Researchers at the University of South Australia have written a computational algorithm that allows mobile robots to navigate obstacles, finding the quickest path to their destination.
Image Credit: University of South Australia. (2022). Algorithm helps robots avoid obstacles in their path. [online] Available at: https://www.unisa.edu.au/media-centre/Releases/2022/algorithm-helps-robots-avoid-obstacles-in-their-path/
In the Journal of Field Robotics, the Australian researchers describe how they programmed the best elements from existing algorithms into a TurtleBot to enable it to adjust its speed and steering angles.
There are two types of path planning strategies for mobile robots, depending on whether they are being used in fixed environments or where they are encountering moving obstacles, such as humans or machines.
Dr. Habib Habibullah, Engineering Lecturer, University of South Australia
A Brief Introduction to Mobile Robots
Mobile robots are machines controlled by artificial intelligence software (algorithms) that use tracks, wheels or “legs” to navigate their environment.
Mobile robots can operate in many types of environments. These include aerial robots such as drones, polar robots, unmanned ground vehicles, autonomous underwater vehicles and delivery robots. Mobile robots can either operate autonomously or under guidance.
Guided robots are guided by tracks or other sorts of guidance systems. They often require the supervision of an operator. Autonomous mobile robots (AMRs), on the other hand, navigate independently from external guidance.
To achieve this, AMRs are controlled by artificial intelligence software incorporating machine learning and specialized path planning algorithms. Additionally, cameras and sensors help them locate and avoid obstacles.
In 1979, the Stanford Cart was the first mobile robot to successfully navigate a room without human intervention. A computer algorithm was written to process images captured from an onboard camera, thus enabling the cart to navigate.
Ever since then, industries such as agriculture, logistics and healthcare have sought to use autonomous mobile robots to improve their processes. Navigation is only truly autonomous once a robot can navigate to its destination in a reasonable amount of time, without human intervention.
Therefore, these robots must be able to negotiate obstacles successfully. The difficulty, however, is compounded because obstacles may not always be stationary.
Path Planning Algorithms for Mobile Robots
Path planning is essential to enable autonomous mobile robots to negotiate obstacles successfully. Their optimal path must be collision-free from starting point to destination, taking into account distance traveled, time taken and cost of operation. Path planning can be conducted either in an unknown environment or a known environment where obstacles have already been mapped.
Researchers have written many algorithms that tackle path planning problems for mobile robots. These include so-called genetic algorithms which model evolutionary behavior, fuzzy logic algorithms and neural network algorithms. Others carry exotic names such as ant colony optimization, particle swarm optimization, artificial bee colony and shuffled frog leaping algorithm.
Some path planning strategies employ reactive navigation, where the path planning process occurs while the robot is moving (online). These strategies rely upon real-time feedback from onboard sensors.
The Artificial Potential Field (APF) method is such a method. It guides the robot to its destination using artificially induced attractive and repulsive forces. Vector field histogram (VFH) is another such method. It updates a two-dimensional cartesian histogram grid as the robot is moving.
Recognizing the strengths and weaknesses of existing methods, the Australian team incorporated the best from each into their own custom-written algorithm. They tested their method against two readily available algorithms: Artificial Potential Field (APF) just mentioned and Dynamic Window Approach (DWA). DWA, like APF, is an online path planning (collision avoidance) algorithm.
Dr. Habibullah and his team tested nine different scenarios in which they compared times to destination, collision rates and average speeds of the robot.
The Australian-built algorithm successfully navigated each scenario without any collisions. In contrast, the DWA algorithm resulted in 3 collisions, a 66% success rate. The APF model was collision-free, but the robot took longer to reach its destination.
Our proposed method sometimes took a longer path, but it was faster and safer, avoiding all collisions.
Dr. Habib Habibullah, Engineering Lecturer, University of South Australia
The Australian researchers believe their algorithm will be useful in many industrial scenarios, including warehouses, restaurants and farms where robots could be used for picking, packing and pelletizing.
References and Further Reading
Hossain, T. et al. (2021) Local path planning for autonomous mobile robots by integrating modified dynamic‐window approach and improved follow the gap method. Journal of field robotics, [online]. Available at: https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22055
Patle, B. K. et al. (2019). A review: On path planning strategies for navigation of mobile robot. Defence technology, [online] 15(4), pp. 582–606. Available at: https://www.researchgate.net/publication/332717157_A_review_On_path_planning_strategies_for_navigation_of_mobile_robot
University of South Australia. (2022). Algorithm helps robots avoid obstacles in their path. [online] Available at: https://www.unisa.edu.au/media-centre/Releases/2022/algorithm-helps-robots-avoid-obstacles-in-their-path/
Stanley Innovation. 12 of the most innovative and groundbreaking mobile robots. [online] Available at: https://stanleyinnovation.com/12-coolest-mobile-robots/
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