Journal of Field Robotics最新文献

This paper describes a vertical take off and landing (VTOL) aircraft equipped with a rotor obliquely fixed to the wing and a control surface that changes the direction of the slipstream of the propeller. Conventional VTOL aircraft, such as lift-cruise or tilt rotor, show either increased drag and weight, resulting in reduced efficiency and payload capacity, or added mechanical complexity accompanied by sophisticated control requirements. Unlike other conventional VTOL aircraft, this vehicle achieves a stable transition between fixed-wing and rotary-wing modes simply by changing the pitch attitude of the aircraft. As the rotor is mounted at an inclined angle, it can control the pitch not only during hovering but also during horizontal flight by using differential thrust between the front and rear propulsion. Moreover, the roll angle can be controlled by using differential thrusts between the left and right thrusts. Additionally, this aircraft achieves yaw axis control by changing the direction of the rotor's slipstream. The control surface that adjusts the direction of the slipstream is termed the “rudder vane,” which is expected to provide rapid yaw response during hovering and naturally enhance directional stability during horizontal flight. Overall, this design promises improved energy efficiency, reduced mechanical and software complexity, and enhanced maneuverability, making the vehicle particularly well suited to demanding real-world operational environments. In this paper, mathematical modeling of a fixed-tilt rotor VTOL aircraft equipped with a rudder vane is performed, and a control law for the aircraft is designed and validated via flight tests.

Multi-axle active suspension vehicles are very promising for traversing impassable abrupt roads under high payload demands and complement the strengths of mobile robots. However, this hope is severely blocked by the high-order indeterminate property of the vehicle and the complex vehicle-ground interactions, making the suspension adjustment infinitely solvable. For the low-speed traversing reality, this paper first proposes a body attitude and wheel load coupling control model based on the explicit characterization of the load-deformation coupling nature of vehicles; then, the suspension adjustment-based wheel gait control is designed for typical impassable scenarios, wherein the coupling control model is invoked to solve the suspension adjustment under the stability objectives; finally, a multi-axle active suspension unmanned ground vehicle (MAS-UGV) in near-conventional configurations is developed and typical abrupt road traversing experiments are carried out. Experiments confirm that the proposed framework and controller can support high stability traversing of multi-axle active suspension vehicles (at least 50% improvement in attitude stability and controllable wheel loads) on originally impassible abrupt roads via the bionic-like gait, thus providing new possibilities for UGVs and even near-conventional vehicles to construct versatile, tough terrain crossing schemes.

Localization in mobile robotics is essential for achieving autonomy. Effective localization systems integrate data from multiple sensors to enhance state estimation and achieve accurate positioning. Accurate real-time localization is crucial for robot control and trajectory following. Key challenges include initializing the inertial measurement unit (IMU) biases and the direction of gravity, as well as determining the metric scale with a monocular camera. Traditional visual–inertial (VI) initialization techniques rely on precise vision-only motion assessments to address these issues. Multi-sensor fusion faces challenges, such as precise calibration, initialization of sensor groups, and handling measurement errors with varying rates and delays. This paper introduces an Adaptive Tuna Schooling Optimization (ATSO) method to adjust localization strategies based on environmental conditions dynamically. The environmental factors affecting the localization process are considered in the optimization algorithm, and the position is optimally selected accordingly. Using Q-learning with the Q-DNN performs the decision-making process based on past experiences. The dynamic adaptation of the weight parameter allows the algorithm to converge toward optimal solutions, reducing computational complexity. Experimental results demonstrate that the proposed approach improves localization performance, even in challenging conditions.

To solve the problems of high labor intensity and high cost when picking mango manually, a mango picking robot system with dual robotic arms was developed to realize automatic mango picking. Firstly, the YOLOMS network was used to realize the 3D localization of picking points for single mangoes and mango clusters in unstructured environments. Secondly, a new “shearing and grasping integrated” end-effector for non-destructive harvesting of mangoes was designed. Then, a task division method for the workspace of the dual robotic arm harvesting robot was proposed to minimize the likelihood of collisions between dual arms. Additionally, a depth-first picking strategy was introduced to reduce fruit damage and enhance the success rates of picking mangoes from layered canopies. Finally, a mango harvesting robotic system with dual arms was developed and integrated. The performance of the system was evaluated by field mango picking experiments. The results showed that the average recognition rate and planning success rate of the harvesting robot were 83.94% and 98.45%, respectively. In addition, the average harvesting success rate of the robot was 73.92%, and the average single-fruit harvesting time was 8.93 s. Compared with the robot with single arm, the harvesting time was reduced by 48.38%, which indicated that the harvesting efficiency of the dual robotic arm harvesting robot was significantly improved. The average collision-free harvesting rate with the addition of the depth-first harvesting strategy was 91.68%, which verified the rationality and effectiveness of the dual robotic arm collaborative mango harvesting robotic system. The results provide technical support for automated mango harvesting.

Quadruped robots are gaining more importance among researchers due to their adaptability to complex terrains, though their stability handling is complex. Quadruped robots are one of the best kinds of legged robotic systems with regard to their framework and movement flexibility. For making quadrupeds fully autonomous, it is important to concentrate on their modeling, modulation, and maneuverability. This article sheds light on quadruped robots, starting from the early-stage developments of TITAN series quadrupeds to current progress based on variations of design, gait analysis, control, and motion planning, so as to provide directions for future researchers to develop efficient quadrupeds. Furthermore, we have made a comparative analysis on various aspects, including various mobile robots, material specifications, leg structure and topology, crucial modeling parameters, sensors and actuation systems, as well as traditional and advanced algorithms. This work explores gait analysis, followed by numerous researchers concentrating on various gait stability challenges; in addition, it presents the studies on control strategies of quadruped robots based on a PID controller, neural network controller, fuzzy logic controller, delayed feedback controller, hybrid controller, and other peculiar controllers. Furthermore, the representative explorations on motion planning algorithms of quadruped robots have been done. To conclude, this paper provides an overview of the various variants of quadrupeds, focusing on important aspects, followed by an analysis of pivot features and providing solutions to current challenges and issues so as to help future researchers in the field identify key areas of research.

Obstacle avoidance (OA) is an important issue in the motion planning of redundant robot manipulators. Various effective OA schemes have been reported, but they may suffer from a large amount of calculation for the situation of multiple obstacles and/or complex-shaped obstacles. In this paper, to address the aforementioned limitation, a new OA scheme with dimensionality reduction is proposed and studied for redundant robot manipulators. Specifically, by combining robot kinematics and geometry, a typical inequality criterion for OA is designed, which can reduce the calculation for an obstacle point from the general three dimensions to one dimension. Such an inequality criterion is further aided by (1) the dynamic selection for the situation of a large number of obstacle points, and (2) the feature extraction for the situation of complex-shaped obstacles. With the OA environment optimized and the obstacles' dimension limited, the computational efficiency of generating the inequality criterion for specific scenarios can thus be improved. By incorporating the inequality criterion and the joint physical constraint, the new dimensionality-reduction OA (DROA) scheme is developed for redundant robot manipulators. Such a DROA scheme is depicted as a quadratic program that is solved by the reinforcement learning method. Simulation and experiment results under the PA10 robot manipulator verify the efficacy and applicability of the proposed DROA scheme.

Tracked robots are widely used in agriculture, military, mining, and other fields. During the traveling process of tracked robots, the complex interaction between the tracks and the ground causes slip, which leads to problems such as low path tracking accuracy and poor control stability. To solve this thorny problem, quantitative control compensation parameters are generally obtained through experiments, or extended Kalman filter (EKF) is designed based on Gaussian noise to estimate slip parameters during motion. However, the sensor measurement noise of tracked robots usually exhibits a non-Gaussian distribution in uneven terrain and variable soil conditions. Under such circumstances, these methods exhibit larger errors and demonstrate inadequate adaptability. Therefore, this paper proposes a variational Bayesian EKF (VBEKF) algorithm for slip parameters estimation, and designs a slip compensation path tracking controller to improve the accuracy and adaptability of the control system of tracked robots under complex operating conditions. The main contributions of this paper are as follows: (1) The non-Gaussian noise was re-modeled using the Student's t-distribution, and combined with variational Bayesian, the VBEKF algorithm was designed. This algorithm can more accurately estimate the slip parameters between the tracks and soil under complex and varying operating conditions, demonstrating enhanced adaptability. (2) Based on the backstepping control principle, a path tracking controller with slip parameter compensation was designed for tracked robots. This controller dynamically adjusts its output control based on the estimated slip parameters to eliminate the impact of slip between the tracks and soil on path tracking accuracy. Finally, the effectiveness of the method was demonstrated through simulations and experiments. This study can improve the adaptability and stability of tracked robots under complex and variable operating conditions, ensuring accurate and rapid task completion, and has broad application prospects.