Path planning for mobile robot using self-adaptive learning particle swarm optimization

Abstract

In this paper, I first review the seminal work by Thomas Kuhn-The Structure of Scientific Revolutions-and elaborate my view on paradigm shifts in software engineering research and practice as it turns 50 years old in 2018. I then examine major undertakings of the computing profession since early days of modern computing, especially those done by the software engineering community as a whole. I also enumerate anomalies and crises that occurred at various stages, and the attempts to provide solutions by the software engineering professionals in the past five decades. After providing such a background, I direct readers' attention toward emerging anomalies in software engineering, at a severity level that is causing another software engineering crisis, and suggest a set of criteria for feasible solutions. The main theme of this paper is to advocate that situation analytics, equipped with necessary definitions of essential concepts including situation and intention as parts of a new computational framework, can serve as the foundation for a new software engineering paradigm named the Situation-Centric Paradigm. In this framework, situation is considered a new abstraction for computing and is clearly differentiated from the widely accepted existing abstractions, namely function and object. I argue that the software engineering professionals will inevitably move into this new paradigm, willingly or unwillingly, to empower Human-Embedded Computing (HEC) and End-User Embedded Computing (EUEC), much more than what they have done with traditional humancentered or user-centric computing altogether. In the end, I speculate that an ultimate agile method may be on the rise, and challenge readers to contemplate "what if" hundreds of thousands "end-user developers" emerge into the scene where the boundaries between end users and developers become much more blurred.

Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

“…where t is the positional parameter, |P(t)| min stands for the length of the Bezier curve path, S ⟶ T represents the path from start point S to end point T, and collision − free is the path without collision. Equations (1) and (2) show that the Bezier curve is determined by the control points. erefore, the problem is equal to finding the control points of the Bezier curve with constraint (9).…”

“…Path planning is an important research direction in the field of mobile robots, and it is one of the main difficulties in research on such robots [1]. e path planning problem aims to find the safest and shortest path autonomously without collisions from the start point to the target point under a given environment with barriers [2,3]. Path planning has been widely used in fields such as logistics distribution, intelligent transportation, and weapons navigation [4][5][6].…”

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

“…If the user-defined number of control points of the UAV path is D, then the path planner should determine D1 = 3 * D coordinate parameters, namely X 1 , X 2 , · · · , X D1 , where (X i , X D+i , X 2D+i ), i = 1, 2, · · · , D denote the three dimensional coordinates in solution space. The point (x i , x D+i , x 2D+i ) in solution space can be transformed into the point (x(i), y(i), z(i)) in the real flying space by the following transform equation [41]:   …”

Multi-Robot Path Planning Method Using Reinforcement Learning

“…When learning data is not provided, some actions are taken to compensate the system for learning. Reinforcement learning, which includes actor critic [12][13][14] structure and q learning [15][16][17][18][19][20][21], has many applications such as scheduling, chess, and robot control based on image processing, path planning [22][23][24][25][26][27][28][29] and etc. Most of the existing studies using reinforcement learning exclusively the performance in simulation or games.…”