Autonomous place detection has long been a major hurdle to topological map-building techniques. Theoretical work on topological mapping has assumed that places can be reliably detected by a robot, resulting in deterministic actions. Whether or not deterministic place detection is always achievable is controversial; however, even topological mapping algorithms that assume non-determinism benefit from highly reliable place detection. Unfortunately, topological map-building implementations often have handcoded place detection algorithms that are brittle and domain dependent.

We propose a novel, vision-based method for robot homing, the problem of computing a route so that a robot can return to its initial "home" position after the execution of an arbitrary "prior" path. The method assumes that the robot tracks visual features in panoramic views of the environment that it acquires as it moves. By exploiting only angular information regarding the tracked features, a local control strategy moves the robot between two positions, provided that there are at least three features that can be matched in the panoramas acquired at these positions. The strategy is successful when certain geometric constraints on the configuration of the two positions relative to the features are fulfilled. In order to achieve long-range homing, the features' trajectories are organized in a visual memory during the execution of the "prior" path. When homing is initiated, the robot selects Milestone Positions (MPs) on the "prior" path by exploiting information in its visual memory. The MP selection process aims at picking positions that guarantee the success of the local control strategy between two consecutive MPs. The sequential visit of successive MPs successfully guides the robot even if the visual context in the "home" position is radically different from the visual context at the position where homing was initiated. Experimental results from a prototype implementation of the method demonstrate that homing can be achieved with high accuracy, independent of the distance traveled by the robot. The contribution of this work is that it shows how a complex navigational task such as homing can be accomplished efficiently, robustly and in real-time by exploiting primitive visual cues. Such cues carry implicit information regarding the 3D structure of the environment. Thu...

Abstract — Given a robot model and a class of admissible environments, this paper provides a framework for automatically and verifiably composing controllers that satisfy high level task specifications expressed in suitable temporal logics. The desired task specifications can express complex robot behaviors such as search and rescue, coverage, and collision avoidance. In addition, our framework explicitly captures sensor specifications that depend on the environment with which the robot is interacting, resulting in a novel paradigm for sensor-based temporal logic motion planning. As one robot is part of the environment of another robot, our sensor-based framework very naturally captures multi-robot specifications. Our computational approach is based on first creating discrete controllers satisfying so-called General Reactivity(1) formulas. If feasible, the discrete controller is then used in order to guide the sensor-based composition of continuous controllers resulting in a hybrid controller satisfying the high level specification, but only if the environment is admissible. Index Terms — Motion planning, temporal logics, sensorbased planning, controller synthesis, hybrid control.

A central problem in interactive virtual environments is planning high-quality paths for characters avoiding obstacles in the environment. Current applications require a path planner that is fast (to ensure real-time interaction with the environment) and flexible (to avoid local hazards). In addition, paths need to be natural, i.e. smooth and short. To satisfy these requirements, we need an adequate representation of the free space stored in a convenient data structure, a fast mechanism for querying this data structure, and an algorithm that constructs natural paths for the characters. We improve an existing data structure, the Corridor Map, which represents the free space by a graph whose edges correspond to collision-free corridors. We show that this structure, together with a kd-tree, can be used for fast querying, resulting in a corridor that guides the global path of the character. Its local motions are controlled by force functions, providing the desired flexibility. Experiments show that the improvements lead to a method which can steer a crowd of ±10,000 characters in real-time.

Abstract — In robot motion planning, many algorithms have been proposed that create a roadmap from which a path for a moving object can be extracted. These algorithms generally do not give guarantees on the quality of the roadmap, i.e. they do not promise that a path will always be found in the roadmap if one exists in the world. Furthermore, such roadmaps often become very large which can cause memory problems and high query times. We present a new efficient algorithm that creates small roadmaps for two- and three-dimensional problems. The algorithm ensures that a path is always found (if one exists) at a given resolution. These claims are verified on a broad range of environments. The results also give insight in the structure of covering roadmaps. Keywords—motion planning, small roadmaps, PRM I.

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Abstract — This paper addresses the problem of exploring an unknown environment with a team of mobile robots. The key issue in coordinated multi-robot exploration is how to assign target locations to the individual robots such that the overall mission time is minimized. In this paper, we propose a novel approach to distribute the robots over the environment that takes into account the structure of the environment. To achieve this, it partitions the space into segments, for example, corresponding to individual rooms. Instead of only considering frontiers between unknown and explored areas as target locations, we send the robots to the individual segments with the task to explore the corresponding area. Our approach has been implemented and tested in simulation as well as in real world experiments. The experiments demonstrate that the overall exploration time can be significantly reduced by considering our segmentation method. I.

Abstract. This paper presents a graph-based spatial model which can serve as a reference for guiding pedestrians inside buildings. We describe a systematic approach to construct the model from geometric data. In excess of the well-known topological relations, the model accounts for two important aspects of pedestrian navigation: firstly, visibility within spatial areas and, secondly, generating route descriptions. An algorithm is proposed which partitions spatial regions according to visibility criteria. It can handle simple polygons as encountered in floor plans. The model is structured hierarchically- each of its elements corresponds to a certain domain concept (‘room’, ‘door’, ‘floor ’ etc.) and can be annotated with meta information. This is useful for applications in which such information have to be evaluated. 1 Introduction and Related Work Pedestrian guidance within buildings [6] differs from customary navigation based on networks. This is due to the following reasons:

One of the fundamental tasks robots have to perform is planning their motions while avoiding collisions with obstacles in the environment. This is the central topic of the thesis. We restrict ourselves to motion planning for two- and three-dimensional rigid bodies and articulated robots moving in static and known virtual environments. This thesis has been divided into two parts. The first part deals with comparing and analyzing sampling-based motion planning techniques, in particular variants of the Probabilistic Roadmap Method (PRM). The PRM consists of two phases: a construction and a query phase. In the construction phase, a roadmap (graph) is built, approximating the motions that can be made in the environment. First, a free random sample is created. Such a sample describes a particular placement of the moving object (robot) in the workspace. Then, a simple local planner is employed to connect the sample to some neighbors. Samples and connections are added to the graph until the roadmap is dense enough. In the query phase, the start and goal samples are connected to the graph. The path is obtained by a Dijkstra's shortest path algorithm. Many variants of the PRM have been developed over the past decade. Using both time-based as well as reachability-based analysis, we compare some of the most prominent techniques. The results are surprising in the sense that techniques often perform differently than claimed by the designers. In addition, contrary to general belief, the main challenge is not getting the free space covered but getting the nodes connected, especially when the problems get more complicated, e.g. when a narrow passage is present. By using this knowledge, we can tackle the narrow passage problem by incorporating a more powerful local planner, a refined neighbor selection strategy and a hybrid sampling strategy. The analysis also shows why the PRM successfully deals with many motion planning problems. The second part deals with quality aspects of paths and roadmaps. A good path is relatively short, keeps some distance (clearance) from the obstacles, and is smooth. We will provide algorithms that increase path clearance. A big advantage of these algorithms is that high-clearance paths can now be efficiently created without using complex data structures and algorithms. We also elaborate on algorithms that successfully decrease path length. Then, we introduce the Reachability Roadmap Method which creates small roadmaps for two- and three-dimensional problems. Such a small roadmap has many advantages over a roadmap that is created by the PRM. In particular, the method assures low query times, low memory consumption, and the roadmap can be optimized easily. The algorithm also ensures that a path is always found (if one exists) at a given resolution. We unify the techniques to create high-quality roadmaps for interactive virtual environments. That is, we use the Reachability Roadmap Method to create an initial roadmap. We add useful cycles to provide alternative routes and short paths, and we add clearance to the roadmap to obtain high-clearance paths in real-time.

Abstract In robot motion planning, many algorithms have been proposed that create a path fora robot in an environment with obstacles. Since it can be hard to create such paths, most algorithms are aimed at finding a solution. However, for most applications the paths mustbe of a good quality as well. That is, paths should preferably be short, be smooth, and should preserve some clearance from the obstacles. In this paper, we focus on improvingthe clearance of paths. Existing methods only extract high clearance paths for rigid, translating bodies. We present an algorithm that improves the clearance along paths ofa broader range of robots which may reside in arbitrary high-dimensional configuration spaces. Examples include planar, free-flying and articulated robots. 1 Introduction Motion planning is one of the fundamental problems in robotics. The motion planning problem can be defined as finding a path between a start and goal placement of a robot in an environment with obstacles. The last decade, efficient algorithms have been devised to tackle this problem. They are successfully applied in fields such as mobile robots, manipulation planning, CAD systems, virtual environments, protein folding and human robot planning. See e.g. the books of Latombe [1] and Lavalle [2] for many interesting results.