Autonomous Obstacle Avoidance Research Articles

A high-sensitivity flexible bionic tentacle sensor for multidimensional force sensing and autonomous obstacle avoidance applications

Bionic tentacle sensors are important in various fields, including obstacle avoidance, human‒machine interfaces, and soft robotics. However, most traditional tentacle sensors are based on rigid substrates, resulting in difficulty in detecting multidirectional forces originating from the external environment, which limits their application in complex environments. Herein, we proposed a high-sensitivity flexible bionic tentacle sensors (FBTSs). Specifically, the FBTS featured an ultrahigh sensitivity of 37.6 N−1 and an ultralow detection limit of 2.4 mN, which benefited from the design of a whisker-like signal amplifier and crossbeam architecture. Moreover, the FBTS exhibited favorable linearity (R2 = 0.98) and remarkable durability (more than 5000 cycles). This was determined according to the improvement in the uniformity of the sensing layer through a high-shear dispersion process. In addition, the FBTS could accurately distinguish the direction of external stimuli, resulting in the FBTS achieving roughness recognition, wind speed detection and autonomous obstacle avoidance. In particular, the ability of autonomous obstacle avoidance was suitably demonstrated by leading a bionic rat through a maze with the FBTS. Notably, the proposed FBTS could be widely applied in tactile sensing, orientation perception, and obstacle avoidance.

Microwave Coincidence Imaging with Phase-Coded Stochastic Radiation Field

Microwave coincidence imaging (MCI) represents a novel forward-looking radar imaging method with high-resolution capabilities. Most MCI methods rely on random frequency modulation to generate stochastic radiation fields, which introduces the complexity of radar systems and imposes limitations on imaging quality under noisy conditions. In this paper, microwave coincidence imaging with phase-coded stochastic radiation fields is proposed, which generates spatio-temporally uncorrelated stochastic radiation fields with phase coding. Firstly, the radiation field characteristics are analyzed, and the coding sequences are designed. Then, pulse compression is applied to achieve a one-dimensional range image. Furthermore, an azimuthal imaging model is built, and a reference matrix is derived from the frequency domain. Finally, sparse Bayesian learning (SBL) and alternating direction method of multipliers (ADMM)-based total variation are implemented to reconstruct targets. The methods have better imaging performance at low signal-to-noise ratios (SNRs), as shown by the imaging results and mean square error (MSE) curves. In addition, compared with the SBL and ADMM-based total variation methods based on the direct frequency-domain solution, the proposed method’s computational complexity is reduced, giving it great potential in forward-looking high-resolution scenarios, such as autonomous obstacle avoidance with vehicle-mounted radar and terminal guidance.

Electronic brake system-based hierarchical motion control of commercial vehicle for autonomous obstacle avoidance

As a high-level autonomous driving technology, efficient and safe automatic obstacle avoidance on highway is one of the critical and ongoing topics that requires in-depth research for commercial vehicles. Electronic control air pressure brake system, owing to its fast response and active braking ability, is an effective active-chassis technology to function path tracking and stability control in such condition. Yaw motion stability control problem of autonomous driving commercial vehicle based on active braking control with electronic air brake system for automatic obstacle avoidance is investigated. First, a hierarchical yaw motion dynamics control architecture is introduced after vehicle and brake system modeling. The quintic polynomial curve-based trajectory planning method and a Model Predictive Control based trajectory tracking control strategy are formulated. Second, to evade the possible stability losing issue in this circumstance, Cubature Kalman Filter (CKF) is utilized to estimate the vehicle motion states, and a fuzzy PID control-based differential brake stability control strategy is designed for the electronic brake system composed of front-and-rear two sets of dual-channel pressure regulator and ABS solenoid valve. Finally, the proposed hierarchical yaw stability control strategy for a commercial vehicle in typical obstacle avoidance conditions is comparatively verified based on the joint simulation tools. After the yaw stability control, the peak value of the lateral displacement error of trajectory tracking can be reduced by 44.7%. The yaw stability control strategy based on fuzzy PID control can reduce the yaw rate by 46%–57% and the sideslip angle by 60%–73% under different conditions. The results show that the proposed control strategy can effectively improve the yaw stability of the vehicle while avoiding obstacles at high speed.

A hybrid RVO-MPPI approach for efficient collision avoidance for multiple autonomous underwater vehicles

Collision-avoidance path planning for Autonomous Underwater Vehicles (AUVs) in complex underwater environments presents a significant challenge, particularly considering the maneuverability limitations of fin-rudder-controlled AUVs. This study proposes a novel navigation approach that combines the strengths of Reciprocal Velocity Obstacle (RVO) principles and Model Predictive Path Integral (MPPI) control to enhance autonomous obstacle avoidance for multiple AUVs. The key innovation lies in leveraging RVO-based obstacle avoidance velocities to enhance both action sampling and cost function optimization in MPPI framework. Specifically, a neural network trained through supervised learning is utilized to convert the RVO-provided avoidance velocities into desired actions for MPPI. Additionally, these velocities are incorporated into the MPPI cost function, thereby enhancing the optimization process and facilitating convergence towards optimal, collision-free navigation solutions. Simulation experiments validated the method's capability to provide effective obstacle avoidance behavior across various complex scenarios. Results indicate that this approach successfully integrates MPPI's predictive capabilities with RVO's velocity avoidance logic, enabling AUVs to navigate efficiently through complex underwater environments while ensuring mission safety and effectiveness. This approach offers a promising new solution for autonomous navigation of AUVs, with potential applications in ocean exploration, underwater search and rescue, and other maritime operations. Future research will explore the performance of this approach in real-world underwater environments and consider the impact of additional dynamic factors.

Collision probability sliding mode guidance for spacecraft autonomous obstacle avoidance under state uncertainty

Spacecraft must possess the capability of autonomous obstacle avoidance to ensure flight safety. Currently, the available methods for obstacle avoidance guidance are primarily deterministic and will fail with large state estimation errors, which necessitates the robustness of guidance algorithms towards uncertainties. A collision probability sliding mode guidance method is presented for the autonomous obstacle avoidance of spacecraft. Based on the multiple sliding mode surfaces guidance law, a sliding mode structure is proposed to realize obstacle avoidance in the presence of state uncertainty, taking into account the collision probability constraint. First, A multiple power reaching law is designed to enhance the convergence speed of the first sliding mode surface, so that the spacecraft system reaches the target state within a finite time. Subsequently, the collision probability gradient information function is defined according to the relative relation between the spacecraft and obstacles. This function is then combined with the improved reaching law to develop the second sliding mode surface. The sliding mode surface can quantify the collision probability in real-time, and generate corresponding obstacle avoidance guidance commands to prompt the spacecraft to move away from the obstacles according to the magnitude of collision probability, which is robust to the state uncertainty. The capability of the proposed guidance algorithm is validated by a series of simulations, including scenarios involving asteroid landing and spacecraft rendezvous, and the effectiveness and robustness in the face of uncertainties are confirmed.

An active SLAM with multi-sensor fusion for snake robots based on deep reinforcement learning

Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.

State-dependent multi-agent discrete event simulation for urban rail transit passenger flow

Urban rail transit passenger flow modeling is the foundation of urban rail transit planning, design, and operation. The motivation of this paper is to accurately and efficiently simulate passenger flow dynamics in urban rail transit systems. To this end, we propose a State-dependent Multi-agent Discrete Event Simulation (SdMaDES). The state-dependence (or congestion-dependence) means that the service abilities (or travel times) of bottleneck facilities (e.g. platform screen doors and transfer corridors) are not constant and they interact with the congestion dynamics. Specifically, we first establish a modular Multi-agent Discrete Event Simulation (MaDES), which includes four types of modules (passenger, train, station, and network modules) and three types of agents (passenger, train, and station agents). The logical connections and event-triggering modes between the modules are analyzed and defined, and thirty types of events are designed. The state-dependence is then captured by an Improved Social Force Model (ISFM), which adds an autonomous obstacle avoidance mechanism. The ISFM reproduces passenger movement behavior within bottleneck facilities of urban rail transit systems at the microscopic level. These state-dependent functions or general rules are explicitly formulated by fitting the results of ISFM and are subsequently applied to the proposed MaDES model, resulting in the SdMaDES. This integration aims to enhance the accuracy of the simulation. We conducted a real case from the Chengdu Metro network. Some interesting results are found. (a) The maximum number of boarding passengers in a train carriage is a complex nonlinear function that is dependent on the state (density inside the train carriage). This challenges the linear function commonly utilized in most studies. (b) Compared to the actual data, the proposed SdMaDES model shows a cumulative error of 9.85% after data smoothing, while the conventional MaDES model exhibits a much higher cumulative error of 21.9% after data smoothing. (c) As the overall traffic demand level increases, the gap between the two simulation models’ results is getting wider and wider due to the amplified nonlinear impact of congestion.

Design and application of UAV single target tracking system based on YOLOv5

In aerial reconnaissance, pursuit, and post-disaster search and rescue, rotorcraft UAVs are valued for their agility. A key challenge is target tracking in complex environments with no prior information, dense obstacles, GNSS signal loss, and occluded targets. This paper introduces a YOLOv5-based UAV tracking system to address detection and path planning. The quadrotor UAV model ensures stable flight through mathematical modeling and simulation. YOLOv5 enhances detection performance with real-time tracking. Feedback control enables autonomous obstacle avoidance and target tracking. Results show adaptability and robustness, supporting reconnaissance and rescue missions effectively.

Multiple unmanned ship coverage and exploration in complex sea areas

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Development Analysis of Key Autonomous Perception Applied to Path Planning and Autonomous Obstacle Avoidance of Land Wheeled Mobile Robots

Development Analysis of Key Autonomous Perception Applied to Path Planning and Autonomous Obstacle Avoidance of Land Wheeled Mobile Robots

Cooperative autonomous obstacle avoidance control of spacecraft cluster via event-triggered reference governors

This paper conducts research on the design of a cooperative obstacle avoidance control mechanism for spacecraft cluster. It proposes an event-triggered reverse command regulator to address the current unidirectional obstacle avoidance mechanism, which only goes from the cluster reference command to the individual execution of spacecraft. The proposed mechanism enhances the survivability of spacecraft cluster in complex environments. By employing the designed event-triggered command regulator, the spacecraft can autonomously decide whether to execute a leader-following task based on whether the tracking error satisfies the performance requirements of the cluster closed-loop system. This allows the spacecraft to dynamically choose between task execution and temporarily “forgetting” the task to focus on collision avoidance, thereby increasing the collaborative flexibility of the spacecraft cluster. Furthermore, based on Lyapunov stability theory, the convergence of the proposed cooperative obstacle avoidance control algorithm is theoretically proven. Ultimately, simulation results demonstrate the real-time effectiveness of the designed cooperative obstacle avoidance solution.

Autonomous robotic drone system for mapping forest interiors

Abstract. During the last decade, the use of drones in forest monitoring and remote sensing has become highly popular. While most of the monitoring tasks take place in high altitudes and open air, in the last few years drones have also gained interest in under-canopy data collection. However, flying under the forest canopy is a complex task since the drone can not use Global Navigation Satellite Systems (GNSS) for positioning and it has to continually avoid obstacles, such as trees, branches, and rocks, on its path. For that reason, drone-based data collection under the forest canopy is still mainly based on manual control by human pilots. Autonomous flying in GNSS-denied obstacle-rich environment has been an actively researched topic in the field of robotics during the last years and various open-sourced methods have been published in the literature. However, most of the research is done purely from the point-of-view of robotics and only a few studies have been published in the boundary of forest sciences and robotics aiming to take steps towards autonomous forest data collection. In this study, a prototype of an autonomous under-canopy drone is developed and implemented utilizing state-of-the-art open-source methods. The prototype is utilizing the EGO-Planner-v2 trajectory planner for autonomous obstacle avoidance and VINS-Fusion for Visual-inertial-odometry based GNSS-free pose estimation. The flying performance of the prototype is evaluated by performing multiple test flights with real hardware in two different boreal forest test plots with medium and difficult densities. Furthermore, the first results of the forest data collecting performance are obtained by post-processing the data collected with a low-cost stereo camera during one test flight to a 3D point cloud and by performing diameter breast at height (DBH) estimation. In the medium-density forest, all seven test flights were successful, but in the difficult test forest, one of eight test flights failed. The RMSE of the DBH estimation was 3.86 cm (12.98 %).

Current Study on A* Algorithm in Autonomous Obstacle Avoidance

The A* algorithm is a heuristic search algorithm widely used in the fields of path planning and graph search. This paper aims to delve deeply into the principles, implementation, and performance optimization of the A* algorithm in practical applications. Firstly, we elaborate on the basic principles of the A* algorithm, including the definitions of cost function and heuristic function, as well as how to achieve efficient search by maintaining open lists and closed lists. Secondly, we analyze the application of the A* algorithm in path planning problems. By constructing environmental models and designing appropriate heuristic functions, we achieve optimal path search from the start point to the end point. Additionally, we explore the performance optimization methods of the A* algorithm, including strategies such as utilizing priority queues to manage nodes to be searched and dynamically adjusting heuristic functions, to improve the execution efficiency and search accuracy of the algorithm. Finally, through a series of experiments and case studies, we verify the effectiveness and superiority of the A* algorithm in path planning problems. The research results of this paper provide beneficial references and insights for the application of the A* algorithm in related fields.

An Obstacle Avoidance Strategy for AUV Based on State-Tracking Collision Detection and Improved Artificial Potential Field

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

A powered descent trajectory planning method with quantitative consideration of safe distance to obstacle

High scientific value areas on celestial bodies such as the Moon and Mars are often located in hazardous terrains. To achieve safe landing exploration, a novel planning method is proposed, which can ensure that the planned trajectory maintains a user-specified distance from obstacles, thus reducing potential collision risk induced by factors such as the body size and model uncertainties. Firstly, the basic model of the trajectory optimization problem and its convexification version is given. The obstacles are modeled as polynomial functions, based on which the “if-then” obstacle avoidance logic explicitly considering the safe distance, is described as a sum of squares constraint. This constraint formulation applies to any obstacle described by a finite number of polynomials, independent of the specific expression of the polynomials (reflecting the shape of the obstacle). Subsequently, the convexification process for the obstacle avoidance constraint is given. Finally, the sequential sum of squares programming problem for the obstacle avoidance trajectory is established, which boils down to a series of semidefinite programming problems. Simulation results show that the closest distance between the planned trajectory and obstacles strictly satisfies the specified distance constraint, and the trajectory could avoid non-convex obstacles. With the promising convergence properties of underlying convex optimization algorithms, advanced autonomous obstacle avoidance guidance schemes are expected to be formed based on the proposed trajectory planning method.

Vision-Locomotion Coordination Control for a Powered Lower-Limb Prosthesis Using Fuzzy-Based Dynamic Movement Primitives

An amputee cannot directly use the perceptual visual information to control the movements and gait patterns of his worn prosthesis. In order to help an amputee walk and cross over obstacles smoothly, the paper proposes a vision-locomotion coordination control method for a powered lower-limb prosthesis (PLLP), in which a vision system is proposed to detect obstacles, and a complete vision-locomotion loop is then constructed. With deep learning techniques, the vision system can recognize common obstacles (e.g., garbage cans, bricks and boxes) and obtain the features of obstacles (e.g., distance from obstacles to the depth camera and height of obstacles). Through integrating the vision system into the locomotion control system, the PLLP can make obstacle avoidance decisions and use dynamic movement primitives with type-2 fuzzy models (T2FDMPs) to help amputees cross over obstacles simultaneously. Utilizing the type-2 fuzzy models, smooth trajectories for crossing over obstacles can be obtained. The experimental results show that the PLLP with visual information can switch an amputee’s gaits between level walking and obstacle avoidance adaptively, which demonstrates the effectiveness of the vision-locomotion coordination control system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The paper is motivated by the challenge of obstacle avoidance of prostheses. Traditional prostheses cannot achieve autonomous obstacle avoidance because they lack of environmental perception capability. In addition, most prostheses always utilize use the human-robot interaction between amputees and prostheses to recognize the environments. However, due to noise and individual differences, recognition results are not accurate enough. We found that the integration of vision can improve the accuracy and efficiency of environmental recognition. Therefore, it is necessary to construct a complete vision and locomotion closed-loop. In the paper, a vision-locomotion coordination control is proposed, and to make the PLLP cross over obstacles smoothly, a novel trajectory shaping with fuzzy-based dynamic movement primitives is developed. The coordination control is partitioned into the obstacle detection, trajectory shaping and joint control, which can help the PLLP fulfill several obstacle avoidance tasks.

Multi-degree-of-freedom unmanned aerial vehicle control combining a hybrid brain-computer interface and visual obstacle avoidance

ObjectiveThe difficulty of unmanned aerial vehicle (UAV) control recently lies in multidirectional movement in 3-dimensional space, improving control accuracy and manipulation safety. To address these challenges, a UAV control system that incorporates a hybrid brain-computer interface (hBCI), gyroscope and visual obstacle avoidance based on monocular depth estimation is proposed. Approach. We propose an efficient steady-state visual evoked potential (SSVEP) classification network (CL-NET) featuring a one-dimensional convolutional neural network, a long short-term memory module and an attention module to identify the user's intention for UAV movement in the front, back, left and right directions. The take-off, landing and rising control of the UAV is realized by an electrooculogram (EOG) signal detection algorithm, a blink state detector. In addition, the UAV can fly in an oblique state and rotate according to the current head posture detected by a gyroscope. Furthermore, an improved monocular depth estimation network is employed to design the autonomous obstacle avoidance module of the UAV, ensuring the safety of the brain-controlled system in practice. Main results. The proposed CL-NET delivers an accuracy of 98.67% on the public dataset and an accuracy of 97.92% on the self-collected dataset, both of which surpass the performance of state-of-the-art models. Additionally, we set up a brain control group and a remote control group to conduct practical experiments in a realistic environment. In the experiments involving sixteen subjects, the proposed UAV control system reached an average information transfer rate (ITR) of 44.09 bits/min, and the brain control group had a lower collision rate than the remote control group. Significance. The hybrid control method ensures that the multi-degree-of-freedom (multi-DOF) UAV control system maintains outstanding performance while ensuring good safety.

Autonomous obstacle avoidance and target tracking of UAV: Transformer for observation sequence in reinforcement learning

Reinforcement learning (RL) is an effective approach to solve autonomous obstacle avoidance and target tracking for Unmanned Aerial Vehicle (UAV). However, due to communication interruptions or delays, transmission information loss often occurs in practical environments, which greatly reduces the success rate of UAV tracking. Currently, most research methods focus on ideal environments where UAV can fully obtain target information, but there are significant limitations in engineering practice. To solve this problem, we first formalize the UAV as a continuous partially observable Markov decision process (POMDP) and consider the loss of target state information. Then, we propose a new algorithm, namely the Transformer for Observing Sequences in Reinforcement Learning (TOSRL), which uses a transformer encoder to process observation sequences from the past to the present, and further utilizes the transformer encoder to make decisions based on the extracted features. Compared with Recurrent Neural Networks (RNN) or Long Short Term Memory (LSTM), Transformer encoders not only perform parallel processing on inputs but also alleviate their long sequence forgetting defects. The experimental results show that TOSRL is significantly superior to state-of-the-art algorithms in many different scenarios, achieving the highest tracking rate and the lowest crash rate with the least number of parameters.

A novel hybrid swarm intelligence algorithm for solving TSP and desired-path-based online obstacle avoidance strategy for AUV

Aiming at the problem that Ant Colony Optimization (ACO) is subject primarily to the parameters, we propose a hybrid algorithm SOA-ACO-2Opt to optimize the ACO parameter combination through Seagull Optimization Algorithm (SOA) to strengthen ACO’s search capability. To obtain a uniform initial distribution of the ACO parameter combination, we incorporated the Kent Chaos Map (KCM) to randomly initialize the seagull’s position, reducing the tendency of SOA to fall into the local optimum. To avoid slow calculation speed and premature convergence of ACO, we improved the adaptive multi-population mechanism to reduce repeated redundant calculations and used the ϵ−greedy and default strategy, respectively, to update the ants’ position. 2Opt is applied to find shorter paths in each iteration. In addition, when AUV navigates on the planned path, it may encounter obstacles. Therefore, this paper proposes an autonomous obstacle avoidance algorithm based on forward-looking sonar to ensure safety during tasks. SOA-ACO-2Opt is verified against twelve different problems extracted from TSPLIB and compared with some state-of-the-art algorithms. Furthermore, sea trials were carried out for several representative marine engineering applications of TSP and obstacle avoidance. Experimental results show that this work can significantly improve AUV’s work efficiency and intelligence and protect the AUV’s safety.

An investigation on the impact of human-robot interactions during an autonomous obstacle avoidance task

An investigation on the impact of human-robot interactions during an autonomous obstacle avoidance task