Closest Gap based (CG) reactive obstacle avoidance Navigation for highly cluttered environments

Abstract

A new reactive collision avoidance approach for mobile robots moving in cluttered and complex environments was developed and implemented. The novelty of this approach lies in the creation of a new method for analyzing openings in front of the robot that highly reduces their number when compared with the Nearness-Diagram Navigation (ND) technique, particularly in complex scenarios. Moreover, the angular width of the chosen (selected) gap with respect to the robot vision is taken into consideration. Consequently, oscillations are alleviated, the computational complexity is reduced and a smoother behavior will be achieved. Our technique adjusts the motion law proposed in the Smooth Nearness-Diagram Navigation (SND) method to generate safer paths for the robot by considering the ratio of threats on its sides and applying stricter deviation against an obstacle as it gets closer to the robot. Hence, the problem of deadlock occurring in narrow corridors, with high threats on one side and low threats on the other, is solved without affecting the smoothness behavior. Simulation and experimental results demonstrate the power of the proposed approach.

Safe Gap based (SG) reactive navigation for mobile robots

“…A gap is a potentially free path wide enough for the robot to move through [1]. The side of gap G with the smaller angle is called the right side and the other is the left side.…”

“…The subgoal is then located in-between the two side points creating the safe gap. For determining the closest gap, we follow the same solution proposed in the CG method and the reader is directed to [1]. Next, we present an algorithm to identify the appropriate location of the safe gap based on analyzing the structure of the obstacles between the current robot location and the closest gap.…”

“…In this regard, the robot trajectory is based on all nearby obstacles, not just the closest two, and this leads to smoother paths. The Closest Gap (CG) approach proposed in our earlier work [1] has developed a new method for fetching gaps surrounding the robot which reduces oscillations and computational complexity. Moreover, the motion laws proposed in the SND are improved to generate safer paths, particularly when the difference in the number of obstacles on the two sides of the robot is high.…”

A new tentacles-based technique for avoiding obstacles during visual navigation

“…This method, however, suffered from its simplicity, which led to very conservative collision avoidance, and strongly varying control inputs. Alternative reactive strategies include: the vector field histogram [11], dynamic window [12], obstacle-restriction method [13], and closest gap [14]. The first two methods rely on a candidate set of commands; however, trap situations and oscillations may arise.…”

“…In [13], these issues are solved, but, since the robot is assumed holonomic and a 3D subgoal is required, the approach is not suitable for our problem. Similarly, [14] uses the robot pose, which is noisy in our framework. Instead, we take inspiration from [15] and [16], where a set of trajectories (arcs of circles) is evaluated for navigating.…”