Geochemical properties of shells of Arctica islandica (Bivalvia) - implications for environmental and climatic change

Trace elemental concentrations of bivalve shells content a wealthy of environmental and climatic information of the past, and therefore the studies of trace elemental distributions in bivalve shells gained increasing interest lately. However, after more than half century of research, most of the trace elemental variations are still not well understood and trace elemental proxies are far from being routinely applicable. This dissertation focuses on a better understanding of the trace elemental chemistry of Arctica islandica shells from Iceland, and paving the way for the application of the trace elemental proxies to reconstruct the environmental and climatic changes. Traits of trace elemental concentrations on A. islandica shells were explored and evaluated. Then based the geochemical traits of the shells, four non-environmental/climatic controlling is indentified. (1) Trace elemental concentrations of bivalve shells are effected by early diagenesis by the leach or exchange of elemental ions, especially in shell tip part, even with the protection of periostrucum; (2) The analytical methods also affect the results of trace elemental concentrations, especially for the element, such as Mg, which is highly enriched in organic matrices; (3) Shell organic matrices are found play a dominating role on the concentration of trace elements on A. islandica shells. Most trace elements only occurred in insoluble organic matrices (IOM), although others are only found in the carbonate fraction. IOM of A. islandica shells is significantly enriched in Mg, while Li and Na are more deplete in IOM, but enriched in shell carbonate. Ba is more or less even contented in IOM and shell carbonate. The concentrations of certain elements vary between primary layer and secondary layer; (4) The vital /physiological controlling on trace elemental distributions of bivalve shells is also confirmed. Six elemental (B, Na, Mg, Mn, Sr, and Ba) concentrations show significant correlation (exponential functions) with ontogenetic age and shell grow rates (logarithmic equations). It is worthy to remark that B, Mg, Sr and Ba concentrations are negatively correlated with shell growth rate, positive with ontogenetic age, while the concentrations of Na and Mn show the opposite trends. At last, all the controlling described above can be taken into account and corrected to extract the environmental and climatic signal by a kind of standardization. The derived six exponential functions of the high correlations between six trace elemental concentrations and ontogenetic year are applied to make the standardization of these element-Ca ratios. The gotten standardized indices are compared with the variations of environmental and climatic parameters in this region, and many correlations are found. Standardized indices of Sr/Ca ratios are strongly related to the sun spot number, autumn NAO, autumn Europe surface air temperature (SAT) and Arctic sea surface temperature anomaly (TA), and those of Mg/Ca ratios are strongly associated with Arctic TA, Europe SAT and Solar variation (irradiance). The variations of autumn Europe SAT demonstrated more similarity with standardized indices of B/Ca than other parameters. Except for the SAT index of Arctic, the standardized indices of Na/Ca showed no distinct relation to temperature. European precipitation and the Arctic sea level pressure index compared well the Na/Ca ratios of the shells, and so did the autumn NAO. Standardized indices of Mn/Ca were correlated with the number of hurricanes in the North Atlantic, Northern Europe SAT and sun spot number

Modularized Control Synthesis for Complex Signal Temporal Logic Specifications

The control synthesis of a dynamic system subject to signal temporal logic (STL) specifications is commonly formulated as a mixed-integer linear programming (MILP) problem. Solving a MILP problem is computationally expensive when the STL formulas are long and complex. In this paper, we propose a framework to transform a long and complex STL formula into a syntactically separate form, i.e., the logical combination of a series of short and simple subformulas with non-overlapping timing intervals. Using this framework, one can easily modularize the synthesis of a complex formula using the synthesis solutions of the subformulas, which improves the efficiency of solving a MILP problem. Specifically, we propose a group of separation principles to guarantee the syntactic equivalence between the original formula and its syntactically separate counterpart. Then, we propose novel methods to solve the largest satisfaction region and the open-loop controller of the specification in a modularized manner. The efficacy of the methods is validated with a robot monitoring case study in simulation. Our work is promising to promote the efficiency of control synthesis for systems with complicated specifications

An online robot collision detection and identification scheme by supervised learning and Bayesian decision theory

This article is dedicated to developing an online collision detection and identification (CDI) scheme for human-collaborative robots. The scheme is composed of a signal classifier and an online diagnosor, which monitors the sensory signals of the robot system, detects the occurrence of a physical human-robot interaction, and identifies its type within a short period. In the beginning, we conduct an experiment to construct a data set that contains the segmented physical interaction signals with ground truth. Then, we develop the signal classifier on the data set with the paradigm of supervised learning. To adapt the classifier to the online application with requirements on response time, an auxiliary online diagnosor is designed using the Bayesian decision theory. The diagnosor provides not only a collision identification result but also a confidence index which represents the reliability of the result. Compared to the previous works, the proposed scheme ensures rapid and accurate CDI even in the early stage of a physical interaction. As a result, safety mechanisms can be triggered before further injuries are caused, which is quite valuable and important toward a safe human-robot collaboration. In the end, the proposed scheme is validated on a robot manipulator and applied to a demonstration task with collision reaction strategies. The experimental results reveal that the collisions are detected and classified within 20 ms with an overall accuracy of 99.6%, which confirms the applicability of the scheme to collaborative robots in practice

Automated Formation Control Synthesis from Temporal Logic Specifications

In this paper, we propose a novel framework using formal methods to synthesize a navigation control strategy for a multi-robot swarm system with automated formation. The main objective of the problem is to navigate the robot swarm toward a goal position while passing a series of waypoints. The formation of the robot swarm should be changed according to the terrain restrictions around the corresponding waypoint. Also, the motion of the robots should always satisfy certain runtime safety requirements, such as avoiding collision with other robots and obstacles. We prescribe the desired waypoints and formation for the robot swarm using a temporal logic (TL) specification. Then, we formulate the transition of the waypoints and the formation as a deterministic finite transition system (DFTS) and synthesize a control strategy subject to the TL specification. Meanwhile, the runtime safety requirements are encoded using control barrier functions, and fixed-time control Lyapunov functions ensure fixed-time convergence. A quadratic program (QP) problem is solved to refine the DFTS control strategy to generate the control inputs for the robots, such that both TL specifications and runtime safety requirements are satisfied simultaneously. This work enlights a novel solution for multi-robot systems with complicated task specifications. The efficacy of the proposed framework is validated with a simulation study

Automated Formation Control Synthesis from Temporal Logic Specifications

In this paper, we propose a novel framework using formal methods to synthesize a navigation control strategy for a multi-robot swarm system with automated formation. The main objective of the problem is to navigate the robot swarm toward a goal position while passing a series of waypoints. The formation of the robot swarm should be changed according to the terrain restrictions around the corresponding waypoint. Also, the motion of the robots should always satisfy certain runtime safety requirements, such as avoiding collision with other robots and obstacles. We prescribe the desired waypoints and formation for the robot swarm using a temporal logic (TL) specification. Then, we formulate the transition of the waypoints and the formation as a deterministic finite transition system (DFTS) and synthesize a control strategy subject to the TL specification. Meanwhile, the runtime safety requirements are encoded using control barrier functions, and fixed-time control Lyapunov functions ensure fixed-time convergence. A quadratic program (QP) problem is solved to refine the DFTS control strategy to generate the control inputs for the robots, such that both TL specifications and runtime safety requirements are satisfied simultaneously. This work enlights a novel solution for multi-robot systems with complicated task specifications. The efficacy of the proposed framework is validated with a simulation study

Average Communication Rate for Networked Event-Triggered Stochastic Control Systems

Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is a challenging problem due to the non-stationary nature of the system's stochastic processes. For such a system, a closed-loop effect emerges due to the interdependence between the system variable and the trigger of communication. This effect, commonly referred to as \textit{side information} by related work, distorts the stochastic distribution of the system variables and makes the ACR computation non-trivial. Previous work in this area used to over-simplify the computation by ignoring the side information and misusing a Gaussian distribution, which leads to approximated results. This paper proposes both analytical and numerical approaches to predict the exact ACR for a NET-SCS using a recursive model. Furthermore, we use theoretical analysis and a numerical study to qualitatively evaluate the deviation gap of the conventional approach that ignores the side information. The accuracy of our proposed method, alongside its comparison with the simplified results of the conventional approach, is validated by experimental studies. Our work is promising to benefit the efficient resource planning of networked control systems with limited communication resources by providing accurate ACR computation

Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation

Reinforcement learning demonstrates significant potential in automatically building control policies in numerous domains, but shows low efficiency when applied to robot manipulation tasks due to the curse of dimensionality. To facilitate the learning of such tasks, prior knowledge or heuristics that incorporate inherent simplification can effectively improve the learning performance. This paper aims to define and incorporate the natural symmetry present in physical robotic environments. Then, sample-efficient policies are trained by exploiting the expert demonstrations in symmetrical environments through an amalgamation of reinforcement and behavior cloning, which gives the off-policy learning process a diverse yet compact initiation. Furthermore, it presents a rigorous framework for a recent concept and explores its scope for robot manipulation tasks. The proposed method is validated via two point-to-point reaching tasks of an industrial arm, with and without an obstacle, in a simulation experiment study. A PID controller, which tracks the linear joint-space trajectories with hard-coded temporal logic to produce interim midpoints, is used to generate demonstrations in the study. The results of the study present the effect of the number of demonstrations and quantify the magnitude of behavior cloning to exemplify the possible improvement of model-free reinforcement learning in common manipulation tasks. A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications

Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving

Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment. Nevertheless, the reward function for an RL agent, which is significant to its performance, is challenging to be determined. The conventional work mainly focuses on rewarding safe driving states but does not incorporate the awareness of risky driving behaviors of the vehicles. In this paper, we investigate how to use risk-aware reward shaping to leverage the training and test performance of RL agents in autonomous driving. Based on the essential requirements that prescribe the safety specifications for general autonomous driving in practice, we propose additional reshaped reward terms that encourage exploration and penalize risky driving behaviors. A simulation study in OpenAI Gym indicates the advantage of risk-aware reward shaping for various RL agents. Also, we point out that proximal policy optimization (PPO) is likely to be the best RL method that works with risk-aware reward shaping