Robotics and Autonomous Systems最新文献

Snake robots with limbless structure and rich locomotion gaits have been designed and built for wide application in various fields including military reconnaissance, pipeline operation, disaster search and rescue, etc. However, the problem how to flexibly and smoothly control switch and change of different locomotion gaits is still facing enormous challenges. A novel unified design rule of the CPG network model composed of improved Hopf oscillators is proposed, based on which a variety of different network structures can be created by designing connection distances and coupling weights among all oscillator units. Through the relationships between the control parameters of the Hopf oscillator, decoupling of the bifurcation parameters is achieved to solve inconsistent output waveform amplitude when the bifurcation parameters are not completely equal. Furthermore, five typical movement modes of biological snake are designed and smooth switch between different locomotion gaits is realized. A control system is constructed based on the Robot Operating System (ROS) and a prototype of snake robot is built, and the effectiveness of the proposed CPG model in controlling locomotion gaits was verified through simulations and experiments. The CPG modeling approach has important theoretical significance and practical instructive value for motion planning and gait control of snake robots in complex environments.

Work-related Musculoskeletal Disorders (WMSDs) are the most common occupational diseases caused by the prolonged performance of strenuous work, such as manual handling of loads or long-term maintenance of incongruous postures. Different safety protocols are implemented to reduce WMSDs and optimize the working environment, but one of the most promising solutions is using occupational exoskeletons (OEs). However, to truly acknowledge the benefits of OEs and be able to introduce them into daily business use, devices must pass several development and testing stages that determine the Technology Readiness Level (TRL). This review study aims to present an up-to-date collection of the most advanced assessments of exoskeletons for upper and back support, ranging from laboratory real-task simulations to operational scenarios in industrial sites. To identify relevant studies, we conducted comprehensive searches across different electronic databases, i.e., PubMed, Scopus, and Web of Science. Different keywords were used for the literature search, e.g., occupational exoskeleton, industrial exoskeleton, etc. Studies were included if they investigated the assessment of exoskeletons in the laboratory with real tasks or an industrial environment. We identified 45 research articles that fulfilled this selection criterion. Several features are compared and discussed in detail, such as industrial environment, experimental protocol, task performed, and exoskeleton typology. These data allowed us to formulate results that report the correspondence or discrepancy between the number of papers testing exoskeletons and WMSDs in different industrial sectors, the type of assessment performed, and the impact of exoskeletons on workers and industries at different TRLs. Among the results, the incidence of WMSDs in the manufacturing industry is 21.13%, while the adoption of exoskeletons in the same field is the highest with respect to the other industrial fields, at 44.45%. Electromyography (EMG) and Questionnaires were the most evaluated typologies across all development and testing stages (with an incidence of 64% across the selected articles). Additionally, an average reduction of EMG activity was reported, with 24% for Upper Limb and 20% for Back Support. Regarding the subjective assessment reported in the questionnaires, 68% of the studies reported a positive evaluation. Based on these outcomes, this work provides a framework for an effective evaluation process for the OEs to raise TRL with recommendations for future research activities.

Wearable rehabilitation robots have become an important auxiliary tool in rehabilitation therapy, providing effective rehabilitation training and helping to recover damaged muscles and joints. In response to the difficulty of traditional control methods in solving various constraints in the trajectory tracking process of the Upper Limb Rehabilitation Robot (ULRR), this study uses model predictive control to study the trajectory tracking problem of the upper limb rehabilitation robot. Firstly, based on the Lagrangian dynamic model of wearable rehabilitation robots, an extended state space model with pseudo linearization of the system was established. Given the performance indicators and various constraints of the system, a corresponding model predictive controller is designed based on the Laguerre model to ensure system performance while greatly reducing the computational complexity of predictive control. Secondly, the stability of the model predictive controller is demonstrated, and a disturbance observer is introduced into the controller to achieve compensation for slow-varying perturbations; a joint space sliding mode variable is also introduced to achieve simultaneous tracking of the joint’s desired position and desired velocity. Finally, taking a planar two bar robot as an example, comparative simulation verification was conducted on unconstrained joint trajectory tracking and constrained joint trajectory tracking. The simulation results show that the model predictive controller can achieve simultaneous tracking of joint expected trajectory and expected speed while meeting various constraints. It has good effects in improving patient motion control ability and reducing patient fatigue, providing new research ideas and methods for the field of rehabilitation therapy.

The rapid development of robotics technology has driven the growth of robot types and the development of related technologies. As an important aspect of robot research, path planning technology plays an irreplaceable role in practical production and application. Ant colony algorithm has a wide range of applications in robot path planning, but there is also a problem of performance overly relying on initial parameter selection. In order to solve this problem and improve the performance of mobile robot path planning, an improved ant colony algorithm based on firefly algorithm was studied and designed in a two-dimensional environment. In order to further explore the performance of ant colony algorithm in solving robot coordinated path planning problems, an improved ant colony algorithm based on heuristic function was also designed. In a three-dimensional environment, an improved ant colony algorithm based on the improved artificial potential field method was designed. The research results show that the maximum running time of the improved ant colony algorithm based on the firefly algorithm in different grid environments is 819.36 s, 847.01 s, and 811.54 s, respectively. The average running time of the improved ant colony algorithm based on heuristic function in different grid environments is 5.19 s, 5.97 s, and 9.09 s, with average path lengths of 29.90 cm, 31.08 cm, and 37.01 cm, and path length variances of 0.35, 0.87, and 2.21, respectively. The ant colony algorithm based on the improved artificial potential field method has a running time of 1.930 s, 3.182 s, and 4.662 s in different grid environments, and a path length of 29.275 cm, 49.447 cm, and 67.057 cm, respectively. The ant colony algorithm for research and design optimization has good performance. The contribution of the research lies in the design of three path planning methods for mobile robots, including two-dimensional path planning and three-dimensional path planning, which improves the time of path planning and shortens the average path length. The novelty of the research is reflected in the design of a path planning method for mobile robots in two-dimensional and three-dimensional environments, which improves the ant colony algorithm through firefly algorithm and heuristic function, and combines the ant colony algorithm with the improved artificial potential field method. The method designed by the research institute can provide technical support for path planning of mobile robots.

This note scrutinizes an adaptive fault-tolerant control (FTC) approach tailored for unmanned aerial vehicles (UAVs), addressing the critical need for both fault accommodation and disturbance suppression. Departing from traditional reliance on robust discontinuous control strategies prone to chattering and demanding precise uncertainty bounds, our FTC method ensures fixed-time stability, guaranteeing the convergence of attitude tracking errors to zero. Central to our approach is an adaptive algorithm adept at concurrently estimating unknown actuator faults and upper bounds of lumped uncertainties. Moreover, our adaptive schemes accurately estimate the upper bound of the lumped uncertainty term, encompassing model uncertainties, external disturbances, and unmodeled dynamics, thereby eliminating the need for assuming known bounds on uncertainties. Stability analysis under the developed control law is thoroughly performed using the Lyapunov stability theory. Notably, our strategy employs an extended Kalman filter (EKF) observer for state estimation and fault detection, facilitating fault detection through an adaptive threshold technique dynamically adjusted based on real-time mean and variance of the residual signal. Through comprehensive simulation and experimental validations, our proposed methodology demonstrates significant advancements in ensuring safety and reliability in UAVs.

This paper presents adaptive and occupancy grid map algorithms for automatic lane change technology, a core technology in autonomous vehicles. The objectives are to improve driver safety and convenience with technology that automatically changes lanes at the request of the driver. The algorithms construct nine grids on the basis of ego vehicles and generate adaptive and occupancy grid maps by using the relative speeds of ego and target vehicles. When a driver requests a lane change, the adaptive grid map reduces the number of cases where the target vehicles may exist around the ego vehicle from 256 to 32, thus decreasing the calculation amount. Therefore, the algorithms are suitable for use in autonomous vehicles that require real-time calculations. An occupancy grid map is formed in accordance with the location of the target vehicles, and whether lane changes are possible is determined. The algorithms generate a virtual simulation environment with the CarMaker and are simulated using Matlab/Simulink. An experiment is conducted in a real driving environment with real vehicles to prove the validity of the algorithms.

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We explore sim-to-real transfer of deep reinforcement learning controllers for a heavy vehicle with active suspensions designed for traversing rough terrain. While related research primarily focuses on lightweight robots with electric motors and fast actuation, this study uses a forestry vehicle with a complex hydraulic driveline and slow actuation. We simulate the vehicle using multibody dynamics and apply system identification to find an appropriate set of simulation parameters. We then train policies in simulation using various techniques to mitigate the sim-to-real gap, including domain randomization, action delays, and a reward penalty to encourage smooth control. In reality, the policies trained with action delays and a penalty for erratic actions perform nearly at the same level as in simulation. In experiments on level ground, the motion trajectories closely overlap when turning to either side, as well as in a route tracking scenario. When faced with a ramp that requires active use of the suspensions, the simulated and real motions are in close alignment. This shows that the actuator model together with system identification yields a sufficiently accurate model of the actuators. We observe that policies trained without the additional action penalty exhibit fast switching or bang–bang control. These present smooth motions and high performance in simulation but transfer poorly to reality. We find that policies make marginal use of the local height map for perception, showing no indications of predictive planning. However, the strong transfer capabilities entail that further development concerning perception and performance can be largely confined to simulation.

Underwater humanoid robots (UHRs) have emerged as a significant area of interest in robotics, with the potential to overcome the limitations of traditional underwater robots and revolutionize underwater activities. This review examines the development of UHRs, focusing on their perception, decision-making, and execution capabilities within a hierarchical human-machine cooperation framework. The Perception Layer involves gathering information from the environment and human collaborators. The Decision-making Layer explores different levels of robot autonomy and the current status of human-UHR collaborative decision-making. The Execution Layer encompasses modeling, control, and actuation mechanisms to translate high-level intentions into physical actions. Various UHR implementations across research teams are reviewed to provide a comprehensive overview of current advancements. Discussions and challenges surrounding UHR progress are provided as well. Continued research and development efforts of UHR represent a promising avenue for advancing human-machine cooperation and pushing the boundaries of underwater exploration, contributing to scientific discoveries and societal benefits in this captivating realm.

Online footstep planning is essential for bipedal walking robots, allowing them to walk in the presence of disturbances and sensory noise. Most of the literature on the topic has focused on optimizing the footstep placement while keeping the step timing constant. In this work, we introduce a footstep planner capable of optimizing footstep placement and step time online. The proposed planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer based on Augmented Lagrangian (AL) method with analytical gradient descent, solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real time to optimize for footstep location as well as step timing at the rate of 200 Hz. We show that such asynchronous real-time optimization with the AL method (ARTO-AL) provides the required robustness and speed for successful online footstep planning. Furthermore, ARTO-AL can be extended to plan footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains. Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL demonstrates increased stability in simulated walking experiments as it can resist pushes on flat ground and on a $10°$ ramp up to 120 N and 100 N respectively. Videos² and open-source code³ are released.

In the context of real world application, Search and Rescue Missions on the ocean surface remain a complex task due to the large-scale area and the forces of the ocean currents, spreading lost targets and debris in an unpredictable way. In this work, we present a Path Planning Approach to search for a lost target on ocean surface using a swarm of UAVs. The combination of GlobCurrent dataset and a Lagrangian simulator is used to determine where the particles are moved by the ocean currents forces while Deep Q-learning algorithm is applied to learn from their dynamics. The evaluation results of the trained models show that our search strategy is effective and efficient. Over a total search area (red Sea zone), surface of 453422 Km${}^2$, we have shown that our strategy Search Success Rate is 98.61%, the maximum Search Time to detection is 15 days and the average Search Time to detection is almost 15 h.