Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame mat...
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ISBN:
(纸本)9798350384581;9798350384574
Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://***/jisehua/***. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.
Hexapod robots used in resource exploration and post-disaster rescue operations often face the risk of multiple unexpected leg failures, which can lead to an immediate threat of falling. Traditional controllers genera...
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ISBN:
(纸本)9798350364200;9798350364194
Hexapod robots used in resource exploration and post-disaster rescue operations often face the risk of multiple unexpected leg failures, which can lead to an immediate threat of falling. Traditional controllers generally lack the fault-tolerance required to adjust locomotion gaits to continue to move in a target direction. Maintaining the original motion patterns in such situations can result in severe damage to the robot. This work proposes a hierarchical fault-tolerant control scheme that employs a meta-learning training architecture capable of addressing the non-stationary multi-task leg failure adaptation problem. The meta Learning architecture simultaneously trains multiple locomotion tasks with all possible configurations of one, two or three leg failures occurring during the normal movement of hexapod robots. Experimental results demonstrate the exceptional fault-tolerant capabilities of the trained controller in adapting to unexpected leg failures. Furthermore, the controller maintains the robot's planar mobility, providing superior velocity tracking and directional following abilities compared to baselines.
The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones repr...
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ISBN:
(纸本)9798350384581;9798350384574
The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone's conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimisation to a multi-body winged drone with trajectory optimisation to solve the motion intelligence problem under diverse flight mission requirements, such as energy consumption and mission completion time. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and mission time, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.
Advanced control methods play a role in robotic grasping of objects. Currently most of the advanced control methods for robotic grasping can fall into the reinforcement learning and neural networks, which typically re...
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ISBN:
(纸本)9798350385731;9798350385724
Advanced control methods play a role in robotic grasping of objects. Currently most of the advanced control methods for robotic grasping can fall into the reinforcement learning and neural networks, which typically require large amounts of training data and computational resources. To achieve faster and more efficient robotic grasping with multiple fingers is still a challenging issue. In this study, we proposed a novel intelligentcontrol method for multi-finger robotic grasping based on multi-agent reinforcement learning (MARL). The MARL was constructed based on potential energy model, enabling the agents to accomplish tasks within relatively shorter time. An experiment was performed to simulate the object grasping and movement with multi-finger robotic hand from an initial position to a target position. Results showed that the task could be accomplished within only 20 iterations using the MARL. The success rate of grasping using the MARL reached up to 90%, which was higher than the success rates of traditional Q-learning (80%) or preprogrammed in structured environment (70%). These results indicated that the proposed MARL could achieve higher success rate of grasping with shorter training time for multi-finger robotic grasping, which may play a role in a variety of dexterous object grasping and manipulation with robotic hand in unstructured environment.
This study proposes a novel planning framework based on a model predictive control formulation that incorporates signal temporal logic (STL) specifications for task completion guarantees and robustness quantification....
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ISBN:
(纸本)9798350384581;9798350384574
This study proposes a novel planning framework based on a model predictive control formulation that incorporates signal temporal logic (STL) specifications for task completion guarantees and robustness quantification. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion push recovery, where the robot experiences unexpected disturbances. Existing recovery strategies often struggle with complex task logic reasoning and locomotion robustness evaluation, making them susceptible to failures due to inappropriate recovery strategies or insufficient robustness. To address this issue, the STL-guided framework generates optimal and safe recovery trajectories that simultaneously satisfy the task specification and maximize the locomotion robustness. Our framework outperforms a state-of-the-art locomotion controller in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Furthermore, it demonstrates versatility in tasks such as locomotion on stepping stones, where the robot must select from a set of disjointed footholds to maneuver successfully.
Though larger vessels may be well-equipped to deal with wavy conditions, smaller vessels are often more susceptible to disturbances. This paper explores the development of a nonlinear model predictive control (NMPC) s...
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ISBN:
(纸本)9798350377712;9798350377705
Though larger vessels may be well-equipped to deal with wavy conditions, smaller vessels are often more susceptible to disturbances. This paper explores the development of a nonlinear model predictive control (NMPC) system for Uncrewed Surface Vessels (USVs) in wavy conditions to minimize average roll. The NMPC is based on a prediction method that uses information about the vessel's dynamics and an assumed wave model. This method is able to mitigate the roll of an under-actuated USV in a variety of conditions by adjusting the weights of the cost function. The results show a reduction of 39 % of average roll with a tuned controller in conditions with 1.75-metre sinusoidal waves. A general and intuitive tuning strategy is established. This preliminary work is a proof of concept which sets the stage for the leveraging of wave prediction methodologies to perform planning and control in real time for USVs in real-world scenarios and field trials.
One of the most notorious control problems for robot manipulators with flexible joints is vibration during movement, which substantially deteriorates motion accuracy. To cope with this problem, this paper proposes a l...
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Autonomous underwater vehicles (AUVs) play an important role in human understanding and management of the ocean. Fully actuated AUVs can achieve 6 degrees of freedom motion control, with high control accuracy and anti...
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One of the major challenges in non-contact actuation of miniaturized medical robots (MMRs) during minimally invasive surgery is precise magnetic force control to ensure accurate movement of these devices inside the hu...
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ISBN:
(纸本)9798350355376;9798350355369
One of the major challenges in non-contact actuation of miniaturized medical robots (MMRs) during minimally invasive surgery is precise magnetic force control to ensure accurate movement of these devices inside the human body. This study presents a novel model for the control of the magnetic field generated by a magnetic actuator composed of an electromagnetic coil with a ferromagnetic core. A hybrid model that incorporates analytical formulations of physical phenomena and adapts them based on experimental measurements is used as an alternative approach. This model enables calculation of the in-workspace magnetic field, even in nonlinear conditions associated with high currents in the actuator, offering a significant extension of the operating region. We have experimentally evaluated this model in terms of accuracy, computation time and implementability. The results show that it can be computed accurately in a few milliseconds, making it an ideal choice for controlling magnetic mini-robots.
The emergence of intelligent prostheses has facilitated the life and work of disabled patients. The interaction aspect of prostheses has become a highlight research topic in the field of rehabilitation robotics. Howev...
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ISBN:
(纸本)9798350384581;9798350384574
The emergence of intelligent prostheses has facilitated the life and work of disabled patients. The interaction aspect of prostheses has become a highlight research topic in the field of rehabilitation robotics. However, most of the existing prosthetic interaction methods focus on the use of myoelectricity to classify finite gestures, rather than continuous (infinite) force detection, which greatly limits the use of prosthetic scenarios. In this study, a novel optical waveguide sensor was used to collect muscle deformation information from the human arm for continuous control of the prosthetic grip force. The optical waveguide sensor was embedded with carbon fiber to limit the stretching of the waveguide, which led to the optical waveguide sensor being sensitive to bending deformation. Compared with EMGs, the accuracy of continuous grip force control based on the optical waveguide sensor is higher. The R-Square for prosthetic grip force and hand grip force were 0.867 and 0.9724 in the periodic and sustaining grip force experiments, respectively. The results suggested that the proposed method could provide a new approach to the interaction of prostheses.
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