Page 39 - Read Online

Page 132 Ortiz et al. Intell Robot 2021;1(2):131-50 I http://dx.doi.org/10.20517/ir.2021.09

Path planning (PP) can be performed under the following conditions:

(1) The environment is known. PP is an optimization problem [11–13] .

(2) The environment is partially known. PP can find new objects during navigation [14,15] .

(3) The environment is totally unknown. PP depends on the navigation and has a recursive solution [16–18] .

Simultaneous localization and mapping (SLAM) can be used in unknown environments [19] or in partially
unknown environments [20] . SLAM [21] uses the current position to construct a map, and it can be classified
into feature-based [22] , pose-based [23] , appearance-based [24] , and variants [25] .

The most popular SLAM uses Kalman filter [21] for Gaussian noise. Nonlinear SLAM uses extended Kalman
filter (EKF) [26] , where the noise assumptions are not satisfied [27] . EKF-SLAM applies linearization [28] .

1.2. Related work
Few AN uses SLAM. Visual SLAM uses several cameras [29] . AN can use both SLAM and GPS signals [30] .
Robots can avoid moving obstacles using neural networks [31] . Swarm optimization helps robots follow an
object [32] . Neural networks help robots construct the navigation path [33] . The optimal path is considered in
the sense of trajectory length, execution time, or energy consumption.

Genetic algorithms (GA) have been developed recently [34,35] . They are easy to use for optimization in non-
deterministiccases [36] , uncertainty models [37] , androbustcases [38] . GAcan be in form ofant-based GA [39,40] ,
cell decomposition GA [41] , potential field GA [42] , ant colony [43] , and particle swarm optimization [44] . Finite
Markov chain is a theory tool for GA [45,46] .

1.3. Our work
In this paper, we try to design AN in an unknown environment in real time. The contributions are as follows:

(1) Sliding mode SLAM: The robustness of this SLAM is better than other SLAM models in bounded noise.

(2) GA SLAM: We use roadmap PP and GA to generate the local optimal map.

(3) Comparisons and simulations with other SLAM models were made by using a mobile robot [47] .

2. SLIDING MODE SLAM

SLAMgivesthe robotpositionandenvironment map at the sametime. At time , the state is x = ( , , ),

2
1
where ( , ) is the position and is the orientation of the robot. x = m ,m , ...,m are landmarks,

with m = ( , ) the th landmark. We assume the true location is time-invariant.

x has two parts: the robot x and the landmarks x . The state equation is

x f (x ,u ) + w
x +1 = +1 = = F(x ,u ) + [w , 0] (1)

x x
+1
where f () is the robot dynamics, w is the noise, and u is the robot control. Since x is not influenced by

motion noise, the noise is [w , 0] .