Navigation Policy Research Articles

Motion Planning with Gamma-Harmonic Potential Fields

This paper extends the capabilities of the harmonic potential field (HPF) approach to planning. The extension covers the situation where the workspace of a robot cannot be segmented into geometrical subregions where each region has an attribute of its own. The suggested approach uses a task-centered, probabilistic descriptor of the workspace as an input to the planner. This descriptor is processed, along with a goal point, to yield the navigation policy needed to steer the agent from any point in its workspace to the target. The approach is easily adaptable to planning in a cluttered environment containing a vector drift field. The extension of the HPF approach is based on the physical analogy with an electric current flowing in a nonhomogeneous conducting medium. The resulting potential field is known as the gamma-harmonic potential (GHPF). Proofs of the ability of the modified approach to avoid zero-probability (definite threat) regions and to converge to the goal are provided. The capabilities of the planer are demonstrated using simulation.

Swarm intelligence routing approach in networked robots

Robot swarm combined with wireless communication has been a key driving force in recent few years and has currently expanded to wireless multihop networks, which include ad hoc radio networks, sensor networks, wireless mesh networks, etc. The aim of this paper is to propose an approach which introduces a polynomial time approximation path navigation algorithm and constructs dynamic state-dependent navigation policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize simultaneously two criteria: cumulative cost path and end-to-end delay path. The approach samples, estimates, and builds the model of pertinent aspects of the environment. It uses a model that combines both a stochastic planned prenavigation for the exploration phase and a deterministic approach for the backward phase. To show the robustness and performances of the proposed approach, simulation scenario is built through the specification of the interested network topology and involved network traffic between robots. For this, this approach has been compared to traditional optimal path routing policy.

Efficient vision-based navigation

In this article, we present a novel approach to learning efficient navigation policies for mobile robots that use visual features for localization. As fast movements of a mobile robot typically introduce inherent motion blur in the acquired images, the uncertainty of the robot about its pose increases in such situations. As a result, it cannot be ensured anymore that a navigation task can be executed efficiently since the robot's pose estimate might not correspond to its true location. We present a reinforcement learning approach to determine a navigation policy to reach the destination reliably and, at the same time, as fast as possible. Using our technique, the robot learns to trade off velocity against localization accuracy and implicitly takes the impact of motion blur on observations into account. We furthermore developed a method to compress the learned policy via a clustering approach. In this way, the size of the policy representation is significantly reduced, which is especially desirable in the context of memory-constrained systems. Extensive simulated and real-world experiments carried out with two different robots demonstrate that our learned policy significantly outperforms policies using a constant velocity and more advanced heuristics. We furthermore show that the policy is generally applicable to different indoor and outdoor scenarios with varying landmark densities as well as to navigation tasks of different complexity.

Securing frame communication in browsers

Many Web sites embed third-party content in frames, relying on the browser's security policy to protect against malicious content. However, frames provide insufficient isolation in browsers that let framed content navigate other frames. We evaluate existing frame navigation policies and advocate a stricter policy, which we deploy in the open-source browsers. In addition to preventing undesirable interactions, the browser's strict isolation policy also affects communication between cooperating frames. We therefore analyze two techniques for interframe communication between isolated frames. The first method, fragment identifier messaging, initially provides confidentiality without authentication, which we repair using concepts from a well-known network protocol. The second method, <code>postMessage</code>, initially provides authentication, but we discover an attack that breaches confidentiality. We propose improvements in the <code>postMessage</code> API to provide confidentiality; our proposal has been standardized and adopted in browser implementations.

MULTIMODAL SENSORY INTEGRATION AND CONCURRENT NAVIGATION STRATEGIES FOR SPATIAL COGNITION IN REAL AND ARTIFICIAL ORGANISMS

Flexible spatial behavior requires the ability to orchestrate the interaction of multiple parallel processes. At the sensory level, multimodal inputs must be combined to produce a robust description of the spatiotemporal properties of the environment. At the action-selection level, multiple concurrent navigation policies must be dynamically weighted in order to adopt the strategy that is the most adapted to the complexity of the task. Different neural substrates mediate the processing of spatial information. Elucidating their anatomo-functional interrelations is fundamental to unravel the overall spatial memory function. Here we first address the multisensory integration issue and we review a series of experimental findings (both behavioral and electrophysiological) concerning the neural bases of spatial learning and the way the brain builds unambiguous spatial representations from incoming multisensory streams. Second, we move at the navigation strategy level and present an overview of experimental data that begin to explain the cooperation-competition between the brain areas involved in spatial navigation. Third, we introduce the spatial cognition function from a computational neuroscience and neuro-robotics viewpoint. We provide an example of neuro-computational model that focuses on the importance of combining multisensory percepts to enable a robot to acquire coherent (spatial) memories of its interaction with the environment.

Learning to transfer optimal navigation policies

Autonomous agents that act in the real world utilizing sensory input greatly rely on the ability to plan their actions and to transfer these skills across tasks. The majority of path-planning approaches for mobile robots, however, solve the current navigation problem from scratch, given the current and goal configuration of the robot. Consequently, these approaches yield highly efficient plans for the specific situation, but the computed policies typically do not transfer to other, similar tasks. In this paper, we propose to apply techniques from statistical relational learning to the path-planning problem. More precisely, we propose to learn relational decision trees as abstract navigation strategies from example paths. Relational abstraction has several interesting and important properties. First, it allows a mobile robot to imitate navigation behavior shown by users or by optimal policies. Second, it yields comprehensible models of behavior. Finally, a navigation policy learned in one environment naturally transfers to unknown environments. In several experiments with real robots and in simulated runs, we demonstrate that our approach yields efficient navigation plans. We show that our system is robust against observation noise and can outperform hand-crafted policies.

Distributed visual navigation based on neural Q-learning for a mobile robot

Distributed visual navigation based on neural Q-learning for a mobile robot is studied in this paper. First, a general distributed structure based on the multiple processors for visual navigation is established according to the decomposition of the mobile robot visual navigation task. Second, in terms of the general distributed structure, the local environment description method based on the Peer Group Filtering (PGF) and fuzzy technology is put forward. Third, in each local environment description, a controller based on neural Q-learning is designed to guide the mobile robot navigation. In the last part of this paper, experimental simulations are done to test the effectiveness of the presented distributed algorithm, including the image segmentation, environment description and navigation policy.

Vietnam: Fisheries and navigation policies and issues

Abstract Vietnam claims a 12‐nautical‐mile territorial sea, a 12‐nautical‐mile contiguous zone, a continental shelf, a 200‐nautical‐mile exclusive economic zone (EEZ), historical waters encompassing most of the Gulf of Tonkin, and much of the Spratly Islands area. Vietnam's claimed boundaries overlap with those of China, Indonesia, Malaysia, Thailand, and Cambodia, and, in the Spratlys, with those of China, Taiwan, the Philippines, and Malaysia. The area claimed contains significant fisheries resources, and Vietnam has stipulated provisions for access to fish by foreign vessels. Yet issues pertaining to shared and migratory stocks remain to be addressed. Similarly, Vietnam has established regulations governing foreign ships navigating in Vietnamese‐claimed waters, including those designed to protect the environment. But some of these provisions do not conform to the provisions of the 1982 U.N. Convention on the Law of the Sea (UNCLOS). This article reviews Vietnam ‘s fisheries and navigation policies and issues.

On the Treatment of Inertial Measurement Unit Data in Optimal Linear Navigation Policies

The inclusion of the integrated nongravitational acceleration outputs from an inertial measurement unit in an optimal linear navigation policy is treated. This formulation appears to be considerably different nt from the treatments of IMU data for space vehicle navigation that have previously been discussed and promises to yield considerably better results in many instances. It has the advantage that the data are processed in an optimal fashion that makes the best use of knowledge of the general system characteristics. Further, the IMU enters the formulation in the same manner as any other sensor and, therefore, provides a more uniform method of treatment. Two methods of dealing ng with the IMU are discussed and then applied to a simple example. The advantages of the second formulation are observed to be quite significant.

FUTURE NAVIGATION POLICY IN THE ROYAL AIR FORCE*: Directorate ofNavigation, AirMinistry

NAVIGATIONVolume 1, Issue 3 p. 49-50 FUTURE NAVIGATION POLICY IN THE ROYAL AIR FORCE*: Directorate of Navigation, Air Ministry First published: September 1946 https://doi.org/10.1002/j.2161-4296.1946.tb01089.x † Excerpt from a paper presented at the Los Angeles meeting of the Institute, June 27, 1946, by Group Captain H. E. C. Boxer, RAF. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume1, Issue3September 1946Pages 49-50 RelatedInformation

One Hundred and Fifty Years of American Navigation Policy

I. Introduction, 238. — II. The Development of American Policy, 1789–1914, 240. — III. The Post-War Problem, 246. — IV. The Merchant Marine Act of 1936, 248. — V. Alternative Provision for Government Ownership, 256. — VI. The Experience of the Commission under the Act of 1936, 256. — VII. Conclusion, 258.