Biomimetic Intelligence and Robotics最新文献

Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the most promising regions, which significantly reduces planning time-consumption. The RRT# algorithm defines the Relevant Region based on the cost-to-come provided by the optimal forward-searching tree. However, it uses the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. To improve the path planning efficiency, we propose a batch sampling method that samples in a refined Relevant Region with a direct sampling strategy, which is defined according to the optimal cost-to-come and the adaptive cost-to-go, taking advantage of various sources of heuristic information. The proposed sampling approach allows the algorithm to build the search tree in the direction of the most promising area, resulting in a superior initial solution quality and reducing the overall computation time compared to related work. To validate the effectiveness of our method, we conducted several simulations in both $SE\left(2\right)$ and $SE\left(3\right)$ state spaces. And the simulation results demonstrate the superiorities of proposed algorithm.

Quadrotor unmanned aerial vehicles have become the most commonly used flying robots with wide applications in recent years. This paper presents a bioinspired control strategy by integrating the backstepping sliding mode control technique and a bioinspired neural dynamics model. The effects of both disturbances and system and measurement noises on the quadrotor unmanned aerial vehicle control performance have been addressed in this paper. The proposed control strategy is robust against disturbances with guaranteed stability proven by the Lyapunov stability theory. In addition, the proposed control strategy is capable of providing smooth control inputs under noises. Considering the modeling uncertainties, the adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates to improve tracking effectiveness. Finally, the simulation results demonstrate that the proposed control strategy provides satisfactory tracking performance for a quadrotor unmanned vehicle operating under disturbances and noises.

Early detection of vulnerable road users is a crucial requirement for autonomous vehicles to meet and exceed the object detection capabilities of human drivers. One of the most complex outstanding challenges is that of partial occlusion where a target object is only partially available to the sensor due to obstruction by another foreground object. A number of leading pedestrian detection benchmarks provide annotation for partial occlusion, however each benchmark varies greatly in their definition of the occurrence and severity of occlusion. Research demonstrates that a high degree of subjectivity is used to classify occlusion level in these cases and occlusion is typically categorized into 2–3 broad categories such as “partially” and “heavily” occluded. In addition, many pedestrian instances are impacted by multiple inhibiting factors which contribute to non-detection such as object scale, distance from camera, lighting variations and adverse weather. This can lead to inaccurate or inconsistent reporting of detection performance for partially occluded pedestrians depending on which benchmark is used. This research introduces a novel, objective benchmark for partially occluded pedestrian detection to facilitate the objective characterization of pedestrian detection models. Characterization is carried out on seven popular pedestrian detection models for a range of occlusion levels from 0%–99% to demonstrate the impact of progressive levels of partial occlusion on pedestrian detectability. Results show that the proposed benchmark provides more objective, fine grained analysis of pedestrian detection algorithms than the current state of the art.

Path planning is a crucial concern in the field of mobile robotics, particularly in complex scenarios featuring narrow passages. Sampling-based planners, such as the widely utilized probabilistic roadmap (PRM), have been extensively employed in various robot applications. However, PRM’s utilization of random node sampling often results in disconnected graphs, posing a significant challenge when dealing with narrow passages. In order to tackle this issue, we present equipotential line sampling strategy for probabilistic roadmap (EPL-PRM), a novel approach derived from PRM. This paper initially proposes a sampling potential field, followed by the construction of equipotential lines that are denser in the proximity of obstacles and narrow passages. Random sampling is subsequently conducted along these lines. Consequently, the sampling strategy enhances the likelihood of sampling nodes around obstacles and narrow passages, thereby addressing the issue of sparsity encountered in traditional sampling-based planners. Furthermore, we introduce a nodal optimization method based on an artificial repulsive field, which prompts sampled nodes to move in the direction of repulsion. As a result, nodes around obstacles are distributed more uniformly, while nodes within narrow passages gravitate toward the middle of the passages. Finally, extensive simulations are conducted to evaluate the proposed method. The results demonstrate that our approach achieves path planning with superior efficiency, lower cost, and higher reliability compared with traditional algorithms.

Digital display instrument identification is a crucial approach for automating the collection of digital display data. In this study, we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments. We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory (LSTM). First, the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure. Next, the digital display instrument identification network was constructed based on a convolutional neural network using two-way LSTM and Connectionist temporal classification (CTC) of indefinite length. Finally, an automatic calibration system for digital display instruments was built, and a multiclass digital display instrument dataset was constructed by sampling in the system. We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.

Minimal invasion is an important trend in surgery. However, the endoscope, as one of the key devices for monitoring the process of minimally invasive surgery, is limited by its size and working space it operates in, which result in a considerably narrow field of view. In particular, when a surgical instrument enters through the tool channel, the instrument occupies most of the area in an endoscopic image. This hampers the surgeon’s field of view and has a negative impact on the surgery. This study proposes a novel method for removing the occlusion caused by surgical instruments in endoscopic images by making foreground occlusions on endoscopic images transparent using image restoration and interframe information filling. Compared with unprocessed images, this method can provide a clearer field of view that is necessary for minimally invasive endoscopic surgeries and improve the quality of surgeries. Clinical endoscopic images are used to verify the feasibility of the proposed method, and the results show that the proposed method improves the visual effect of endoscopic images by removing surgical-instrument occlusions. This demonstrates the considerable potential of the proposed method for use in clinical applications.

Flexible electrohydrodynamic (EHD) pumps have been developed and applied in many fields due to no transmission structure, no wear, easy manipulation, and no noise. Physical simulation is often used to predict the output performance of flexible EHD pumps. However, this method neglects fluid–solid interaction and energy loss caused by flexible materials, which are both difficult to calculate when the flexible pumps deform. Therefore, this study proposes a flexible pump output performance prediction using machine learning algorithms. We used three different types of machine learning: random forest regression, ridge regression, and neural network to predict the critical parameters (pressure, flow rate, and power) of the flexible EHD pump. Voltage, angle, gap, overlap, and channel height are selected as five input data of the neural network. In addition, we optimized essential parameters in the three networks. Finally, we adopt the best predictive model and evaluate the significance of five input parameters to the output performance of the flexible EHD pumps. Among the three methods, the MLP model has exceptionally high accuracy in predicting pressure and flow. Our work is beneficial for the design process of fluid sources in flexible soft actuators and soft hydraulic sources in microfluidic chips.

Snake robots have great potential for exploring and operating in challenging unstructured environments, such as rubble, caves, and narrow pipelines. However, due to the complexity and unpredictability of unstructured environments, designing a controller that can achieve adaptive motion is crucial. This paper proposes a self-adaptive torque-based rolling controller for snake robots, enabling compliant motion in unstructured environments. First, a controller is designed to modify the torque of each motor by focusing on the different motion states of the rolling gait. Second, an experimental platform is established for snake robots to verify the effectiveness of the controller. Finally, a series of rolling experiments are conducted using the torque-based rolling controller. In conclusion, the self-adaptive torque-based rolling controller enhances snake robot adaptability and mobility.

Recently, bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps. However, these approaches endure performance degradation as problem complexity increases, often resulting in lengthy search times to find an optimal solution. This limitation is particularly critical for real-world applications like autonomous off-road vehicles, where high-quality path computation is essential for energy efficiency. To address these challenges, this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm, improved seagull optimization algorithm (iSOA) for rapid path planning of autonomous robots. A modified Douglas–Peucker (mDP) algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains. The resulting mDP-derived graph is then modeled using a Maklink graph theory. By applying the iSOA approach, the trajectory of an autonomous robot in the workspace is optimized. Additionally, a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints. The proposed model is validated through simulated experiments undertaken in various real-world settings, and its performance is compared with state-of-the-art algorithms. The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

Unmanned aerial vehicles (UAVs) lifelong flight is essential to accomplish various tasks, e.g., aerial patrol, aerial rescue, etc. However, traditional UAVs have limited power to sustain their flight and need skilled operators manually control their charging process. Manufacturers and users are eagerly seeking a reliable autonomous battery-changing solution. To address this need, we propose and design an autonomous battery-changing system for UAVs using the theory of inventive problem solving (TRIZ) and user-centered design (UCD) methods. For practical application, we employ UCD to thoroughly analyze user requirements, identify multiple pairs of technical contradictions, and solve these inconsistencies using TRIZ theory. Furthermore, we design an autonomous battery-changing hardware system that meets user requirements and realizes the battery change process after the automatic landing of UAVs. Finally, we conduct experiments to validate our system’s effectiveness. The experimental results show that our battery-changing system can implement the autonomous battery-changing task, and our system has high efficiency and robustness in the real environment.