An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning
Table 2
Summary of strengths and challenges of conventional based mobile robot path planning and obstacle avoidance methods.
Approach
Major Implementation
Strengths
Challenges
Artificial Potential Field
Simulation and real Experiment
(i) Easy to implement by considering attraction to goal and repulsion to obstacles
(i) Results in local minima problem causing the robot to be trapped at a position instead of the goal [36, 40, 41, 105, 106] (ii) Inability of the robot to pass between closely spaced obstacles and results in oscillations [107, 108, 110] (iii) Difficulty to perform in environment with obstacles of different shapes [109] (iv) Constraints of the hardware of the mobile robots affect the performance of APF methods [51]
Visual Based and Reactive approaches
Simulation and real Experiment
(i) Easy to implement by relying on the information from sensors and cameras to take decision on the movement of the robot
(i) Noise from sensors and cameras due to distance from objects, color, temperature and reflection affects performance (ii) Hardware constraints of the robots affect performance (iii) Computational expensive
Robot Motion and Sliding Mode Strategy
Simulation
(i) Is Fast with respect to response time (ii) Works well with uncertain systems and other unconducive external factors [64]
(i) Performs poorly when the longitudinal velocity of the robot is fast (ii) Computational expensive (iii) Chattering problem leading to low control accuracy [114]
Dynamic Window
Simulation
(i) Easy to implement
(i) Local minima problem (ii) Difficult to manage the effects of mobile robot constraints [75, 102–104]
