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With the steady progress of China’s agricultural modernization, the demand for agricultural machinery for production is widely growing. Agricultural unmanned aerial vehicles (UAVs) are becoming a new force in the field of precision agricultural aviation in China. In those agricultural areas where ground-based machinery have difficulties in executing farming operations, agricultural UAVs have shown obvious advantages. With the development of precision agricultural aviation technology, one of the inevitable trends is to realize autonomous identification of obstacles and real-time obstacle avoidance (OA) for agricultural UAVs. However, the complex farmland environment and changing obstacles both increase the complexity of OA research. The objective of this paper is to introduce the development of agricultural UAV OA technology in China. It classifies the farmland obstacles in two ways and puts forward the OA zones and related avoidance tactics, which helps to improve the safety of aviation operations. This paper presents a comparative analysis of domestic applications of agricultural UAV OA technology, features, hotspot and future research directions. The agricultural UAV OA technology of China is still at an early development stage and many barriers still need to be overcome.

This paper presents an efficient 3D collision avoidance algorithm for fixed wing Unmanned Aerial Systems (UAS). The algorithm increases the ability of aircraft operations to complete mission goals by enabling fast collision avoidance of multiple obstacles. The new algorithm, which we have named Fast Geometric Avoidance algorithm (FGA), combines geometric avoidance of obstacles and selection of a critical avoidance start time based on kinematic considerations, collision likelihood, and navigation constraints. In comparison to a current way-point generation method, FGA showed a 90 % of reduction in computational time for the same obstacle avoidance scenario. Using this algorithm, the UAS is able to avoid static and dynamic obstacles while still being able to recover its original trajectory after successful collision avoidance. Simulations for different mission scenarios show that this method is much more efficient at avoiding multiple obstacles than previous methods. Algorithm effectiveness validation is provided with Monte Carlo simulations and flight missions in an aircraft simulator. FGA was also tested on a fixed-wing aircraft with successful results. Because this algorithm does not have specific requirements on the sensor data types it can be applied to cooperative and non-cooperative intruders.

Aiming at the problem of Unmanned Surface Vessel (USV) avoiding dynamic vessels on the sea surface, an Artificial Potential Field method based on Velocity Obstacle Method (VO-APF) was studied. Under the premise of complying with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the overlapping of ship domains is minimized. The Quaternion ship domain was introduced into VO to make it more suitable for collision avoidance judgment in the ship domain, and the collision avoidance time of the ship was predicted, and the reasonable obstacle avoidance point was finally obtained. The attraction function of the obstacle avoidance target point and the repulsion function of the ship domain based on the APF are set, and the USV is subjected to the repulsion force and attraction in the APF, and finally completes the whole dynamic ship collision avoidance task. In the simulation environment, VO, APF and VO-APF were compared in three different encounter situations. The results show that VO-APF can help USV to complete the collision avoidance task under the premise of complying with COLREGs, and the overlap time between USV’s own ship domain and the other ship domain is the shortest, and the safety degree is the highest.

This paper investigates a novel hybrid gait obstacle-avoidance control strategy based on a perception system for the six wheel-legged robot (BIT-6NAZA) in uneven terrain. This robot has stronger payload transportation performance benefited from the flexibility of the 6-degree of freedom Stewart platform. It can guarantee the attitude level stability when passing through different shapes of obstacles. Firstly, the motion state matrix and gait unit of the BIT-6NAZA robot are considered. Moreover, the current local terrain is identified by the visual perception system. Then the wheel-legged hybrid gait types and parameters are selected according to the terrain detection. The gait topology matrix and gait planning matrix are generated for each leg controller to realize the wheel-legged hybrid obstacle-avoidance. In addition, a feedback controller combined with the posture sensor and foot-end force sensor is utilized to maintain the robot body. Finally, some demonstrations using the developed BIT-6NAZA robot are carried out. The performance illustrates the effectiveness and feasibility of the hybrid gait obstacle-avoidance control strategy.

Unmanned surface vehicle (USV) path planning is a crucial technology for achieving USV autonomous navigation. Under global path planning, dynamic local obstacle avoidance has become the primary focus for safe USV navigation. In this study, a USV autonomous dynamic obstacle avoidance method based on the enhanced velocity obstacle method is proposed in order to achieve path replanning. Through further analysis of obstacles, the obstacle geometric model set in the conventional velocity obstacle method was redefined. A special triangular obstacle geometric model was proposed to reconstruct the velocity obstacle region. The collision time was predicted by fitting the previously gathered data to the detected obstacle’s distance, azimuth, and other relevant data. Then, it is combined with the collision risk to determine when obstacle avoidance should begin and end. In order to ensure safe driving between path points, the international maritime collision avoidance rules (COLREGs) are incorporated to ensure the accuracy of obstacle avoidance. Finally, through numerical simulations of various collision scenarios, it was determined that, under the assumption of ensuring a safe encounter distance, the maximum change rates of USV heading angle are optimized by 17.54%, 58.16%, and 28.63% when crossing, head-on, and overtaking, respectively. The results indicate that, by optimizing the heading angle, the enhanced velocity obstacle method can avoid the risk of ship rollover caused by an excessive heading angle during high-speed movement and achieve more accurate obstacle avoidance action in the event of a safety encounter.

In this paper, the problem of collaborative obstacle avoidance of swarm robots with limited view angles is studied. In the case of limited view angles, the control model of swarm robots system is designed. The swarm robots can safely avoid a single static obstacle when the obstacle is within the robot’s visual angle. The main research result of this paper is to design a collaborative obstacle avoidance control algorithm for swarm robots, and subdivide the overall control algorithm into three sub algorithms: formation, obstacle avoidance and navigation. The condition of limited view angle is added to the collaborative obstacle avoidance control algorithm of swarm robots, and a control model of collaborative obstacle avoidance of swarm robots with limited view angle is constructed. Then based on the Simulink platform, the simulation is carried out in the case of static obstacle to verify the correctness and stability of the algorithm.

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM arm.

This paper presents a new algorithm for the motion control of painting robots applied to intelligent motion control for wall painting and logistics handling. The study introduces a mathematical equation that can be described using a motion function. Its initial condition is around the obstacle point from the starting point to the target point, and its boundary condition is that a straight line or a line segment tangent to an arc in the search circle must be taken at the obstacle point. This mathematical equation is a nonlinear algebraic equation that finds the path from the starting point through the obstacle to the target point by a dynamic method. A minimum value evaluation criterion is applied to optimize multiple tracing paths to find an optimal solution. The advantage of this algorithm is that it simplifies the problem by finding the differences between them through a one-dimensional search for paths that avoid obstacles in the region of the motion function. It makes decisions by numerical comparison, thus avoiding the errors associated with objective evaluation. This algorithm can be applied in the field of intelligent control and the field of driverless vehicles. The combination of numerical and information control offers a new way of applying mathematical engineering in engineering control.

Computer vision plays a significant role in mobile robot navigation due to the wealth of information extracted from digital images. Mobile robots localize and move to the intended destination based on the captured images. Due to the complexity of the environment, obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement. This study offers a real-time solution to the problem of extracting corridor scenes from a single image using a lightweight semantic segmentation model integrating with the quantization technique to reduce the numerous training parameters and computational costs. The proposed model consists of an FCN as the decoder and MobilenetV2 as the decoder (with multi-scale fusion). This combination allows us to significantly minimize computation time while achieving high precision. Moreover, in this study, we also propose to use the Balance Cross-Entropy loss function to handle diverse datasets, especially those with class imbalances and to integrate a number of techniques, for example, the Adam optimizer and Gaussian filters, to enhance segmentation performance. The results demonstrate that our model can outperform baselines across different datasets. Moreover, when being applied to practical experiments with a real mobile robot, the proposed model’s performance is still consistent, supporting the optimal path planning, allowing the mobile robot to efficiently and effectively avoid the obstacles.

With space technology development, the spatial robotic arm plays an increasingly important role in space activities. Spatial robotic arms can effectively replace humans to complete in-orbit service tasks. The trajectory planning is the basis of robotic arm motion. Its merit has an essential impact on the quality of the completed operation. The research on spatial robotic arm trajectory planning has not yet formed a broad framework categorization, so it is necessary to analyze and deeply summarize the existing research systematically. This paper introduces the current situation of space obstacle avoidance trajectory planning and motion trajectory planning. It discusses the basic principle and practical application of the spatial robotic arm trajectory planning method. The future development trend has also been prospected.