Obstacle Avoidance With Dynamic Avoidance Risk Region for Mobile Robots in Dynamic Environments

Guo, Binghua; Guo, Nan; Cen, Zhisong

doi:10.1109/lra.2022.3161710

Cited by 27 publications

(8 citation statements)

References 21 publications

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“…There are some recent studies considering dynamic obstacles. Guo [34] proposed an obstacle avoidance algorithm suitable for two-wheeled differential-driven robots by using a stereo vision system to detect dynamic obstacles, estimating the motion state of dynamic obstacles through an extended Kalman filter, and then by using the velocities of obstacles to calculate dynamic risk regions and realize dynamic obstacle avoidance for the robot.…”

Section: Related Workmentioning

confidence: 99%

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

Ou,

You,

2024

Processes

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Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is proposed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle clusters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated: we predict the obstacle position corresponding to the TEB pose through the detected obstacle velocity and add this constraint to the corresponding TEB pose vertex. Then, a pose sequence considering the future positions of obstacles is obtained through a graph optimization algorithm. Compared with the original TEB, our method reduces the total running time by 22.87%, reduces the running distance by 19.23%, and increases the success rate by 21.05%. Simulations and experiments indicate that the improved TEB enables robots to efficiently avoid dynamic obstacles and reach the goal as quickly as possible.

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Section: Related Workmentioning

confidence: 99%

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

Ou,

You,

2024

Processes

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“…Subsequently, although many researchers have improved upon the classical speed obstacle method, most studies have tended to choose a speed that is at the boundary between the collision and non-collision zones, as it is considered the most efficient solution that will ensure that there is no collision with the obstacle in the ideal situation [9][10][11]. However, in this case, when approaching an obstacle, the probability of collision between the AGV and the obstacle may be greatly increased due to the interference of uncertainties in the environment, possible sudden changes in the speed of the obstacle, possible errors in sensor measurements, and possible wheel slippage, among other factors [12,13].…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Obstacle Avoidance Method for AGV Based on Improved Speed Barriers

et al. 2022

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The complexity and difficulty of dynamic obstacle avoidance for AGVs are increased by the uncertainty in a dynamic environment. The adaptive speed obstacle method allows the size of the collision cone to be dynamically changed to solve this problem, but this method may cause the AGV to turn too much when it is close to obstacles, as the collision cone expands too fast, which may lead to unstable operations or even collision. In order to address these problems, we propose an improved speed obstacle algorithm. The proposed algorithm uses Kalman filtering to estimate the positions of dynamic obstacles and adopts the idea of forward simulation to build a speed obstacle buffer according to the estimated positions of obstacles, such that the AGV can use the predicted positions of obstacles in the next moment, instead of the current positions, to build a speed obstacle model. Finally, an objective function that balances efficiency and safety was established to score all the candidate speeds, such that the highest-rated speed could be selected as the candidate speed for the next moment.

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“…This approach guides robots to make dynamic responses by enhancing both global path planning methods (e.g., A * [13], Dijkstra [14]) and local path planning methods (such as the velocity obstacle method [15] and the dynamic window method [16]). For instance, Goller et al [17] combined the A * algorithm with a reactive local planning algorithm in densely populated supermarket environments to plan safe paths for robots. Nevertheless, when conflicts arise between human presence and the global path, robots may only opt for a waiting approach, which can adversely affect the comfort of individuals in the environment.…”

Section: Introductionmentioning

confidence: 99%

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

Li,

Zhang,

2024

Robotica

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In order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a “first-come, first-served” expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments.

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Obstacle Avoidance With Dynamic Avoidance Risk Region for Mobile Robots in Dynamic Environments

Cited by 27 publications

References 21 publications

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

A Dynamic Obstacle Avoidance Method for AGV Based on Improved Speed Barriers

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

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