Unstructured Dynamic Environments Research Articles

Research on adaptive impedance control strategy for humanoid walking in unstructured dynamic environment

Research on adaptive impedance control strategy for humanoid walking in unstructured dynamic environment

Vision-Based Reactive Temporal Logic Motion Planning for Quadruped Robots in Unstructured Dynamic Environments

Vision-Based Reactive Temporal Logic Motion Planning for Quadruped Robots in Unstructured Dynamic Environments

Multimodality Driven Impedance-Based Sim2Real Transfer Learning for Robotic Multiple Peg-in-Hole Assembly.

Robotic rigid contact-rich manipulation in an unstructured dynamic environment requires an effective resolution for smart manufacturing. As the most common use case for the intelligence industry, a lot of studies based on reinforcement learning (RL) algorithms have been conducted to improve the performances of single peg-in-hole assembly. However, existing RL methods are difficult to apply to multiple peg-in-hole issues due to more complicated geometric and physical constraints. In addition, previously limited solutions for multiple peg-in-hole assembly are hard to transfer into real industrial scenarios flexibly. To effectively address these issues, this work designs a novel and more challenging multiple peg-in-hole assembly setup by using the advantage of the Industrial Metaverse. We propose a detailed solution scheme to solve this task. Specifically, multiple modalities, including vision, proprioception, and force/torque, are learned as compact representations to account for the complexity and uncertainties and improve the sample efficiency. Furthermore, RL is used in the simulation to train the policy, and the learned policy is transferred to the real world without extra exploration. Domain randomization and impedance control are embedded into the policy to narrow the gap between simulation and reality. Evaluation results demonstrate the effectiveness of the proposed solution, showcasing successful multiple peg-in-hole assembly and generalization across different object shapes in real-world scenarios.

A Unified Framework Integrating Trajectory Planning and Motion Optimization Based on Spatio-Temporal Safety Corridor for Multiple AGVs

Planning safe and smooth trajectories for multiple autonomous ground vehicles (MAGVs) in a complex dynamic unstructured environment is a fundamental and challenging task. In this paper, a novel unified framework integrating trajectory planning and motion optimization (TPMO) is proposed based on spatio-temporal safety corridor (STSC), which guarantees collision avoidance and trajectory smoothness. The proposed TPMO framework consists of two parts. The first part is to establish the STSC for each AGV based on the mixed integer quadratic programming (MIQP) algorithm. The proposed STSC method ensures collision avoidance in the environment of static and dynamic obstacles, and provides a longitudinal and lateral coupled trajectory (LLCT) for trajectory planning. The second part is to design a motion optimization methodology, which considers the constraints of AGV geometry as well as longitudinal and lateral coupled motion characteristics. Moreover, our formulation provides a theoretical guarantee that the entire trajectory is optimal under collision avoidance. Finally, the proposed TPMO framework is applied to solve the optimal cooperative trajectory and motion planning problem of MAGVs in a near-natural simulation and real vehicle environments, validating the proposed framework's effectiveness and practicality.

Perception-aware online trajectory generation for a prescribed manoeuvre of unmanned surface vehicle in cluttered unstructured environment

This paper proposes a perception-aware online trajectory generation system that facilitates prescribed manoeuvres of an unmanned surface vehicle (USV) in a dynamic unstructured environment. The proposed system is developed based on the principles of the inverse dynamics in the virtual domain (IDVD) method and an event-triggered receding horizon control (ETRHC) mechanism. This approach transforms the underlying nonconvex constrained optimization problem into a virtual space with a differentially flat dynamics and uses relatively few decision variables to prototype feasible quasi-optimal trajectories. The closed-loop configuration is provided by a computationally efficient ETRHC mechanism that uses situational awareness of operating environment to trigger trajectory replanning if/when required. This addresses the challenge of continuously updating a closed-loop trajectory which imposes unnecessary computational burden on a system with the limited onboard resources. To investigate the performance of the proposed trajectory generating system, a dynamic unstructured environment including variable and uncertain no-fly zone areas as well as variable current vector fields are modeled. Further, different operating conditions incorporating the uncertainties of environment and sudden failure on the USV propulsion system are introduced to examine the effectiveness, agility, and robustness of the proposed trajectory generating system. A comparative study with benchmark solutions generated by the hp-adaptive Radau pseudo-spectral method is conducted to provide a detailed statistical analysis of the proposed approach robustness, computational complexity, and effectiveness. The simulation results confirm the effectiveness of the proposed trajectory generator and ability to produce a solution for online realization.

Teleoperation of Humanoid Robots: A Survey

Teleoperation of humanoid robots enables the integration of the cognitive skills and domain expertise of humans with the physical capabilities of humanoid robots. The operational versatility of humanoid robots makes them the ideal platform for a wide range of applications when teleoperating in a remote environment. However, the complexity of humanoid robots imposes challenges for teleoperation, particularly in unstructured dynamic environments with limited communication. Many advancements have been achieved in the last decades in this area, but a comprehensive overview is still missing. This survey article gives an extensive overview of humanoid robot teleoperation, presenting the general architecture of a teleoperation system and analyzing the different components. We also discuss different aspects of the topic, including technological and methodological advances, as well as potential applications.

An Obstacle-Avoidance Motion Planning Method for Redundant Space Robot via Reinforcement Learning

On-orbit operation tasks require the space robot to work in an unstructured dynamic environment, where the end-effector’s trajectory and obstacle avoidance need to be guaranteed simultaneously. To ensure the completability and safety of the tasks, this paper proposes a new obstacle-avoidance motion planning method for redundant space robots via reinforcement learning (RL). First, the motion planning framework, which combines RL with the null-space motion for redundant space robots, is proposed according to the decomposition of joint motion. Second, the RL model for null-space obstacle avoidance is constructed, where the RL agent’s state and reward function are defined independent of the specific information of obstacles so that it can adapt to dynamic environmental changes. Finally, a curriculum learning-based training strategy for RL agents is designed to improve sample efficiency, training stability, and obstacle-avoidance performance. The simulation shows that the proposed method realizes reactive obstacle avoidance while maintaining the end-effector’s predetermined trajectory, as well as the adaptability to unstructured dynamic environments and robustness to the space robot’s dynamic parameters.

Hierarchical Motion Planning for Autonomous Vehicles in Unstructured Dynamic Environments

This letter presents a hierarchical motion planner for generating smooth and feasible trajectories for autonomous vehicles in unstructured environments with static and moving obstacles. The framework enables real-time computation by progressively shrinking the solution space. First, a graph searcher based on combined heuristic and partial motion planning is proposed for finding coarse trajectories in spatiotemporal space. To enable fast online planning, a time interval-based algorithm that considers obstacle prediction trajectories is proposed, which uses line segment intersection detection to check for collisions. Second, to practically smooth the coarse trajectory, a continuous optimizer is implemented in three layers, corresponding to the whole path, the near-future path and the speed profile. We use discrete points to represent the far-future path and parametric curves to represent the near-future path and the whole speed profile. The approach is validated in both simulations and real-world off-road environments based on representative scenarios, including the “wait and go” scenario. The experimental results show that the proposed method improves the success rate and travel efficiency while actively avoiding static and moving obstacles.

An Anthropomorphic Robotic Finger With Innate Human-Finger-Like Biomechanical Advantages Part I: Design, Ligamentous Joint, and Extensor Mechanism

Exploring human hand fundamental biomechanical features and exploiting them to robotic hands have been proven to be an effective approach to enhancing artificial hands' performance, especially when interacting with various objects in dynamic unstructured environments. In this article, a bioinspired anthropomorphic robotic finger is first proposed, which embeds human finger musculoskeletal features in the design. Based on this design, three human-finger-like biomechanical advantages are systematically investigated and embodied in the bioinspired robotic finger. This article for the first time derives, presents, and experimentally verifies the mathematical models for the variable stiffness of finger ligamentous joints and self-adaptive morphing mechanism of finger flexible tendon sheaths, and validates and compares the influence of the reticular and linear extensor morphologies on fingertip feasible forces in three-dimensional (3-D) space. In this Part I of the article, two of the biomechanical properties, i.e., joint stiffness generated by the ligamentous joint of the finger, and fingertip feasible force space influenced by the reticular extensor mechanism are systematically investigated through theoretical modeling and experimental verification. Correspondingly, two biomechanical advantages were found, i.e., the ligamentous joint of the finger could provide anisotropic variable joint stiffness, enhancing the adaptivity, dexterity, and stability of fingers; and a reticular extensor mechanism could enlarge the fingertip feasible force space in 3-D space by 30.9% theoretically and 146.4% experimentally on average compared with the linear extensor, contributing to enrich force conditions during interactions. The third biomechanical advantage, i.e., fingertip force–velocity workspace can be augmented through the flexible tendon sheath, and grasping tests for a robotic hand designed with the aforementioned advantages are presented in Part II of this article.

Adaptive fractional-order admittance control for force tracking in highly dynamic unknown environments

PurposeWith the increasing demands of industrial applications, it is imperative for robots to accomplish good contact-interaction with dynamic environments. Hence, the purpose of this research is to propose an adaptive fractional-order admittance control scheme to realize a robot–environment contact with high accuracy, small overshoot and fast response.Design/methodology/approachFractional calculus is introduced to reconstruct the classical admittance model in this control scheme, which can more accurately describe the complex physical relationship between position and force in the interaction process of the robot–environment. In this control scheme, the pre-PID controller and fuzzy controller are adopted to improve the system force tracking performance in highly dynamic unknown environments, and the fuzzy controller is used to improve the trajectory, transient and steady-state response by adjusting the pre-PID integration gain online. Furthermore, the stability and robustness of this control algorithm are theoretically and experimentally demonstrated.FindingsThe excellent force tracking performance of the proposed control algorithm is verified by constructing highly dynamic unstructured environments through simulations and experiments. In simulations and experiments, the proposed control algorithm shows satisfactory force tracking performance with the advantages of fast response speed, little overshoot and strong robustness.Practical implicationsThe control scheme is practical and simple in the actual industrial and medical scenarios, which requires accurate force control by the robot.Originality/valueA new fractional-order admittance controller is proposed and verified by experiments in this research, which achieves excellent force tracking performance in dynamic unknown environments.

An Anthropomorphic Robotic Finger With Innate Human-Finger-Like Biomechanical Advantages Part II: Flexible Tendon Sheath and Grasping Demonstration

The human hand has a fantastic ability to interact with various objects in the dynamic unstructured environment of our daily activities. We believe that this outstanding performance benefits a lot from the unique biological features of the hand musculoskeletal system. In Part I of this article, a bio-inspired anthropomorphic robotic finger was developed, based on which two human-finger-like biomechanical advantages were elaborately investigated, including the anisotropic variable stiffness associated with the ligamentous joints and the enlarged feasible force space associated with the reticular extensor mechanisms. In Part II, the fingertip force-velocity characteristics resulting from the flexible tendon sheath are studied. It indicates that the fingertip force–velocity workspace can be greatly augmented owing to the self-adaptive morphing of the flexible tendon sheaths, showing the average improvement of 41.2% theoretically and 117.5% experimentally compared with the results of 2 mm, 4 mm, and 6 mm size rigid tendon sheaths. Grasping tests and comparisons are then conducted with four three-fingered robotic hands (one with the robotic finger proposed in Part I, one with hinge joints, one with linear extensors, and one with rigid tendon sheaths) and the human hands of six subjects to handle various objects on flat, rough, and soft surfaces. The results show that the novel bio-inspired design in this research could improve the grasping success rates of the robotic hand. Compared with the grasping test results from the robotic hand with the bio-inspired robotic finger proposed in Part I, the overall grasping performance of a robotic hand with hinge joints, linear extensors, and rigid tendon sheaths decreases by 10%, 6%, and 17%, respectively. The results have also shown that with the embedded biomechanical advantages, even without complex control and sensory systems, the robotic fingers can achieve very comparable performance to human fingers in the grasping demonstrations presented, indicating average 94% of the success rate achieved by the human fingers. Successfully demonstrating 14 of 16 grasp types in the Cutkoskey taxonomy further shows the human-finger-like grasping capability of the proposed robotic fingers.

Piecewise-deterministic Quasi-static Pose Graph SLAM in Unstructured Dynamic Environments

Piecewise-deterministic Quasi-static Pose Graph SLAM in Unstructured Dynamic Environments

Construction and benchmark of an autonomous tracked mobile robot system

Robots require a certain set of skills to perceive and analyse the environment and act accordingly. For tracked mobile robots getting good odometry data from sensory information is a challenging key prerequisite to perform in an unstructured dynamic environment, thus an essential issue in the tracked mobile robotics domain. In this article, we construct a ROS-based tracked mobile robot system taking the Jaguar V4 mobile robot as the base platform. On which several visual odometry solutions based on different cameras and methods (Intel RealSense T265, Zed camera, RTAB-Map RGBD) are integrated and benchmark comparison is performed. Analysis of new challenges faced by different methods while applied on a tracked vehicle as well as recommendations and conclusions are presented. Intel RealSense T265 solution proved to perform well in uncertain conditions which involves bounded vibrations and low lighting conditions with low latency, which result in good map generation. Further evaluations with a path planning algorithm and Intel RealSense T265 were conducted to test the effect of the robot’s motion profiles on odometry data accuracy.

A Learning-Based Model Predictive Trajectory Planning Controller for Automated Driving in Unstructured Dynamic Environments

This paper presents a learning-based model predictive trajectory planning controller for automated driving in unstructured, dynamic environments with obstacle avoidance. We first address the problem of lacking prior knowledge in unstructured environments by introducing a risk map that maps the density and motion of obstacles and the road to an occupancy risk. Model predictive control is then used to integrate trajectory planning and tracking control into one framework to bridge the gap between planning and control. Meanwhile, we use Gaussian Process (GP) regression to learn the residual model uncertainty for improving the model accuracy. An objective function considering both risks within the feasible region and vehicle dynamics is carefully formulated to obtain collision-free and kinematically-feasible local trajectories. Field experiments are performed on real unstructured environments with our automated vehicle. Experimental results demonstrate the effectiveness of the proposed algorithm for successful obstacle avoidance in various complex unstructured scenarios.

Bionic Perception Method of Navel Orange Plucking Position Based on Fmincon and PD Angle Control

In this paper, a bionic perception method of navel orange plucking position based on Fmincon and Proportional Differential (PD) angle control is proposed to solve the problems of wind disturbance and green branches in dynamic unstructured environment. Different from these algorithms that limited to two-dimensional images, this method realizes picking position perception in three-dimensional. Meanwhile, the perception method and the picking robot control algorithm are achieved simultaneously. Firstly, an optimal solution model of the global target rotation angle of the control system based on Fmincon is established to solve the angle optimization problem of robot target approach motion. Secondly, a bionic perception system of plucking position based on PD angle control is constructed to solve specific perception problems. Finally, a joint simulation platform for picking robots based on Solidworks, Adams, and Simulink is given; the validity and accuracy of the algorithm were verified. The experimental results show that the picking accuracy rate is 95%, the angle error of each mechanism and the displacement error are less than 0.5 degrees and 10mm, respectively. The total time from the optimized angle calculation to the system's stability is only about 0.33s. This method is suitable for the rapid perception of plucking position and active angle control of picking robots under dynamic unstructured environment.

Humanoid Robotics

An automaton may be a automaton with its body form designed to give the body. the look could also be for purposeful functions, like interacting with human tools and environments, for experimental functions, like the study of two-footed locomotion, or for alternative functions. In general, automaton robots have a body, a head, two arms, and 2 legs, though' some sorts of automaton robots could model solely a part of the body, for instance, from the waist up. Some automaton robots even have heads designed to duplicate human face expression like eyes and mouths. Androids area unit automaton robots designed to aesthetically gibe humans. Humanoid robots square measure expected to exist and add a detailed relationship with people at large |individuals| personalities} within the everyday world and to serve the wants of physically unfit people. These robots should be ready to address the wide range of tasks and objects encountered in dynamic unstructured environments. robot robots for private use for old and disabled folks should be safe and simple to use. Therefore, robot robots would like a light-weight body, high flexibility, several forms of sensors and high intelligence. The victorious introduction of those robots into human environments can have confidence the event of human friendly part

LocoGear: Locomotion Analysis of Robotic Landing Gear for Multicopters

Nowadays, there are few unmanned aerial vehicles (UAVs) that are capable of landing on uneven surfaces or moving on the complex ground. A drone having both of these functionalities can significantly improve its performance in accomplishing difficult tasks, such as monitoring and exploring various types of terrain, searching for survivors, and delivering medical aid or repair kits in an unstructured dynamic environment, e.g., rescue operation following an earthquake. This article presents a motion analysis of the gait of an adaptive landing platform for multicopters. This landing gear has four robotic legs facilitated with torque sensors in the knee joints. Though it had been developed to provide adaptive landing on uneven surfaces, we decided to exploit the system for terrain locomotion purposes. For this purpose, we have analyzed the center of mass trajectory to evaluate the possible trajectory of the robot. Based on the result of this experimental analysis, we propose a LocoGear, a novel algorithm for locomotion of UAV equipped with the robotic landing gear. The servomotor torques were estimated using Lagrangian dynamic formulation and kinetostatic methods. We conducted experiments to verify the LocoGear approach based on feedforward control that proves the capability of landing gear to move along the desired trajectory. We achieved the robot motion along the straight line with the standard deviation of length in each step of 9.8 mm and standard deviation in yaw of 9°.

A novel impedance control method of rubber unstacking robot dealing with unpredictable and time-variable adhesion force

Unpredictable and time-variable adhesion force between the rubber unstacking robot and the rubber block is generated, which makes it difficult for the robot to smoothly complete the rubber disassembly task, thereby bringing about new robot control problems. For solving the above problems, a novel method of inner/outer loop impedance control based on natural gradient actor-critic (NAC) reinforcement learning is proposed in this paper. The required impedance is applied by the inner/outer loop impedance control with time delay estimation, which can correct the modeling error and compensate the nonlinear dynamics term to improve the computational efficiency of the system. In addition, the NAC reinforcement learning algorithm based on recursive least squares filtering is used to optimize the impedance parameters online, which can improve the impedance accuracy and robustness in the unstructured dynamic environment. At the same time, three stability constraints of the control strategy are derived in the analysis process. Finally, by setting up the experimental platform, it is verified that the control strategy can make the robot work smoothly under the action of unpredictable and time-variable adhesion force to reduce vibration and improve rubber unstacking performance.

Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments

This letter presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contouring control (MPCC) and extend it to incorporate a static map by computing, online, a set of convex regions in free space. We model moving obstacles as ellipsoids and provide a correct bound to approximate the collision region, given by the Minkowsky sum of an ellipse and a circle. Our framework is agnostic to the robot model. We present experimental results with a mobile robot navigating in indoor environments populated with humans. Our method is executed fully onboard without the need of external support and can be applied to other robot morphologies such as autonomous cars.

A Semantic-Based Method for Teaching Industrial Robots New Tasks

This paper presents the results of the Artificial Intelligence (AI) method developed during the European project “Factory-in-a-day”. Advanced AI solutions, as the one proposed, allow a natural Human–Robot-collaboration, which is an important capability of robots in industrial warehouses. This new generation of robots is expected to work in heterogeneous production lines by efficiently interacting and collaborating with human co-workers in open and unstructured dynamic environments. For this, robots need to understand and recognize the demonstrations from different operators. Therefore, a flexible and modular process to program industrial robots has been developed based on semantic representations. This novel learning by demonstration method enables non-expert operators to program new tasks on industrial robots.