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In recent decades, the Timed Elastic Band (TEB) algorithm is widely used for the AGV local path panning because of its convenient and efficiency. However, it may make a local detour when encountering a curve turn and cause excessive energy consumption. To solve this problem, this paper proposed an improved TEB algorithm to make the AGV walk along the wall when turning, which shortens the planning time and saves energy. Experiments were implemented in the Rviz visualization tool platform of the robot operating system (ROS). Simulated experiment results reflect that an amount of 5% reduction in the planning time has been achieved and the velocity curve implies that the operation was relatively smooth. Practical experiment results demonstrate the effectiveness and feasibility of the proposed method that the robots can avoid obstacles smoothly in the unknown static and dynamic obstacle environment.

This paper presents a fusion algorithm based on the enhanced RRT* TEB algorithm. The enhanced RRT* algorithm is utilized for generating an optimal global path. Firstly, proposing an adaptive sampling function and extending node bias to accelerate global path generation and mitigate local optimality. Secondly, eliminating path redundancy to minimize path length. Thirdly, imposing constraints on the turning angle of the path to enhance path smoothness. Conducting kinematic modeling of the mobile robot and optimizing the TEB algorithm to align the trajectory with the mobile robot's kinematics. The integration of these two algorithms culminates in the development of a fusion algorithm. Simulation and experimental results demonstrate that, in contrast to the traditional RRT* algorithm, the enhanced RRT* algorithm achieves a 5.8% reduction in path length and a 62.5% decrease in the number of turning points. Utilizing the fusion algorithm for path planning, the mobile robot generates a superior, seamlessly smooth global path, adept at circumventing obstacles. Furthermore, the local trajectory meticulously conforms to the kinematic constraints of the mobile robot.

High-precision positioning and multi-target detection have been proposed as key technologies for robotic path planning and obstacle avoidance. First, the Cartographer algorithm was used to generate high-quality maps. Then, the iterative nearest point (ICP) and the occupation probability algorithms were combined to scan and match the local point cloud, and the positions and attitudes of the robot were obtained. Furthermore, Sparse Matrix Pose Optimization was carried out to improve the positioning accuracy. The positioning accuracy of the robot in x and y directions was kept within 5 cm, the angle error was controlled within 2°, and the positioning time was reduced by 40%. An improved timing elastic band (TEB) algorithm was proposed to guide the robot to move safely and smoothly. A critical factor was introduced to adjust the distance between the waypoints and the obstacle, generating a safer trajectory, and increasing the constraint of acceleration and end speed; thus, smooth navigation of the robot to the target point was achieved. The experimental results showed that, in the case of multiple obstacles being present, the robot could choose the path with fewer obstacles, and the robot moved smoothly when facing turns and approaching the target point by reducing its overshoot. The proposed mapping, positioning, and improved TEB algorithms were effective for high-precision positioning and efficient multi-target detection.

The semi-structured vineyard environments contain numerous irregular obstacles, posing stringent requirements on the navigational safety and trajectory tracking accuracy of mobile robots. To address this challenge, this study first optimizes the A* algorithm at the global planning layer by incorporating a composite turning-cost evaluation model and a heuristic dynamic weighting strategy, thereby effectively enhancing search efficiency and path smoothness. Building upon this, a local planning method is further developed by integrating an adaptive sampling mechanism with high-order interpolation-based kinematic continuity constraints and a heading-rate-driven velocity smoothing strategy. This enables the robot to maintain a safe clearance from obstacles in dynamic environments, thereby significantly enhancing the smoothness of obstacle avoidance maneuvers. Both simulation and field experiment results demonstrate that the improved global planning algorithm reduces the number of critical turning points and the total turning angle by up to 18.0%. Across three typical path scenarios, the proposed fusion method reduces the robot’s positional deviation by up to 21.8% and the heading angle deviation by up to 29.6%, while concurrently increasing the safe clearance from obstacles by 42.0%. These findings suggest that the proposed framework establishes a viable algorithmic foundation for improving the navigation accuracy, obstacle avoidance stability, and operational safety.

To address issues such as the confusion of environmental feature points and significant pose information errors in chili fields, an autonomous navigation system based on multi-sensor data fusion and an optimized TEB (Timed Elastic Band) algorithm is proposed. The system’s positioning component integrates pose data from the GNSS and the IMU inertial navigation system, and corrects positioning errors caused by the clutter of LiDAR environmental feature points. To solve the problem of local optimization and excessive collision handling in the TEB algorithm during the path planning phase, the weight parameters are optimized based on environmental characteristics, thereby reducing errors in optimal path determination. Furthermore, considering the topographic inclination between rows (5–15°), 10 sets of comparison tests were conducted. The results show that the navigation system reduced the average path length by 0.58 m, shortened the average time consumption by 2.55 s, and decreased the average target position offset by 4.3 cm. In conclusion, the multi-sensor data fusion and optimized TEB algorithm demonstrate significant potential for realizing autonomous navigation in the narrow and complex environment of chili fields.

TP242%TP18; 针对混合A?算法规划的路径不够平滑且效率低的问题,提出优化混合A?算法的评价函数,引入角度惩罚系数的方法,使路径更加平滑,并引入最优步长的节点扩展方法以提高搜索效率.首先,将混合 A?算法与 TEB(Timed Elastic Band,时间弹性带)算法结合,生成混合路径规划算法.然后,基于 ROS(Robot Operating System,机器人操作系统)对阿克曼转向机器人的路径规划过程进行仿真.结果表明:使用混合A?算法+DWA(动态窗口法)比使用A?算法+DWA算法的路径规划平均距离少23.83%;使用混合A?算法+TEB算法比使用A?算法+TEB算法的路径规划平均距离少22.49%;使用A?算法+TEB算法比使用A?算法+DWA算法的路径规划平均时间少 6.99%;使用混合A?算法+TEB算法比使用混合A?算法+DWA算法的路径规划平均时间少6.25%.混合A?算法结合TEB算法的路径规划效率更高,规划出的路径更短,验证了混合路径规划算法的有效性和优越性.最后,在仓储环境中进行了阿克曼转向机器人路径规划实验,针对机器人实际运行轨迹与规划的轨迹有较大误差的问题,提出通过卡尔曼滤波对障碍物的位姿进行估计,并对机器人的轨迹进行跟踪的方法.结果表明:卡尔曼滤波的跟踪误差在0.2m以内.

Multi-robot motion planning (MRMP) requires each robot to possess strong local planning capabilities while maintaining global consistency. However, existing research often fails to address both global and local planning simultaneously, resulting in conflicts in real-time path execution. The A* algorithm is widely used for global path planning due to its adaptability and search efficiency, while the Timed Elastic Band (TEB) algorithm excels in local trajectory optimization and real-time dynamic obstacle avoidance. This paper presents a novel motion planning framework integrating an improved A* algorithm with an enhanced TEB strategy to address both levels of planning collaboratively. The proposed improvements include the introduction of steering costs and dynamic weights into the A* algorithm to enhance path smoothness and efficiency, and a hierarchical obstacle treatment in TEB for improved local avoidance. Simulation and real-world experiments conducted with ROS confirmed the feasibility and effectiveness of the method. Compared to the traditional A* algorithm, the proposed framework reduces the average path length by 5.2%, shortens completion time by 11.5%, and decreases inflection points by 66.7%, demonstrating superior performance for multi-robot systems in dynamic environments.

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Oceanic and Atmospheric Administration-20 (NOAA-20) and the Suomi National Polar-orbiting Partnership Program (S-NPP) satellites were launched in late 2017 and 2011, respectively. This paper presents a recent update in the VIIRS thermal emissive bands (TEB) on-orbit calibration algorithm and inter-compares long-term instrument and TEB sensor data records (SDR) performances of the two VIIRS, to support user communities. The VIIRS TEB calibration algorithm was improved to mitigate calibration biases during the blackbody warm-up/cool-down (WUCD) events. Four WUCD bias correction methods were implemented in the NOAA operational processing in 2019: (1) the Nominal-F method, (2) the WUCD-C method, (3) the Ltrace method, and (4) the Ltrace-2 method. Our evaluation results indicate that the on-orbit performances of the two VIIRS instruments have been generally stable and comparable with each other, except that NOAA-20 VIIRS blackbody and instrument temperatures are lower than those of the S-NPP VIIRS. The degradations in the S-NPP TEB detector responsivities remain small after 9 years on-orbit. NOAA-20 detector responsivities have been generally stable after the longwave infrared degradation during its early mission was resolved by the mid-mission outgassing. NOAA-20 and S-NPP VIIRS TEB SDRs agree with co-located Cross-track Infrared Sounder observations, with daily averaged biases within 0.1 K at nadir. After the implementation of operational WUCD bias correction, residual TEB WUCD biases are similar for NOAA-20 and S-NPP, with daily averaged biases ~0.01 K in all bands.

The photovoltaic (PV) bands on the mid-wave and long-wave infrared (MWIR and LWIR) cold focal plane assemblies of Terra and Aqua MODIS have suffered from gradually increasing electronic crosstalk contamination as both instruments have continued to operate in their extended missions, respectively. This contamination has considerable impact, particularly for the PV LWIR bands, which includes image striping and radiometric bias in the Level-1B (L1B)-calibrated radiance products as well as higher level (and mostly atmospheric but also land and oceanic) products (e.g., cloud phase particle, cloud mask, land and sea surface temperatures). The crosstalk was characterized early in the mission, and test corrections were developed then. Ultimately, the groundwork for a robust electronic crosstalk correction algorithm was developed in 2016 and implemented in MODIS Collection 6.1 (C6.1) back in 2017 for the Terra MODIS PV LWIR bands. It was later introduced in Aqua MODIS C6.1 for the same group of bands in April 2022. Additional improvements were made in MODIS Collection 7 (C7) to better characterize the electronic crosstalk in the PV LWIR bands, and the electronic crosstalk correction algorithm was also extended to select detectors in the MODIS MWIR bands. This work will describe the electronic crosstalk correction algorithm and its application on the MODIS L1B product, the differences in application between C6.1 and C7, as well as additional improvements made to enhance the contamination correction and improve image quality for the Aqua MODIS PV LWIR bands. The electronic crosstalk correction coefficient time series for the MODIS PV bands will be discussed, and some cases will be presented to illustrate how image quality improves on the L1B and Level 2 products after the correction is applied. Lastly, experiences gained regarding the PV bands electronic crosstalk and the strategy used to correct it will be discussed to provide future data users and scientists with an insight as to how to improve on the legacy record that the Terra and Aqua MODIS sensors will leave behind after both spacecrafts are decommissioned.

Existing trajectory planning methods often face challenges in ensuring stable robot motion control, leading to significant positional errors during navigation. This study proposes Timed-Elastic-Band-Based Variable Splitting (TEB-VS), a novel framework that integrates variable splitting (VS)—a constrained optimization technique—with the classical Timed-Elastic-Band (TEB) algorithm. Unlike incremental modifications to TEB, TEB-VS introduces a systematic combination of VS and TEB to decompose non-convex global constraints into tractable subproblems while leveraging symmetry principles for balanced multi-objective control (e.g., velocity, acceleration, and obstacle avoidance). Experimental results demonstrate that TEB-VS achieves a 46.5% improvement in motion stability over traditional TEB in obstacle-free simulations and a 37% enhancement in dynamic obstacle scenarios. Real-world tests show a 26.7% reduction in angular velocity oscillations, with computational efficiency comparable to TEB. The framework’s effectiveness in harmonizing trajectory smoothness and dynamic adaptability is validated through extensive simulations and TurtleBot2 experiments.