Abstract
Over the course of the past decade, we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, 1) it guarantees obstacle avoidance for both convex and non-convex obstacles, 2) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, 3) no prior knowledge of the environment, such as the position and geometry of the obstacles, is needed, 4) it only requires temporally and spatially local environmental sensor information, and 5) it can be implemented on a wide range of robotic platforms in both 2D and 3D environments. The proposed navigation algorithm is validated in simulation scenarios as well as through experimentation. The results demonstrate that robotic platforms, ranging from planar point-like robots to robot arm structures such as the Baxter robot, can successfully navigate toward desired targets within an obstacle-laden environment.
Highlights
Over the past decade, robotic technologies have found their way into an ever-growing number of fields
This comparative study includes our magnetic-field-inspired (MFI) navigation system alongside several other navigation methods presented in the literature - the standard Artificial Potential Field (APF) (Khatib, 1985), the circular field (CF) (Haddadin et al, 2011), and the gyroscopic force (GF) (Sabattini et al, 2013)
These methods were chosen as they share the same properties as the proposed algorithm, i.e. they are all reactive navigation methods, able to operate without the need for prior environmental mapping and are capable of operating in 3D environments populated with arbitrarily shaped obstacles
Summary
Robotic technologies have found their way into an ever-growing number of fields. Solutions focused on producing geometrical paths for the robot to take to reach its target While theoretically elegant, this type of planning was found to be rather impractical due to the costly numerical computation that was needed to create the configuration space (C-Space) (LaValle, 2011). This type of planning was found to be rather impractical due to the costly numerical computation that was needed to create the configuration space (C-Space) (LaValle, 2011) This prompted a new Magnetic-Field-Inspired Navigation in Complex Environments approach, known as sampling-based planning which randomly samples and checks whether a configuration lies in free space or not (Choset, 2005). Other algorithms produce initial paths for static environments that are based on a known map and modify those paths online (Brock and Khatib, 2002; Vannoy and Xiao, 2008) Another approach that employs potential function with a single global minimum has been proposed. Significantly, most of these algorithms rely on perfect knowledge of the environment prior to the onset of any motion on the part of the robotic device
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