Obstacle Avoidance Strategy Research Articles

Path planning and collision avoidance methods for distributed multi-robot systems in complex dynamic environments.

Multi-robot systems are experiencing increasing popularity in joint rescue, intelligent transportation, and other fields. However, path planning and navigation obstacle avoidance among multiple robots, as well as dynamic environments, raise significant challenges. We propose a distributed multi-mobile robot navigation and obstacle avoidance method in unknown environments. First, we propose a bidirectional alternating jump point search A* algorithm (BAJPSA*) to obtain the robot's global path in the prior environment and further improve the heuristic function to enhance efficiency. We construct a robot kinematic model based on the dynamic window approach (DWA), present an adaptive navigation strategy, and introduce a new path tracking evaluation function that improves path tracking accuracy and optimality. To strengthen the security of obstacle avoidance, we modify the decision rules and obstacle avoidance rules of the single robot and further improve the decision avoidance capability of multi-robot systems. Moreover, the mainstream prioritization method is used to coordinate the local dynamic path planning of our multi-robot systems to resolve collision conflicts, reducing the difficulty of obstacle avoidance and simplifying the algorithm. Experimental results show that this distributed multi-mobile robot motion planning method can provide better navigation and obstacle avoidance strategies in complex dynamic environments, which provides a technical reference in practical situations.

AUV Dynamic Obstacle Avoidance Method Based on Improved PPO Algorithm

A reasonable obstacle avoidance method for AUV 3D path planning is difficult. Existing obstacle avoidance methods have certain drawbacks. For example, they are only applicable to 2D planar applications and cannot effectively handle dynamic obstacles. To address these problems, this study designs an obstacle collision prediction model (CPM). Based on the results of the simulation operation of the obstacle inertial motion, the safety of the AUV navigation is evaluated to improve the sensitivity of the model to dynamic obstacles. Then, the learning ability of the sequence sample data is enhanced by combining it with the long short-term memory (LSTM) network, and the training efficiency and effect of the algorithm are improved. The trained proximal policy optimization (PPO) network can output reasonable actions to control the AUV to avoid obstacles, forming an AUV 3D dynamic obstacle avoidance strategy based on CPM-LSTM-PPO algorithm. The simulation results show that the proposed algorithm has good generalization in uncertain environments, and successfully realizes dynamic obstacle avoidance of AUV in different three-dimensional unknown environments, providing theoretical and technical support for real path planning.

Obstacle avoidance strategy for an autonomous surface vessel based on modified deep deterministic policy gradient

In the present paper, a decision-making agent based on reinforcement learning is designed for establishing an obstacle avoidance strategy of an autonomous surface vessel (ASV). To solve the sparse feedback in obstacle avoidance issue, the Modified Deep Deterministic Policy Gradient (MDDPG) method is proposed in the present study, using an actor-network to generate actions from the state of the ASV and a critic-network to evaluate the behavior of the actor-network. The gradient descend method is applied to improve the networks. Memory pool modification, success pool modification and target network modification are developed to smooth the training process of the ASV. A comparative analysis based on numerical simulations are carried out using Deep Q Network (DQN) method, Modified Deep Q Network (MDQN) method and MDDPG method. The results demonstrate that the MDDPG method smooths and accelerates the learning process. Compared with the other two methods, the effectiveness and viability of the proposed method is significantly increased. Random obstacles tests are also conducted to confirm the applicability and generalization of the method.

Collision Avoidance Algorithm for USV Based on Rolling Obstacle Classification and Fuzzy Rules

Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions.

Eye-hand coordination: memory-guided grasping during obstacle avoidance.

When reaching to grasp previously seen, now out-of-view objects, we rely on stored perceptual representations to guide our actions, likely encoded by the ventral visual stream. So-called memory-guided actions are numerous in daily life, for instance, as we reach to grasp a coffee cup hidden behind our morning newspaper. Little research has examined obstacle avoidance during memory-guided grasping, though it is possible obstacles with increased perceptual salience will provoke exacerbated avoidance maneuvers, like exaggerated deviations in eye and hand position away from obtrusive obstacles. We examined the obstacle avoidance strategies adopted as subjects reached to grasp a 3D target object under visually-guided (closed loop or open loop with full vision prior to movement onset) and memory-guided (short- or long-delay) conditions. On any given trial, subjects reached between a pair of flanker obstacles to grasp a target object. The positions and widths of the obstacles were manipulated, though their inner edges remained a constant distance apart. While reach and grasp behavior was consistent with the obstacle avoidance literature, in that reach, grasp, and gaze positions were biased away from obstacles most obtrusive to the reaching hand, our results reveal distinctive avoidance approaches undertaken depend on the availability of visual feedback. Contrary to expectation, we found subjects reaching to grasp after a long delay in the absence of visual feedback failed to modify their final fixation and grasp positions to accommodate the different positions of obstacles, demonstrating a more moderate, rather than exaggerative, obstacle avoidance strategy.

UAV formation obstacle avoidance control method based on artificial potential field and consistency

As a typical multi-agent formation, UAV formation is playing an increasingly powerful role in the civilian and military fields. Obstacle avoidance, as an important technology in controlling formation, determines the application prospects of UAVs. This paper studies the time-varying formation of UAVs with interactive topology to avoid obstacles, aiming to improve the ability of UAV formations to deal with complex environments while traveling. Firstly, a repulsive force field is reasonably introduced based on the existing control scheme, and an improved distributed time-varying formation control scheme based on artificial potential field is proposed. Then combined with the basic idea of model predictive control, an obstacle avoidance strategy in which UAV obstacle avoidance and formation shaping are carried out simultaneously is proposed. Finally, a time-varying formation simulation experiment containing four UAVs was carried out to verify the validity of the results.

다중 장애물의 인지적 과제 수행이 노인의 장애물 보행에 미치는 영향

It has been known that the presence of obstacles and deterioration of cognitive function contribute to increased fall rates in older adults. Although age-related changes in obstacle avoidance strategies in the elderly have been studied extensively, little is investigated about adaptive characteristics regarding how elderly people cross multiple obstacles under concurrent cognitive tasks. Nine healthy older and 9 young adults participated in the study. We examined how single or multiple obstacle conditions influence the planning and execution of obstacle avoidance performance under dual-task conditions. Elderly people demonstrated a longer stance time before crossing the first obstacle than young adults when the task complexity increased. These stepping characteristics were more evident in the dual-task condition. However, other gait outcomes were not significantly affected by the level of task complexity when compared with young adults. These results indicate that changes in task demands appeared to influence the planning of stepping strategies rather than the execution for obstacle crossing performance in healthy older adults.

Obstacle Avoidance Control for Multisteering Mode of Multiaxle Wheeled Robot Based on Trajectory Prediction Strategy

A multiaxle wheeled robot is difficult to be controlled due to its long body and a large number of axles, especially for obstacle avoidance and steering in narrow space. To solve this problem, a multisteering mode control strategy based on front and rear virtual wheels is proposed, and the driving trajectory prediction of the multiaxle wheeled robot is analyzed. On this basis, an obstacle avoidance control strategy based on trajectory prediction is proposed. By calculating the relationship between the lidar points of the obstacle and the trajectory coverage area, the iterative calculation of the obstacle avoidance scheme for the proposed steering is carried out, and the feasible obstacle avoidance scheme is obtained. The mechanical structure, hardware, and software control system of a five-axle wheeled robot are designed. Finally, to verify the effectiveness of the obstacle avoidance strategy, a Z-shaped obstacle avoidance experiment was carried out. The results confirm the effectiveness of the proposed control strategy.

Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection.

The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually.

A Monocular Vision Obstacle Avoidance Method Applied to Indoor Tracking Robot

The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes.

Research on an Obstacle Avoidance Method for UAV

With the continuous development of UAV technology, UAV has been widely used in various industries. In the flight process of UAV, UAV often changes the given path because of obstacles (including static nonliving body and moving living body). According to the properties of obstacles and the characteristics of UAV, standard Kalman filter is used for nonmaneuvering targets, and sigma point Kalman filter is used for maneuvering targets. In the aspect of obstacle avoidance, the minimum search method is used to get the initial population of local programming. Then, the improved genetic algorithm is run. Combined with the predicted obstacle features, the local planning path can be obtained. Finally, the local planning path and global planning path are combined to generate the planning path with new obstacles. At the end of the paper, the obstacle avoidance strategies of static and moving obstacles are simulated. The simulation results show that this method has fast convergence speed and good feasibility and can flexibly deal with the obstacle avoidance and local path planning of various new obstacles.

MPC Based Trajectory Tracking for An Automonous Deep-Sea Tracked Mining Vehicle

Model predictive control (MPC) has been successfully used in trajectory tracking for autonomous vehicles based on certain kinematic model under low external disturbance conditions, but when there are model uncertainties and external disturbances, autonomous vehicles will fail to follow the pre-set trajectory. This paper studies trajectory tracking control based on MPC for an autonomous deep-sea tracked mining vehicle in polymetallic nodule mines with model uncertainty and external disturbances. A MPC algorithm is designed for trajectory tracking. To address model uncertainties caused by vehicle body subsidence and track slippage, a drive wheel speed correction controller is designed by experimental data fitting, and Kalman filtering (KF) and adaptive Kalman filtering (AKF) are introduced to improve tracking performance by rejecting external disturbances especially during curve tracking. To handle dead zones and obstacles during actual operation, an obstacle avoidance strategy is proposed that uses the tri-circular arc obstacle avoidance trajectory with an equal curvature for path re-planning. Finally, Simulink&Recurdyn co-simulations validate the performance of the proposed MPC controller through a comparison with nonlinear MPC(NMPC).

Analysis of Obstacle Avoidance Strategy for Dual-Arm Robot Based on Speed Field with Improved Artificial Potential Field Algorithm

In recent years, dual-arm robots have been favored in various industries due to their excellent coordinated operability. One of the focused areas of study on dual-arm robots is obstacle avoidance, namely path planning. Among the existing path planning methods, the artificial potential field (APF) algorithm is widely applied in obstacle avoidance for its simplicity, practicability, and good real-time performance over other planning methods. However, APF is firstly proposed to solve the obstacle avoidance problem of mobile robot in plane, and thus has some limitations such as being prone to fall into local minimum, not being applicable when dynamic obstacles are encountered. Therefore, an obstacle avoidance strategy for a dual-arm robot based on speed field with improved artificial potential field algorithm is proposed. In our method, the APF algorithm is used to establish the attraction and repulsion functions of the robotic manipulator, and then the concepts of attraction and repulsion speed are introduced. The attraction and repulsion functions are converted into the attraction and repulsion speed functions, which mapped to the joint space. By using the Jacobian matrix and its inverse to establish the differential velocity function of joint motion, as well as comparing it with the set collision distance threshold between two robotic manipulators of robot, the collision avoidance can be solved. Meanwhile, after introducing a new repulsion function and adding virtual constraint points to eliminate existing limitations, APF is also improved. The correctness and effectiveness of the proposed method in the self-collision avoidance problem of a dual-arm robot are validated in MATLAB and Adams simulation environment.

The Optimization Model of Composite Material UAV Shooting Flight Routes

UAV is one of the important achievements of scientific and technological innovation, which has been widely used in various fields of life in recent years. UAVs of composite materials are the most popular because the composite itself has designability, and can be optimized according to the strength and stiffness of the aircraft without changing the structural weight. In order to ensure the UAV to carry out more accurate image acquisition under the actual ground conditions, through the analysis of decision variables, this paper uses the linear weighting of image shooting range and pixel accuracy to establish the objective function, and establishes the optimal shooting angle under different constraints Considering the actual motion characteristics of objects such as automobiles, this paper assumes that the tracked ground targets are a class of motion objects with non-integrity constraints when the flight altitude of UAV is fixed and the flight speed is constant. The ground target model and the flight dynamics nonlinear model are established. This paper presents a new RRT* algorithm for path planning based on the obstacles encountered by UAV during flight, that is, using the cost function to select the node with the minimum cost in the field of expanding nodes as the parent node, using the MATLAB to select a reasonable obstacle avoidance strategy, the global optimal route is obtained by smoothing processing, and the data simulation test is carried out. The experimental results show that the model is robust.

Human-Machine Cooperation and Path Planning for Complex Road Conditions

With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation have become the development trend of industrial robot. In this paper, the human-machine cooperation and path optimization of industrial robot in a complex road environment are studied and analyzed. At the theoretical modeling level, firstly, the industrial robot is modeled and obstacle avoidance is analyzed based on the kinematics of industrial robot; thus, an efficient and concise collision detection model of industrial robot is proposed. At the algorithm level, in view of the complex road conditions faced by industrial robots, this paper will study and analyze the obstacle avoidance strategy of human-computer cooperation and real-time path optimization algorithm of industrial robots. Based on the virtual target point algorithm, this paper further improves the problem that the goal of the traditional path planning algorithm cannot be fully covered, so as to propose the corresponding improved path planning algorithm of industrial robots. In the experimental part, based on the existing industrial robot system, the human-machine cooperation and path planning system proposed in this paper are designed. The experimental results show that the algorithm proposed in this paper improves the accuracy of obstacle avoidance by about 10 points and the corresponding convergence speed by about 5% compared with the traditional algorithm and the experimental effect is remarkable.

Card posting does not rely on visual orientation: A challenge to past neuropsychological dissociations

A common set of tasks frequently employed in the neuropsychological assessment of patients with visuomotor or perceptual deficits are the card-posting and the perceptual orientation matching tasks. In the posting task, patients have to post a card (or their hand) through a slot of varying orientations while the matching task requires them to indicate the slot's orientation as accurately as possible. Observations that damage to different areas of the brain (dorsal vs. ventral stream) is associated with selective impairment in one of the tasks - but not the other - has led to the suggestion that different cortical pathways process visual orientation information for perception versus action. In three experiments, we show that this conclusion may be premature as posting does not seem to rely on the processing of visual orientation information but is instead performed using obstacle avoidance strategies that require an accurate judgement of egocentric distances between the card's and the slot's edges. Specifically, we found that while matching is susceptible to the oblique effect (i.e., common perceptual orientation bias with higher accuracy for cardinal than oblique orientations), this was not the case for posting, neither in immediate nor in memory-guided conditions. In contrast to matching, posting errors primarily depended on biomechanical demands and reflected a preference for performing efficient and comfortable movements. Thus, we suggest that previous dissociations between perceptual and visuomotor performance in letter posting tasks are better explained by impairments in egocentric and allocentric spatial processing than by independent visual processing systems.

Space robot motion path planning based on fuzzy control algorithm

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm

Both emergency braking and active steering are possible choices for collision avoidance manoeuvres, and any obstacle avoidance strategy aims to design a control algorithm preventing accidents. However, the real-time path needs to consider the motion state of surrounding participants on the road. This work presents a collision avoidance algorithm containing the path-planning and the tracking controller. Firstly, the lateral lane-changing spacing model and the longitudinal braking distance model are presented, describing the vehicle to reactively process dynamic scenarios in real environments. Then, we introduce the safety distance into the artificial potential field algorithm (APF), thereby generating a safe path in a simulated traffic scene. Redesigning the influence range of obstacles based on the collision areas and corresponding safety distance compared with the classic APF. Besides, based on the threat level, the repulsion is divided into the force of the position repulsion and the speed repulsion. The former is related to the relative position and prevents the vehicle from approaching the obstacle. The latter is opposite to the relative speed vector and decelerates the ego vehicle. Simultaneously, the attraction is improved to apply a dynamic environment. Finally, we design a model predictive control (MPC) to track the lateral motion through steering angle and a Fuzzy-PID control to track the longitudinal speed, turning the planned path into an actual trajectory with stable vehicle dynamics. To verify the performance of the proposed method, three cases are simulated to obtain the vehicle responding curves. The simulation results prove that the active collision avoidance algorithm can generate a safe path with comfort and stability.

Nonlinear Model Predictive Path Following Controller with Obstacle Avoidance

In the control systems community, path-following refers to the problem of tracking an output reference curve. This work presents a novel model predictive path-following control formulation for nonlinear systems with constraints, extended with an obstacle avoidance strategy. The method proposed in this work simultaneously provides an optimizing solution for both, path-following and obstacle avoidance tasks in a single optimization problem, using Nonlinear Model Predictive Control (NMPC). The main idea consists in extending the existing NMPC controllers by the introduction of an additional auxiliary trajectory that maintains the feasibility of the successive optimization problems even when the reference curve is unfeasible, possibly discontinuous, relaxing assumptions required in previous works. The obstacle avoidance is fulfilled by introducing additional terms in the value functional, rather than imposing state space constraints, with the aim of maintaining the convexity of the state and output spaces. Simulations results considering an autonomous vehicle subject to input and state constraints are carried out to illustrate the performance of the proposed control strategy.

Autonomous Obstacle Avoidance Scheme Using Monocular Vision Applied to Mobile Robots

Mobile robots cannot move in an unknown environment with static or slow-moving obstacles effectively. We present an enhanced obstacle avoidance strategy using monocular vision to solve this problem. First, we combine Canny and Otsu to extract the barrier feature and find the critical pixel position of obstacles by the monocular vision. Then the image depth estimation algorithm is used to estimate the gaps. With these parameters of barriers, an improved bug algorithm is proposed to avoid the obstacles autonomously. The experiments show that the proposed obstacles avoidance strategy can effectively make a small mobile robot avoid different kinds of obstacles.