Virtual Obstacle Research Articles

COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships

Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship’s maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.

Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements.

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.

Automatic Route Design by Stepwise Subdivision of Virtual Walls —Reduces Route Length and Speeds Up Execution Time—

With the development of autonomous driving technology utilizing machine learning, AI, and sensors, research on autonomous driving control has become more active, and a large number of innovative studies are underway. In the near future, all autonomous vehicle fleets will be able to communicate with each other for sharing information and overall optimal traffic control will be achieved. One of the vehicle control systems that are based on the premise of such a fully automated society is the “signal-less intersection.” There is an intersection traffic control method that achieves safe and rational route selection by using virtual walls (VWs), which are virtual obstacles, but there are issues in terms of total route length and reduction of computation time. To address the issues, we propose a method that (1) prunes unneeded paths and (2) arranges VWs in a stepwise manner. The effectiveness of the proposed method was evaluated by simulation, and the results showed that the total route length and execution time were reduced.

E-DQN-Based Path Planning Method for Drones in Airsim Simulator under Unknown Environment.

To improve the rapidity of path planning for drones in unknown environments, a new bio-inspired path planning method using E-DQN (event-based deep Q-network), referring to introducing event stream to reinforcement learning network, is proposed. Firstly, event data are collected through an airsim simulator for environmental perception, and an auto-encoder is presented to extract data features and generate event weights. Then, event weights are input into DQN (deep Q-network) to choose the action of the next step. Finally, simulation and verification experiments are conducted in a virtual obstacle environment built with an unreal engine and airsim. The experiment results show that the proposed algorithm is adaptable for drones to find the goal in unknown environments and can improve the rapidity of path planning compared with that of commonly used methods.

Autonomous navigation and control of magnetic microcarriers using potential field algorithm and adaptive non-linear PID.

Microparticles are increasingly employed as drug carriers inside the human body. To avoid collision with environment, they reach their destination following a predefined trajectory. However, due to the various disturbances, tracking control of microparticles is still a challenge. In this work, we propose to use an Adaptive Nonlinear PID (A-NPID) controller for trajectory tracking of microparticles. A-NPID allows the gains to be continuously adjusted to satisfy the performance requirements at different operating conditions. An in-vitro study is conducted to verify the proposed controller where a microparticle of 100 m diameter is put to navigate through an open fluidic reservoir with virtual obstacles. Firstly, a collision-free trajectory is generated using a path-planning algorithm. Secondly, the microparticle dynamic model, when moving under the influence of external forces, is derived, and employed to design the A-NPID control law. The proposed controller successfully allowed the particle to navigate autonomously following the reference collision-free trajectory in presence of varying environmental conditions. Moreover, the particle could reach its targeted position with a minimal steady-state error of 4 m. A degradation in the performance was observed when only a PID controller was used in the absence of adaptive terms. The results have been verified by simulation and experimentally.

Development and Evaluation of a Treadmill-based Video-see-through and Optical-see-through Mixed Reality Systems for Obstacle Negotiation Training.

Mixed reality (MR) technologies have a high potential to enhance obstacle negotiation training beyond the capabilities of existing physical systems. Despite such potential, the feasibility of using MR for obstacle negotiation on typical training treadmill systems and its effects on obstacle negotiation performance remains largely unknown. This research bridges this gap by developing an MR obstacle negotiation training system deployed on a treadmill, and implementing two MR systems with a video see-through (VST) and an optical see-through (OST) Head Mounted Displays (HMDs). We investigated the obstacle negotiation performance with virtual and real obstacles. The main outcomes show that the VST MR system significantly changed the parameters of the leading foot in cases of Box obstacle (approximately 22 cm to 30 cm for stepping over 7cm-box), which we believe was mainly attributed to the latency difference between the HMDs. In the condition of OST MR HMD, users tended to not lift their trailing foot for virtual obstacles (approximately 30 cm to 25 cm for stepping over 7cm-box). Our findings indicate that the low-latency visual contact with the world and the user's body is a critical factor for visuo-motor integration to elicit obstacle negotiation.

Simplified System Integration of Robust Mobile Robot for Initial Pose Estimation for the Nakanoshima Robot Challenge

This paper describes a study of the simple system integration of a mobile robot in the Nakanoshima Robot Challenge 2022. To improve the operability of the robot at the start of its journey, we studied the solution to the problem of initial localization during the experimental run and the setting of virtual obstacles on the map to be used by the mobile robot. This method reduces the amount of time and manual operation required to estimate the initial position and orientation of a mobile robot in mobile robot experiments. In this study, a mobile robot is implemented using open-source products such as robot operating system (ROS) and i-Cart mini. Experimental runs in the Extra Challenge of the Nakanoshima Robot Challenge 2022 demonstrate the feasibility of the method.

The Robust Adaptive Control of Leader–Follower Formation in Mobile Robots with Dynamic Obstacle Avoidance

In this paper, the problem of formation control with regard to leader–follower mobile robots in the presence of disturbances and model uncertainties, without needing to know the velocity of the leader robot, is presented. For this purpose, at first, a first-order kinematic model of leader–follower and leader–leader formations is obtained, and considering the absolute velocity of the leader robots as an uncertainty, a robust adaptive controller is designed to keep the desired formation. In this case, the upper bound of uncertainty is unknown and is obtained via stable adaptive laws. Afterwards, in order to deal with the accelerated robots and obstacles, second-order leader–follower and leader–leader formation models are obtained from the previous models. A robust adaptive controller is then designed to stabilize the entire system in the presence of disturbances and modeling uncertainties, without needing to know the parameters or matrices of the formation models. In addition, by considering one of the leaders in the leader–leader model as a virtual obstacle, the challenge of avoiding moving obstacles is also addressed in the presence of uncertainties. The simulation results show the effect of the presented controllers in effectively keeping the desired leader–follower formations.

Dynamic Path Planning and Motion Control of Microrobotic Swarms for Mobile Target Tracking

Magnetic field-driven microrobotic swarms have drawn extensive attention, especially in the field of automatic control. Realizing dynamic path planning and motion control of microrobotic swarms for mobile target tracking is one of the important tasks that still remains unsolved. In this paper, we firstly present an enhanced bidirectional rapidly-exploring random tree star (EB-RRT*) algorithm considering the physical size of the swarm to dynamically plan the optimal path for obstacle avoidance. An image-guided motion controller, which consists of a direction controller and a Genetic Algorithm based Linear Quadratic Regulator (GA-LQR) velocity controller, is then proposed to realize mobile target tracking using microrobotic swarms. Targeted bursting algorithm is subsequently developed to meet the requirement of tracking high-speed ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m/s$</tex-math> </inline-formula> ) mobile targets. Simulations are performed to validate the proposed methods and obtain the proper ranges of the input parameters for the controllers. Finally, the control effectiveness of mobile target tracking in different conditions and environments is validated by experimental results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to develop an effective control scheme for mobile target tracking using microrobotic swarms. Conventional control schemes mainly focus on the control of single microrobots to reach static targets, and thus the desired path is fixed once planned. In addition, the motion of single monolithic microrobots can be modelled precisely. However, in mobile target tracking using microrobotic swarms, dynamic planning algorithms are demanded to frequently update the desired path. Swarms consisting of millions of micro-agents are also difficult to be modelled due to the complex agent-agent interactions. In this work, an effective control scheme consisting of a dynamic path planner, a motion controller and a targeted bursting unit is developed. Real-time dynamic paths will be planned even though the positions of the swarm and the target change rapidly. The precise control of the swarm direction and velocity are achieved, and moreover, using the targeted bursting algorithm, the swarm can be accelerated to approach mobile targets accurately with higher efficiency. Experimental results validates the proposed tracking strategy in different environments with virtual obstacles. The proposed control scheme paves the way for a better understanding of advanced motion control methods for microrobotic swarms.

Local minimum enhanced artificial potential field method

Currently, the artificial potential field technique is commonly used in vehicle obstacle avoidance and route planning. However, this method may lead to local minima where the gravitational force and repulsive force are equal during the path planning process, and thus the target location cannot be reached. This paper suggests an approach to enhance the artificial potential field method in light of this. First, the calculation method of the attraction field in the traditional artificial potential field is modified for resolving the issue of unreachable targets. Second, for the local minimum, the path calculation is recovered by modifying the repulsive force range of obstacles and creating a virtual obstacle point, and applying an additional force to get rid of the gravitational force and repulsive force balance. The simulation results check the effectiveness of the designed method, which can solve the cases of unreachable targets and local minima and produce reasonable planned paths.

An Improved Path Planning Algorithm With Adaptive Parameters and Predictions

An improved path planning algorithm based on dynamic window approach (DWA) is proposed to optimize real-time paths for agents moving in complex environments with plenty of static and/or dynamic obstacles, path constraints, and performance constraints. In order to improve the accuracy of the evaluation function and the adaptability of the agents to different obstacle environments, the weight of the evaluation function is ameliorated by designing adaptive laws in this article. Meanwhile, aiming at the defect of local minima in DWA algorithm, a low-speed steering strategy is presented to help the robot escape the U-shaped trap. Especially, in the case of dynamic obstacle environment, a predictive scheme is carried out for the agents to reach the destination safely by fitting the trajectory of the dynamic obstacles to calculate their possible future positions, and then setting these positions as virtual obstacles. By comparison, some simulations and experiments are illustrated to show that the proposed algorithms are superior to the DWA algorithm in terms of running time, path length, and security to a large extent.

UAV-Aided Radio Map Construction Exploiting Environment Semantics

This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground nodes. The main challenge is how to describe the signal strength attenuation due to the blockage from the environment. Conventional interpolation-type approaches fail to exploit the close relation between the radio map and the geometry of the environment. This paper proposes to construct radio maps by first estimating and constructing a multi-class 3D virtual obstacle map that describes the geometry of the environment with radio semantics. Mathematically, a least-squares problem is formulated to jointly estimate the virtual obstacle map and the propagation parameters. This problem is found to have a partial quasiconvexity that leads to the development of an efficient parameter estimation and radio map construction algorithm. Numerical experiments confirm that the proposed method substantially reduces the amount of measurement required for achieving the same radio map accuracy. It is also demonstrated that in a unmanned aerial vehicle (UAV)-aided relay communication scenario, a radio map assisted approach for UAV placement can achieve more than 50% capacity gain.

Influence of user posture and virtual exercise on impression of locomotion during VR observation.

A seated user watching his avatar walking in Virtual Reality (VR) may have an impression of walking. In this paper, we show that such an impression can be extended to other postures and other locomotion exercises. We present two user studies in which participants wore a VR headset and observed a first-person avatar performing virtual exercises. In the first experiment, the avatar walked and the participants (n=36) tested the simulation in 3 different postures (standing, sitting and Fowler's posture). In the second experiment, other participants (n=18) were sitting and observed the avatar walking, jogging or stepping over virtual obstacles. We evaluated the impression of locomotion by measuring the impression of walking (respectively jogging or stepping) and embodiment in both experiments. The results show that participants had the impression of locomotion in either sitting, standing and Fowler's posture. However, Fowler's posture significantly decreased both the level of embodiment and the impression of locomotion. The sitting posture seems to decrease the sense of agency compared to standing posture. Results also show that the majority of the participants experienced an impression of locomotion during the virtual walking, jogging, and stepping exercises. The embodiment was not influenced by the type of virtual exercise. Overall, our results suggest that an impression of locomotion can be elicited in different users' postures and during different virtual locomotion exercises. They provide valuable insight for numerous VR applications in which the user observes a self-avatar moving, such as video games, gait rehabilitation, training, etc.

Research on Static/Dynamic Global Path Planning Based on Improved A ∗ Algorithm for Mobile Robots

In view of the problems of A ∗ algorithm in path planning, such as collision risk, the path is not necessarily optimal, and there are numerous turning nodes. Therefore, this study proposes an improved A ∗ algorithm to improve the quality of the planned path. First, the 8-neighborhood children of the parent node are generated, and the security of the planned path is improved by further investigating the properties of the neighbors of these children one by one to reasonably set virtual obstacles. Second, the heuristic function of A ∗ algorithm is ameliorated to make it closer to the actual cost, so as to enhance the accuracy of the planned path; finally, the planned path is smoothed by using the cubic uniform B-spline curve to eliminate corner sharp points in the path. The simulation results show that the improved A ∗ algorithm can not only ensure the safety and smoothness of the planned path but also obtain the shortest planned path in the static environment with different obstacle rates. In addition, we combine the improved A ∗ algorithm with the dynamic window algorithm to enable mobile robots to realize real-time dynamic obstacle avoidance while ensuring the optimality of global path planning.

Research on Real-Time Obstacle Avoidance Motion Planning of Industrial Robotic Arm Based on Artificial Potential Field Method in Joint Space

The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for modern factory work patterns, and it is difficult to handle situations when the robotic arm encounters singular configurations. In this paper, we propose an improved artificial potential field method in joint space, which effectively improves the real-time performance of the algorithm, and still performs well when the robotic arm falls into a singular configuration. This method solves the gradient of the repulsive potential field in advance by defining the shortest distance from each joint of the robotic arm to the obstacle, and only needs to calculate potential field function once per cycle, which significantly reduces the calculation time. In addition, when a robotic arm falls into a local minimum position in potential field, the algorithm adds a virtual obstacle to make it leave the position, while this virtual obstacle does not require additional input information. Experimental results show that the algorithm obtains short movement paths and requires very little computing time in the face of different obstacles.

Path Planning Algorithm for a Wheel-Legged Robot Based on the Theta* and Timed Elastic Band Algorithms

Aimed at the difficulty of path planning resulting from the variable configuration of the wheel-legged robot for future deep space explorations, this paper proposes a path planning algorithm based on the Theta* algorithm and Timed Elastic Band (TEB) algorithm. Firstly, the structure of the wheel-legged robot is briefly introduced, and the workspace of a single leg is analyzed. Secondly, a method to judge complete obstacles and incomplete obstacles according to the height of the obstacles is proposed alongside a method to search for virtual obstacles, to generate a grid map of the wheel and a grid map of the body, respectively. By dividing obstacles into complete obstacles and incomplete obstacles, the path planning of the wheel-legged robot is split into the planning of the body path and the planning of the wheel path. The body can be still simplified as a point by searching for the virtual obstacle, which avoids the difficulty of a planning path of a variable shape. Then, we proposed hierarchical planning and multiple optimization algorithms for the body path and wheel path based on the Theta* algorithm and TEB algorithm. The path can be optimized and smoothed effectively to obtain a shorter length and higher safety. On that basis, the proposed algorithm is simulated by Matlab. The results of simulations show that the algorithm proposed in this paper can effectively plan the path of the wheel-legged robot by using variable configurations for different types of obstacles. The path-planning algorithm of the wheel-legged robot proposed in this paper has a broad prospect for deep space exploration.

An obstacle avoidance strategy for complex obstacles based on artificial potential field method

AbstractWhen there are obstacles around the target point, the mobile robot cannot reach the target using the traditional artificial potential field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three‐point collinear or semiclosed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy‐APF has been proposed in this paper. There are two main advantages of the proposed method. First, by redefining the gravitational function as a logarithmic function, the proposed method can make the mobile robot reach the target point when there are obstacles around the target. Second, the proposed method can avoid falling into local oscillation for both three‐point collinear and semiclosed obstacles. Compare with APF and other improved APF, the feasibility of the algorithm is proved through software simulation and practical application.

Enhancement of awareness through feedback does not lead to interlimb transfer of obstacle crossing in virtual reality

Locomotor skill transfer is an essential feature of motor adaptation and represents the generalization of learned skills. We previously showed that gait adaptation after crossing virtual obstacles did not transfer to the untrained limb and suggested it may be due to missing feedback of performance. This study investigated whether providing feedback and an explicit goal during training would lead to transfer of adaptive skills to the untrained limb. Thirteen young adults crossed 50 virtual obstacles with one (trained) leg. Subsequently, they performed 50 trials with their other (transfer) leg upon notice about the side change. Visual feedback about crossing performance (toe clearance) was provided using a color scale. In addition, joint angles of the ankle, knee, and hip were calculated for the crossing legs. Toe clearance decreased with repeated obstacle crossing from 7.8 ± 2.7 cm to 4.6 ± 1.7 cm for the trained leg and from 6.8 ± 3.0 cm to 4.4 ± 2.0 cm (p < 0.05) for the transfer leg with similar adaptation rates between limbs. Toe clearance was significantly higher for the first trials of the transfer leg compared to the last trials of the training leg (p < 0.05). Furthermore, statistical parametric mapping revealed similar joint kinematics for trained and transfer legs in the initial training trials but differed in knee and hip joints when comparing the last trials of the trained leg with the first trials of the transfer leg. We concluded that locomotor skills acquired during a virtual obstacle crossing task are limb-specific and that enhanced awareness does not seem to improve interlimb transfer.

Improving virtual reality navigation tasks using a haptic vest and upper body tracking

Haptic feedback is an important component of immersive virtual reality (VR) applications that is often suggested to complement visual information through the sense of touch. This paper investigates the use of a haptic vest in navigation tasks. The haptic vest produces a repulsive vibrotactile feedback from nearby static virtual obstacles that augments the user spatial awareness. The tasks require the user to perform complex movements in a 3D cluttered virtual environment, like avoiding obstacles while walking backwards and pulling a virtual object. The experimental setup consists of a room-scale environment. Our approach is the first study where a haptic vest is tracked in real time using a motion capture device so that proximity-based haptic feedback can be conveyed according to the actual movement of the upper body of the user.User study experiments have been conducted with and without haptic feedback in virtual environments involving both normal and limited visibility conditions. A quantitative evaluation was carried out by measuring task completion time and error (collision) rate. Multiple haptic rendering techniques have also been tested. Results show that under limited visibility conditions proximity-based haptic feedback generated by a wearable haptic vest can significantly reduce the number of collisions with obstacles in the virtual environment.

Online path planning of cooperative mobile robots in unknown environments using improved Q‐Learning and adaptive artificial potential field

Online path planning in unknown environments with obstacles is a challenging issue in mobile robot systems. Cooperative mobile robots have received much attention in recent years due to their robustness and ability to perform more complex tasks and operations faster. In this article, by unifying the Dyna Q-learning and a novel concept (introduced as a feature matrix) an improved Q-learning is proposed that accelerates the learning process even with poor choice of parameters. In addition, to overcoming the local minima, an adaptive artificial potential field method is developed, so that two parameters of the new concept named the virtual obstacle are controlled using the massachusetts institute of technology (MIT) method to navigate the mobile robot in the proper direction. Then, with proper utilizing of the improved Q-learning and adaptive artificial potential field method, the path planning is performed online effectively while target tracking and collision avoidance are guaranteed. Finally, the performance of the proposed method is tested on decentralized cooperative mobile robots. The simulation results showed optimal path planning in terms of the distance in an unknown environment and collision avoidance with the obstacles. In addition, getting out of the local minima (i.e. immobility) is guaranteed in all situations.