Comparative Study of SLAM Techniques for UAV

In the last few decades, the main problem which has attracted the attention of researchers in the field of aerial robotics is the position estimation or Simultaneously Localization and Mapping (SLAM) of aerial vehicles where the GPS system does not work. Aerial robotics are used to perform many tasks such as rescue, transportation, search, control, monitoring, and different military operations where the performance of humans is impossible because of their vast top view and reachability anywhere. There are many different techniques and algorithms which are used to overcome the localization and mapping problem. These techniques and algorithms use different sensors such as Red Green Blue and Depth (RGBD), Light Detecting and Range (LIDAR), Ultra-Wideband (UWB) techniques, and probability-based SLAM which uses two algorithms Linear Kalman Filter (LKF) and Extended Kalman filter (EKF). LKF consists of 5 phases and this algorithm is only used for linear system problems but on the other hand, EKF algorithm is also used for non-linear system. EKF is found better than LKF due to accuracy, practicality, and efficiency while dealing SLAM problem.

A novel speed optimisation scheme for unmanned sailboats by sliding mode extremum seeking control without steady-state oscillation

This paper proposes a novel speed optimisation scheme for unmanned sailboats by sliding mode extremum seeking control (SMESC) without steady-state oscillation. In the sailing speed optimisation scheme, an initial sail angle of attack is first computed by a piecewise constant function in the feed forward block, which ensures a small deviation between sailing speed and the maximum speed. Second, the sailing speed approaches to maximum gradually by extremum search control (ESC) in the feedback block. In SMESC without steady-state oscillation, a switching law is designed to carry out the control transformation, so that the speed optimisation system carries out SMESC in the first convergence phase and ESC without steady-state oscillation in the second stability phase. This scheme combines the advantages of both control algorithms to maintain a faster convergence rate and to eliminate steady-state oscillation. Furthermore, the strict stability of the speed optimisation system is proved in this paper. Finally, we test a 12-m mathematical model of an unmanned sailboat in the simulation to demonstrate the effectiveness and robustness of this speed optimisation scheme.

Analysis of the Impact of Randomization of Search-Control Parameters in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) has been applied successfully in many domains, including games. However, its performance is not uniform on all domains, and it also depends on how parameters that control the search are set. Parameter values that are optimal for a task might be sub-optimal for another. In a domain that tackles many games with different characteristics, like general game playing (GGP), selecting appropriate parameter settings is not a trivial task. Games are unknown to the player, thus, finding optimal parameters for a given game in advance is not feasible. Previous work has looked into tuning parameter values online, while the game is being played, showing some promising results. This tuning approach looks for optimal parameter values, balancing exploitation of values that performed well so far in the search and exploration of less sampled values. Continuously changing parameter values while performing the search, combined also with exploration of multiple values, introduces some randomization in the process. In addition, previous research indicates that adding randomization to certain components of MCTS might increase the diversification of the search and improve the performance. Therefore, this article investigates the effect of randomly selecting values for MCTS search-control parameters online among predefined sets of reasonable values. For the GGP domain, this article evaluates four different online parameter randomization strategies by comparing them with other methods to set parameter values: online parameter tuning, offline parameter tuning and sub-optimal parameter choices. Results on a set of 14 heterogeneous abstract games show that randomizing parameter values before each simulation has a positive effect on the search in some of the tested games, with respect to using fixed offline-tuned parameters. Moreover, results show a clear distinction between games for which online parameter tuning works best and games for which online randomization works best. In addition, the overall performance of online parameter randomization is closer to the one of online parameter turning than the one of sub-optimal parameter values, showing that online randomization is a reasonable parameter selection strategy. When analyzing the structure of the search trees generated by agents that use the different parameters selection strategies, it is clear that randomization causes MCTS to become more explorative, which is helpful for alignment games that present many winning paths in their trees. Online parameter tuning, instead, seems more suitable for games that present narrow winning paths and many losing paths.

Privacy-Preserving Attribute-Based Keyword Search with Traceability and Revocation for Cloud-Assisted IoT

With the rapid development of cloud computing and Internet of Things (IoT) technology, it is becoming increasingly popular for source-limited devices to outsource the massive IoT data to the cloud. How to protect data security and user privacy is an important challenge in the cloud-assisted IoT environment. Attribute-based keyword search (ABKS) has been regarded as a promising solution to ensure data confidentiality and fine-grained search control for cloud-assisted IoT. However, due to the fact that multiple users may have the same retrieval permission in ABKS, malicious users may sell their private keys on the Internet without fear of being caught. In addition, most of existing ABKS schemes do not protect the access policy which may contain privacy information. Towards this end, we present a privacy-preserving ABKS that simultaneously supports policy hiding, malicious user traceability, and revocation. Formal security analysis shows that our scheme can not only guarantee the confidentiality of keywords and access policies but also realize the traceability of malicious users. Furthermore, we provide another more efficient construction for public tracing.

Search strategy in a complex and dynamic environment: the MH370 case

Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March 8, 2014. In this paper, we propose an improvement of a search algorithm rooted in the ergodic theory of dynamical systems which can accommodate complex geometries and uncertainties of the drifting search areas on the ocean surface. We illustrate the effectiveness of this algorithm in a computational replication of the conducted search for MH370. We compare the algorithms using many realizations with random initial positions, and analyze the influence of the stochastic drift on the search success. In comparison to conventional search methods, the proposed algorithm leads to an order of magnitude improvement in success rate over the time period of the actual search operation. Simulations of the proposed search control also indicate that the initial success rate of finding debris increases in the event of delayed search commencement. This is due to the existence of convergence zones in the search area which leads to local aggregation of debris in those zones and hence reduction of the effective size of the area to be searched.

Hill Climbing on Value Estimates for Search-control in Dyna

Dyna is an architecture for model based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna is search control, the mechanism to generate the state and action from which the agent queries the model, which remains largely unexplored. In this work, we propose to generate such states by using the trajectory obtained from Hill Climbing (HC) the current estimate of the value function. This has the effect of propagating value from high value regions and of preemptively updating value estimates of the regions that the agent is likely to visit next. We derive a noisy projected natural gradient algorithm for hill climbing, and highlight a connection to Langevin dynamics. We provide an empirical demonstration on four classical domains that our algorithm, HC Dyna, can obtain significant sample efficiency improvements. We study the properties of different sampling distributions for search control, and find that there appears to be a benefit specifically from using the samples generated by climbing on current value estimates from low value to high value region.