Mobile Robot Collision Avoidance in Human Environments

Abstract

Collision avoidance is a fundamental requirement for mobile robots. Avoiding moving obstacles (also termed dynamic obstacles) with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. In this paper, both active and critical regions are used to deal with the uncertainty of human motion. A procedure is introduced to calculate the region sizes based on worst‐case avoidance conditions. Next, a novel virtual force field‐based mobile robot navigation algorithm (termed QVFF) is presented. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions for avoiding moving obstacles and its stability is proven using a piecewise continuous Lyapunov function. Simulation and experimental results are provided for a human walking towards the robot and blocking the path to a goal location. Next, the proposed algorithm is compared with five state‐of‐the‐art navigation algorithms for an environment with one human walking with an unpredictable change in direction. Finally, avoidance results are presented for an environment containing three walking humans. The QVFF algorithm consistently generated collision‐free paths to the goal

Intersection Recognition and Guide-Path Selection for a Vision-Based AGV in a Bidirectional Flow Network

“…The local position and orientation can be estimated by using a neural extended Kalman filter in a monocular visual SLAM framework [8]. The projection region size of the obstacle can be estimated based on worstcase avoidance conditions and the virtual force field-based algorithm can be used for avoiding moving obstacles [36].…”

“…The local position and orientation can be estimated by using a neural extended Kalman filter in a monocular visual SLAM framework [8]. The projection region size of the obstacle can be estimated based on worstcase avoidance conditions and the virtual force field-based algorithm can be used for avoiding moving obstacles [36]. If the low-cost vision-based AGVs have the intelligence of local real-time SLAM and autonomous obstacle avoidance, this will be helpful in convincing industrial practitioners of the virtues of vision navigation, and to promote the further application of vision-based AGVs in more large-scale AGV systems that may require flexibility, simplicity, efficiency and low cost at the same time.…”

Acoustic Sensing From a Multi-Rotor Drone

“…The first configuration (Array-C) is more compact and the array is mounted close to the middle of the MAV body, centring the four motors. In the second configuration (Array-F), the array is mounted close to the front end of the MAV body, but it is still inside the critical collision protection area [30].…”

Research on Obstacle Avoidance Method for Mobile Robot Based on Multisensor Information Fusion

“…Obstacle avoidance ability is a basic need for all mobile robots, which helps them move without collision in unstructured environments. (1)(2)(3) During the past few years, obstacle avoidance technology for mobile robots has been investigated by many researchers. (4,5) For example, Yang et al designed a neural network enhanced telerobot control system and tested it on a Baxter robot.…”