Agoraphilic Navigation Algorithm in Dynamic Environment with and without Prediction of Moving Objects Location

Abstract

This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benchedmarked with ANADE (Without predication) under similar environment conditions. Form the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations.

Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations

“…The Agoraphilic navigation algorithm has been developed to overcome many challenges in mobile robot navigation. The work presented in this paper is a continuation of authors previous work involved with Agoraphilic algorithm [1]- [3].…”

“…1. However, if d k is greater than the pre-determined d max value, an initial sector force for the corresponding sector is taken as u k [1].…”

“…The force shaping module uses a weighing system to modify the FSFs. In this process forces pointing towards the goal get higher weight while FSFs pointing away from the goal gets lesser weights [1].…”

“…This module helps the algorithm to identify future growing free-space. The necessity of prediction module was identified when multiple moving objects interrupt the robot's path at the same time[1].…”

Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

“…The main reason for using KF is its simplicity and accuracy in tracking slow manoeuvring objects compared with other methodologies [36,37]. The estimated positions of moving obstacles are combined with positions of static obstacles to generate the CGM [38].…”