To the best of our knowledge, an exact solution to the visibility-based pursuit–evasion problem with point agents and polygonal obstacles addressed in this work is not known. Given the above, in this work, we present approximate algorithms, but feasible and with other desirable properties, for such a pursuit–evasion game. Our new method combines asymptotically optimal motion planning based on sampling (more specifically, optimal probabilistic roadmaps) and the value iteration of dynamic programming. In addition, our formulation finds solutions for the evader when there are singular surfaces, which previous work could not find. In this work, we obtain two main theoretical results. We first prove that the proposed discrete formulation is correct (that the method obtains the solution for the discretization of the given configuration space). Subsequently, combining random graph results, Bellman's optimality principle, and limits, it is proved that, as the number of samples tends to infinity, our approximate discrete formulation becomes the continuous formulation corresponding to the Hamilton–Jacobi–Isaacs equation. This results in a feasible method that improves its approximation as the number of samples increases. Simulation experiments in 2-D and 3-D environments with obstacles that are simply and multiplicattively connected exemplify the results of the new method and show the advantages over previous work.

In this paper, we show that Virtual Reality (VR) sickness is associated with a reduction in attention, which was detected with the P3b Event-Related Potential (ERP) component from electroencephalography (EEG) measurements collected in a dual-task paradigm. We hypothesized that sickness symptoms such as nausea, eyestrain, and fatigue would reduce the users' capacity to pay attention to tasks completed in a virtual environment, and that this reduction in attention would be dynamically reflected in a decrease of the P3b amplitude while VR sickness was experienced. In a user study, participants were taken on a tour through a museum in VR along paths with varying amounts of rotation, shown previously to cause different levels of VR sickness. While paying attention to the virtual museum (the primary task), participants were asked to silently count tones of a different frequency (the secondary task). Control measurements for comparison against the VR sickness conditions were taken when the users were not wearing the Head-Mounted Display (HMD) and while they were immersed in VR but not moving through the environment. This exploratory study shows, across multiple analyses, that the effect mean amplitude of the P3b collected during the task is associated with both sickness severity measured after the task with a questionnaire (SSQ) and with the number of counting errors on the secondary task. Thus, VR sickness may impair attention and task performance, and these changes in attention can be tracked with ERP measures as they happen, without asking participants to assess their sickness symptoms in the moment.

In this article, we study properties of local planners for nonholonomic dynamical systems to achieve asymptotic global optimality in a sampling-based planner, namely, the RRT*. Formal results are presented concerning length optimal trajectories under a nonholonomic metric, more specifically, an analysis about necessary or sufficient conditions for the local planners is provided. The analysis makes use of the case of finding time-optimal trajectories for differential drive robots. Later, relevant concepts for complex robotic systems such as detachability and time dominance are introduced. Detachability is a property over the cost functional such that, if respected, it allows the reusability of previously designed local planners for simpler systems into more complex ones. The introduced concepts are further illustrated through a robotic system comprised of a mobile manipulator robot equipped with a camera in the arm’s end-effector, whose task is to move between states maintaining a desired object within its field of view. Experiments in a physical mobile manipulator robot are shown, which validate the proposed theoretical modeling.

This paper considers how the motions of a telepresence robot moving autonomously affect a person immersed in the robot through a head-mounted display. In particular, we explore the preference, comfort, and naturalness of elements of piecewise linear paths compared to the same elements on a smooth path. In a user study, thirty-six subjects watched panoramic videos of three different paths through a simulated museum in virtual reality and responded to questionnaires regarding each path. Preference for a particular path was influenced the most by comfort, forward speed, and characteristics of the turns. Preference was also strongly associated with the users’ perceived naturalness, which was primarily determined by the ability to see salient objects, the distance to the walls and objects, as well as the turns. Participants favored the paths that had a one meter per second forward speed and rated the path with the least amount of turns as the most comfortable.

Differential drive robots, such as robotic vacuums, often have at least two motion primitives: the ability to travel forward in straight lines, and rotate in place upon encountering a boundary. They are often equipped with simple sensors such as contact sensors or range finders, which allow them to measure and control their heading angle with respect to environment boundaries. We aim to find minimal control schemes for creating stable, periodic “patrolling” dynamics for robots that drive in straight lines and “bounce” off boundaries at controllable angles. As a first step toward analyzing high-level mobile robot dynamics in more general environments, we analyze the location and stability of periodic orbits in regular polygons. The contributions of this paper are: 1) proving the existence of periodic trajectories in n-sided regular polygons and showing the range of bounce angles that will produce such trajectories; 2) an analysis of their stability and robustness to modeling errors; and 3) a closed form solution for the points where the robot collides with the environment boundary while patrolling. We present simulations confirming our theoretical results.

In this paper, we address the pursuit/evasion problem of capturing an omnidirectional evader using a Differential Drive Robot (DDR) in an obstacle-free environment. The goal of the evader is to keep the pursuer farther than the capture distance for as long as possible and for the pursuer the goal is to capture the evader as soon as possible. In [1] an open-loop time-optimal strategy is proposed for this pursuit/evasion problem. In [2] a state feedback-based time-optimal motion policy for the DDR is provided. The time-optimal strategies obtained in [1] are in Nash equilibrium, meaning that any unilateral deviation of a player from the optimal strategies does not provide it a benefit in its payoff. However, Nash equilibrium does not tell if one player deviates from its optimal policy then, does there exist a new strategy for the other player that can take advantage of such deviation? If so, which is the required information to improve the payoff compared with the worst case scenario? In this paper we address those questions, analysing the scenario in which the players deviate from their optimal controls. We show that when the evader deviates from its optimal speed there are cases where there exists a new pursuer motion strategy that reduces the time to capture the evader. The shown cases where the time to capture the evader is reduced require more information about the evader's state. Nevertheless, there are also cases in which despite the availability of new information, the pursuer must stick to the worst case strategy, otherwise it cannot capture the evader.

This paper investigates an object detection problem using a mobile robot equipped with a vision sensor. The main novelty of this work is an approach that combines localization of the robot relative to an object believed to be the target and confirmation of this object's identity. Since the position of the robot relative to the candidate target is never exactly known, we model this position by a probability distribution over a set of cells forming a decomposition of the workspace around the candidate target. By performing a series of moves the robot acquires several images and runs a target detector module on each image. Its goal is not only to reach a position where the target detector can confirm the target with high confidence (as this approach would be prone to false positives). It is also to reach a position where, with high probability, the target detector will confirm with high confidence that the candidate target is actually the target. This twofold goal reduces drastically the likelihood of false positives. The target confirmation problem is modeled as a Partially-Observable Markov Decision Process (POMDP), which is solved using Stochastic Dynamic Programming (SDP).

This paper is concerned with determining whether a mobile robot, called the pursuer, is up to maintaining visibility of an antagonist agent, called the evader. This problem, a variant of pursuit-evasion, has been largely studied, following a systematic treatment by increasingly relaxing a number of restrictions. In, we considered a scenario where the pursuer and the evader move at bound speed, traveling around a known, 2D environment, which contains obstacles. Then, considering that, in an attempt to escape, the evader travels the shortest path to reach a potential escape region, we provided a decision procedure that determines whether or not the pursuer is up to maintain visibility of the evader and obtained complexity measures of this surveillance task. In this paper, we prove that there are cases for which an evader may escape only if it does not travel the shortest path to an escapable region. We introduce planning strategies for the movement of the pursuer that keeps track of the evader, even if the evader chooses not to travel the shortest path to an escape region. We also present a sufficient condition for the evader to escape that does not depend on the initial positions of the players. It can be verified only using the environment. All our algorithms have been implemented and we show simulation results.