Enhancing Robots Navigation in Internet of Things Indoor Systems

Abstract

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.

Escaping Local Minima Via Appraisal Driven Responses

“…The second series of simulations were performed in order to compare the proposed approach with more problem-specific approaches from [8]. To do so three of the test environments from [8] were recreated in the Pseudo-SLAM simulator as illustrated in Figure 6.…”

“…The second series of simulations were performed in order to compare the proposed approach with more problem-specific approaches from [8]. To do so three of the test environments from [8] were recreated in the Pseudo-SLAM simulator as illustrated in Figure 6. These three environments are designed specifically with the purpose of causing local minima, and as such are perfect for testing the proposed approach.…”

“…Since the approach proposed in this paper is based on probabilistic methods, some degree of variations in results should be expected. Therefore, 100 simulations were performed for each of these environments with the same initial conditions and goal state as in [8]. Since a goal state where specified for these simulations, the full capabilities of the affective response mechanism described in Section 6.3 were effectively in use.…”

“…Since a goal state where specified for these simulations, the full capabilities of the affective response mechanism described in Section 6.3 were effectively in use. Table 4 summarizes the results from these 1 Results from Table 2 in [8].…”

“…In the literature related to robot navigation, similar phenomena are commonly known as "the local minima issue" [8], "deadlocks" [9], "limit cycles" [10], "infinite loops" [11], "dead ends", "cyclic dead ends", or "trap-situations" [12]. Like the problem mentioned above, all of these terms refer to situations in which a fixed strategy for action selection results in no meaningful progress towards a goal state or compared to a measure of optimality.…”