Tuesday, December 12, 2023

UniSA-designed algorithm helps robots avoid obstacles in their path

Mobile robots need to automatically generate a safe, goal-oriented, and fast collision-free trajectory in real-time during the movement in an indoor/outdoor environment. The ultimate goal of the robot is to reach the destination without hitting any obstacles; therefore, a reactive local path planning algorithm is needed.

There are several algorithms on the market trying to address the issues of robots colliding with moving objects, but none are foolproof. Now, the University of South Australia (UniSA) researchers have developed an algorithm to help robots avoid running into humans and other moving obstacles in their path. The UniSA team has built a computer model that ensures mobile robots can recognize and avoid unexpected obstacles, finding the quickest and safest path to their destination.

The UniSA researchers tested their model against two common online collision avoidance algorithms – Dynamic Window Approach (DWA) and Artificial Potential Field (APF) – to generate a collision-free trajectory for a mobile robot capable of avoiding any moving obstacles presenting in the surrounding environment. In a series of simulations in nine different scenarios, they compared collision rates, the average time to destination, and the average speed of the robot.

In every scenario, the novel algorithm helped robots successfully navigate a path without any collisions. In comparison, the DWA model was only 66% effective, colliding with objects in three of the nine simulations. The APF model was also collision-free but took more time to reach its destination. Researchers say their method sometimes took a longer path, but it was faster and safer, avoiding all collisions.

The UniSA-designed algorithm could be applied in many environments, including industrial warehouses where robots are commonly used, for robotic fruit picking, packing, and pelletizing, and also for restaurant robots that deliver food from the kitchen to the table.

“This could also be a potential solution for agricultural robots, for example, autonomous lawnmowers, ground robots for crop surveillance and autonomous weeding robots, where children, pets, and other animals are often present,” UniSA mechatronics engineering lecturer Dr. Habib Habibullah says.