Despite significant progress in robot hardware, the number of mobile robots deployed in public spaces remains low. One of the challenges hindering a wider deployment is that even if a robot can build a map of the environment, for instance through the use of LiDAR sensors, it also needs to calculate, in real time, a smooth trajectory that avoids both static and mobile obstacles. Considering this scenario, in this paper we investigate whether genetic algorithms can play a role in real-time obstacle avoidance. Historically, the typical use of genetic algorithms was in offline optimization. To investigate whether an online, real-time deployment is possible, we create a family of algorithms called GAVO that combines genetic algorithms with the velocity obstacle model. Through a series of experiments, we show that a carefully chosen chromosome representation and parametrization can achieve real-time performance on the obstacle avoidance problem.

The main purpose of our work was to develop and implement a motion planning algorithm that can ensure the avoidance of static and moving obstacles in the workspace of the robot. We used the Velocity Obstacles method, which is suitable for motion planning of mobile robots in a dynamic environment. With this method, we planned evasive maneuvers by using information about actual velocity and actual position of the robot and obstacles which information are supposed to be known or well estimated. To choose the velocity of the robot, in the most former publications, the authors always tried to find the velocity, that is the fastest solution to reach the target position. While following such a path, the robot usually goes very close to the obstacles which can result a collision-risk, if only inaccurate size, position and velocity information are available about the obstacles. In our work, next to the fastest target reaching method, we introduced a new method, that can ensure the safest motion planning. We named this method Safety Velocity Obstacles (SVO) method. Simulations were performed to test the motion of the robot determined by SVO.

The collision-free movement for a mobile robot in the presence of dynamic obstacles remains a significant challenge. In addition to self-localization, we also need to worry about the location of the moving obstacles, taking into account the noise in the sensors and the uncertainty in the movement of these obstacles. In this paper, we propose an approach for omnidirectional robot maneuvering in a 2D workspace that combines a Particle Filter for the estimation of the obstacles from LiDAR data and a variation of the Velocity Obstacles (VO) technique. The position and the velocity vector of the obstacles can be perceived by the Particles Filter and an uncertainty degree is also calculated. These outputs are combined with the VO algorithm to achieve motion planning that takes into account the current level of uncertainty as well as a cost function that expresses the risk tolerance of the user. We validate the approach in simulation and in experiments with a physical robot.

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations.

The collision-free motion planning for mobile robots is a challenging task in dynamic environment. There are situations when the robot has no chance to select a velocity vector that would cause no collision in the next steps of the motion. Most of the motion planning algorithms for mobile robots cannot generate an appropriate solution for that problem. At the autonomous systems, the only acceptable solution in this situation is emergency braking which could cause huge damage between the robot and the obstacle. The main focus of this paper is to introduce a novel motion planning method for mobile robots that could generate a velocity vector for the robot that would cause the least damage between the robot and the environment. It can be assumed that the obstacles in the environment are passive, and low damage collisions would be attempted only when avoidance is not possible at the sampling time. If the robot has an opportunity to select a velocity vector from the Reachable Avoidance Velocities set, then a cost-function-based algorithm can be used considering different aspects.