual Approach To Learning Collaborative Behavior Via Observation

This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other software agents and creating a behavior function for a single agent. The research described here combines current research in teamwork simulations and learning by observation to effectively train a multi-agent system in effective team behavior. The dissertation describes the background and work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described

Multi Robot Intruder Search

The aim of this work is the development and analysis of methods and algorithms to allow a multi robot system to cooperatively search a closed, 2-dimensional environment for a human intruder. The underlying problem corresponds to the game-theoretic concept of a classical pursuit evasion game, whereas the focus is set to the generation of plans for the group of pursuers. While the main aspect of of this work lies in the field of probabilistic robotics, concepts and ideas are incorporated from differential game theory, algorithmic geometry and graph theory. The probabilistic basis allows the integration of sensor error as well as nondeterministic robot motion. The main contributions of this work can be divided into three major parts: The first part deals with the development and implementation of probabilistic human models. Depending on the specific behavior of an intruder, ranging from uncooperative to unaware, different classes of intruders are identified. Models are proposed for two of these classes. For the case of a clever and uncooperative intruder who actively evades detection, we propose a model based on the concept of contamination. The second class corresponds to a person who is unaware of the pursuit. We show that simple Markov models, which are often proposed in literature, are not suited for modeling realistic human motion and develop advanced Markov models, which conform to random waypoint motion models. The second part, which is also the most extensive part of this work, deals with the problem of finding an uncooperative and clever intruder. A solution is presented, which projects the problem on a graph structure, which is then searched by a highly optimized A* planner. The solution for the corresponding graph problem is afterwards projected back to the original search space and can be executed by the robotic pursuers. By means of the models proposed in the first part, the performance and correctness of the method is shown. We present experiments in simulation as on real robots to show the practicability and efficiency of the method. The third part deals with the problem of finding an intruder who is unaware of the search. Based on the advanced Markov model previously discussed, a greedy algorithm is proposed, which aims at maximizing the probability to find the intruder in the near future. Experimental results for this method are shown and comparisons to simpler methods are given.Mehrroboter-Eindringlings-Suche Ziel dieser Arbeit ist die Entwicklung und Analyse von Methoden und Algorithmen, die einem kooperativen Mehrrobotersystem erlauben nach einem Eindringling in einer zweidimensionalen, geschlossenen Umgebung zu suchen. Das dem zugrunde liegende Problem entspricht dem spieltheoretischen Konzept eines Suche und Ausweichen Spieles (pursuit evasion game), wobei der Fokus auf der Generierung von Plänen für die Verfolger liegt. Der Hauptaspekt dieser Arbeit liegt dabei im Feld der probabilistischen Robotik, wobei Konzepte und Ideen aus dem Gebiet der differentiellen Spieltheorie, der algorithmischen Geometrie und der Graph Theorie verwendet werden. Die probabilistische Modellierung erlaubt dabei die Integration von Sensorfehlern wie auch nichtdeterministische Roboter-Bewegung. Die Arbeit gliedert sich in drei Hauptteile: Der erste Teil beschäftigt sich mit dem Entwurf und der Implementierung von probabilistischen Modellen für menschliche Bewegung. Abhängig vom angenommenen Verhalten eines Eindringlings, von aktiv ausweichend bis zu ignorant, werden verschiedene Klassen von menschlichem Verhalten unterschieden. Für zwei dieser Klassen stellen wir Modelle auf: Für den Fall einer intelligenten und aktiv ausweichenden Person, generieren wir ein Modell basierend auf dem Konzept von Kontamination. Das zweite Modell entspricht einem Eindringling, der sich der Suche nach ihm nicht bewusst ist. Wir zeigen, dass einfache Markov-Modelle, wie sie in der Vergangenheit oft vorgeschlagen worden sind, ungeeignet sind, um realistische Bewegung zu abzubilden und entwickeln entsprechend erweiterte Markov-Modelle für eine realistischere Modellierung. Der zweite Teil der Arbeit beschäftigt sich mit der Frage, wie man einen intelligente und aktiv ausweichenden Eindringling aufspüren kann. Die vorgestellte Lösung basiert auf der Projektion des Problems auf einen Graphen, der anschließend von einem hoch optimierten A*-Planungsalgorithmus durchsucht werden kann. Diese Lösung kann anschließend auf den ursprünglichen Raum rückprojeziert werden und kann als direkter Plan von den verfolgenden Robotern ausgeführt werden. Mittels der Modelle aus dem ersten Teil wird die Korrektheit und Effizienz der Lösung gezeigt. Der letzte Teil befasst sich mit der Frage, wie ein Eindringling gefunden werden kann, der sich neutral zur Suche verhält. Basierend auf den erweiterten Markov-Modellen aus dem ersten Teil, wird eine Lösung durch gierige Suche präsentiert, die die Wahrscheinlichkeit eine Person im nächsten Zeitschritt aufzuspüren, maximiert. Wie im zweiten Teil werden Experimente diskutiert und diese mit der Proformanz simplerer Methoden verglichen

Search and Pursuit-Evasion in Mobile Robotics, A survey

This paper surveys recent results in pursuitevasion and autonomous search relevant to applications in mobile robotics. We provide a taxonomy of search problems that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment. We then list a number of fundamental results in the areas of pursuit-evasion and probabilistic search, and we discuss field implementations on mobile robotic systems. In addition, we highlight current open problems in the area and explore avenues for future work