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Page 334 Sellers et al. Intell Robot 2022;2(4):333­54 I http://dx.doi.org/10.20517/ir.2022.21

Keywords: Adjacent node selection (ANS) algorithm, safety-aware roads, path planning, multiple-waypoint opti-
mization, navigation and mapping

1. INTRODUCTION
[1]
Robotics system has been applied to numerous fields, such as transportation , healthcare service [2,3] , agri-
[5]
[4]
culture , manufacturing , etc., in recent years. Robot navigation is one of the fundamental components in
robotic systems, which includes multi-waypoint navigation system [6–8] . As an increasing demand and limited
onboard resources for autonomous robots, it requires the ability to visit several targets in one mission to op-
timize multiple objectives, including time, robot travel distance minimization, and spatial optimization [9–15] .
For example, due to a global pandemic, the world struggled to sanitize heavily populated areas, such as air-
ports, hospitals, and educational buildings. Autonomous robots with multi-waypoint navigation systems can
effectively sanitize all targeted areas without endangering the workers [14,16] . As well as in agriculture man-
agement, multi-waypoint strategies allow the robotic system to navigate and survey multiple areas to assist in
production and collection.

In order to employ robotic systems in real-world scenarios, one critical factor is to develop autonomous
robot multi-waypoint navigation and mapping system [17] . In order to solve the autonomous robot naviga-
tion problem, countless algorithms have been developed, such as graph-based [18,19] , ant colony optimization
(ACO) [20–22] , bat-pigeon algorithm (BPA) [23] , neural networks [24–26] , fuzzy logic [27] , artificial potential field
(APF) [28] , sampling-based strategy [14,29] , hybrid algorithms [30] , task planning algorithm [31] , etc. Chen et al.
produced a hybrid graph-based reinforcement learning architecture to develop a method for robot navigation
in crowds [18] . Luo et al. proposed an improved vehicle navigation method, which utilizes a heading-enabled
ACO algorithm to improve trajectory towards the target [20] . Lei et al. developed a Bat-Pigeon algorithm
with the ability to adjust the speed navigation of autonomous vehicles [23] . Luo et al. developed the model
for multiple robots complete coverage navigation while using a bio-inspired neural network to dynamically
avoid obstacles [32] . Na and Oh established a hybrid control system for autonomous mobile robot navigation
that utilizes a neural network for environment classification and behavior-based control method to mimic the
human steering commands [25] . Lazreg and Benamrane [27] developed a neuro-fuzzy inference system associ-
ated with a Particle Swarm Optimization (PSO) method for robot path planning using a variety of sensors to
control the speed and position of a robot. Jensen-Nau et al. [28] integrated a Voronoi-based path generation
algorithm and an artificial potential field path planning method, in which the latter is capable of establishing
a path in an unknown environment in real-time for robot path planning and obstacle avoidance. Penicka and
Scaramuzza [14] developed a sampling-based multi-waypoint minimum-time path planning model that allows
obstacle avoidance in cluttered environments. Ortiz and Yu [30] proposed a sliding control method in combi-
nation with simultaneous localization and mapping (SLAM) method to overcome the bounded uncertainties
problem, which utilizes a genetic algorithm to improve path planning capabilities. Bernardo et al. proposed a
task planning method for home environment ontology to translate tasks given by other robots or humans into
feasible tasks for another robot agent [31] .

In robotic path planning, one of the special topics is autonomous robot multi-waypoint navigation which has
been studiedfor manyyears. Forinstance, Shairetal. proposed a modelforreal-world waypointnavigationus-
ing a variety of sensors for accurate environmental analysis [33] . The system is designed utilizing the wide area
augmentation system (WAAS) and the European geostationary navigation overlay service (EGNOS) for GPS
in combination with aerial images to provide valuable positioning data to the system. Yang [34] brought about
a multi-waypoint navigation system based on terrestrial signals of opportunity (SOPs) transmitters, which has
the ability to operate in environments that are not available to global navigation satellite systems (GNSS) for
unmanned aerial vehicles (UAV). Janoš et al. proposed a sampling-based multi-waypoint path planner, space-