Biochar can be used to reclaim nutrients from wastewater, and the nutrient loaded biochar can be regarded as a slow release fertilizer; however, the prerequisite of this concept is that the used biochar should have high adsorption capacities for nutrients. In addition, it remains challenging to tune the release rate of nutrients to coordinate with the uptake rate of plants. Herein, we report bentonite modified biochars derived from the copyrolysis of biomass and bentonite, which exhibit much higher phosphate adsorption capacities and superior P-slow release kinetics compared to that of the biochar without the modification of bentonite. The mechanistic study reveals that the improved adsorption and release performance of the as-prepared biochars originates from the presence of Ca and Mg in bentonite which leads to (1) the formation of desirable porous structure, (2) the reduced negative charge on the surface of the derived biochars, and (3) the formation of Ca and Mg related precipitations. Moreover, we demonstrate that the P-release kinetics of the as-prepared biochars can be precisely tuned by controlling the amount of bentonite, and a modified Fick model is developed to establish a quantitative relationship between biochars with different formulations and their P-release kinetics.
The quality of oral and maxillofacial surgery (OMS) significantly depends on the accuracy of surgical navigation. In this article, a vision-based markerless surgical navigation system is developed to overcome the shortcomings in the currently available technologies. Registration methods both for patient and surgical instrument tracking are improved to increase the navigation performance. For patient-image registration, we propose an efficient texture-less pose estimation method using only shape information. An innovative strategy is developed to effectively reject the outliers and improve the pose accuracy, which is the first attempt at introducing geometric matching information to guide PnP calculation. For surgical instrument tracking, a position-sensing marker is used to achieve robust and convenient instrument localization with high accuracy. Experiments were conducted on the 3-D-printed maxilla and mandible models to evaluate the navigation performance. Evaluation results validate the effectiveness of the proposed pose estimation method in improving the pose accuracy for texture-less teeth. Besides, it is revealed that the position-sensing marker can be localized with high accuracy even under nonideal visibility conditions, which expands the motion range of the instrument and decreases the size of the tool. The entire system has a sufficiently small target registration error (TRE). These experimental results have verified that the proposed surgical navigation system can provide practical guidance for OMS with satisfactory accuracy.
Abstract This article investigates the stability of nonlinear uncertain distributed delay system via integral‐based event‐triggered impulsive control (IETIC) strategy. First, a IETIC mechanism is presented to reduce the redundant data transmission over the system, in which the integral‐based event‐triggered mechanism uses the integration of system states over a time period in the past. Second, a new lemma is proposed to eliminate the Zeno behavior of the established model through the IETIC mechanism. Third, a novel Lyapunov–Krasovskii functional (LKF) method related to probability density function is constructed to guarantee the stability of the established model based on LMI conditions, where a probability density function is introduced as a distributed delay kernel. Compared with existing methods, the constructed novel LKF method is less conservative or requiring less number of decision variables. Numerical examples are further provided to confirm the effectiveness and advantages of the proposed approach.
Information-based autonomous robot exploration methods, aiming to maximize the exploration rewards, e.g., mutual information (MI), get more prevalent in field robotics applications. However, most MI-based exploration methods assume known poses or use inaccurate pose uncertainty approximation, which may lead to deviation or even failure when exploring prior unknown environments. In this paper, we explicitly consider full-state (pose & map) uncertainty for balancing exploration and localizability, i.e., avoiding the robot guiding itself to complex scenes with high exploration rewards but hard to localize. We first propose a Rao-Blackwellized particle filter-based localization and mapping framework (RBPF-CLAM) for a dense environmental map with continuous occupancy distribution. Then we develop a new closed-form particle weighting method to improve the localization accuracy and robustness. We further use these weighted particles to approximate the unknown pose uncertainty and combine it with our previous confidence-rich mutual information (CRMI) metric to evaluate the expected information utility of the robot's new control actions. This new information metric is called uncertain CRMI (UCRMI). Dataset experiments show our RBPF-CLAM improves about 44.7% average root mean square error than the state-of-the-art RBPF localization method, and real-world experimental results show that our UCRMI reduces the pose uncertainty about 32.85% more than CRMI and 25.36% time cost than UGPVR in the exploration of unknown and unstructured scenes given sparse measurements, which shows better performance than other state-of-the-art information metrics. Note to Practitioners —This work was motivated by the problem of ' planning for state estimation ' for a range-sensing robot, i.e., the robot can choose a better future place to facilitate its localization more accurately and explore new areas rationally to gather more information. Existing methods mainly assume the robot's poses during the exploration can be estimated by an independent localization approach or simply propagated via a predefined probabilistic distribution. However, localization failure would lead to higher planning deviation for the planner that does not consider the pose uncertainty, and manually set parametric distribution is more prone to overestimate the pose uncertainty. This paper proposes an RBPF-based localization and mapping scheme and an improved particle weight update method in a confidence-rich map, then uses the weighted particles to approximate trajectory entropy and combines it with CRMI to evaluate the expected information gain of a candidate action/node. Our newly defined information function 'UCRMI' can prevent the robot from exploring too aggressively without considering its localizability in prior unknown and unstructured environments. These scenes may lack robust features to conduct feature-based SLAM or lack accurate external localization information such as GPS. This method can be applied in underwater, planetary, and subterranean robot exploration tasks, even using low-resolution sensors. Future work mainly involves adapting UCRMI to applications in large-scale scenes using small autonomous platforms.
The path tracking of the robotic fish is a hotspot with its high maneuverability and environmental friendliness. However, the periodic oscillation generated by bionic fish-like propulsion mode may lead to unstable control. To this end, this article proposes a novel framework involving a newly designed platform and multiagent reinforcement learning (MARL) method. First, a bionic robotic fish equipped with a reaction wheel is developed to enhance the stability. Second, an MARL-based control framework is proposed for the cooperative control of tail-beating and reaction wheel. Correspondingly, a hierarchical training method including initial training and iterative training is designed to deal with the control coupling and frequency difference between two agents. Finally, extensive simulations and experiments indicate that the developed robotic fish and the proposed MARL-based control framework can effectively improve the accuracy and stability of path tracking. Remarkably, headshaking is reduced about 40%. It provides a promising reference for the stability optimization and cooperative control of bionic swimming robots featuring oscillatory motions.
In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications.
This paper presents a novel closed-loop method for a multilink robotic fish to mimic the C-start maneuver, in which the turning speed and precision are emphasized. The turning speed is maximized by carefully designed preparatory stage, and the turning precision is achieved by the feedback of the turning angle from a gyroscope and a new design of the propulsive stage. Different types of C-starts are studied, due to the different sizes of caudal fins, in order to achieve the highest turning angular velocity. All the proposed types of C-starts are experimented and compared using a four-joint robotic fish. The experimental results show a fastest angular velocity up to 200°/s and the distinctions between all these different types.
To improve environmental information gathering of intelligent vehicles in unknown scenes, this brief presents a hierarchical online informative path planning (IPP) framework containing global action optimization and local path planning. Particularly, we propose a lightweight kernel-based Bayesian optimization for IPP (KBO-IPP) to facilitate highly efficient information utility evaluation and decision-making of control actions. Specifically, KBO-IPP can infer the exact environmental mutual information (MI) and associated uncertainties with an approximate logarithmic complexity, eliminating the need for explicit model training. We develop a new information-theoretic objective function consisting of travel cost and predicted MI values with uncertainties to achieve the balance between high MI values (exploitation) and high prediction variances (exploration). To enhance the optimality of IPP, the past unselected informative actions are also incorporated into the global Bayesian optimization. Online real-world experiments validate that our proposed method shows higher efficiency with comparable performance to modern methods in unknown, complex environments.
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