Abstract
Location-aware services are one of the key elements of modern intelligent applications. Numerous real-world applications such as factory automation, indoor delivery, and even search and rescue scenarios require autonomous robots to have the ability to navigate in an unknown environment and reach mobile targets with minimal or no prior infrastructure deployment. This research investigates and proposes a novel approach of dynamic target localisation using a single RF emitter, which will be used as the basis of allowing autonomous robots to navigate towards and reach a target. Through the use of multiple directional antennae, Received Signal Strength (RSS) is compared to determine the most probable direction of the targeted emitter, which is combined with the distance estimates to improve the localisation performance. The accuracy of the position estimate is further improved using a particle filter to mitigate the fluctuating nature of real-time RSS data. Based on the direction information, a motion control algorithm is proposed, using Simultaneous Localisation and Mapping (SLAM) and A* path planning to enable navigation through unknown complex environments. A number of navigation scenarios were developed in the context of factory automation applications to demonstrate and evaluate the functionality and performance of the proposed system.
Highlights
IntroductionAs the prevalence of autonomous vehicles and robots (collectively referred to as robots in this paper) increases, the challenge of navigation in new and unknown environments (both indoor and outdoor) is a critical barrier to widespread adoption of these systems [1,2]
As the prevalence of autonomous vehicles and robots increases, the challenge of navigation in new and unknown environments is a critical barrier to widespread adoption of these systems [1,2]
The the derived derived distance distance is is generally generally very very inaccurate, inaccurate, as as itit is is emitter strongly influenced by noise and multipath effects, so we primarily focus on using the direction for strongly influenced by noise and multipath effects, so we primarily focus on using the direction for motionplanning planningpurposes
Summary
As the prevalence of autonomous vehicles and robots (collectively referred to as robots in this paper) increases, the challenge of navigation in new and unknown environments (both indoor and outdoor) is a critical barrier to widespread adoption of these systems [1,2]. Simultaneous Localisation and Mapping (SLAM) techniques enable robots to achieve both of these tasks at the same time while operating within the environment [3]. Given a distant objective or goal location, using SLAM enables path planning and overall navigation of the system by dynamically improving the quality of the path over time to account for obstacles and other constraints [4,5]. The majority of existing indoor navigation systems [6,7] rely on vision-based approaches, which demand high computational power, costly equipment, and a stable environment without significant environmental changes over time. In cases where these obstacles cannot be visually
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