A comparative study on the path length performance of maze-searching and robot motion planning algorithms

1991 ◽  
Vol 7 (1) ◽  
pp. 57-66 ◽  
Author(s):  
V.J. Lumelsky
Keyword(s):  
Comparative Study ◽  
Motion Planning ◽  
Path Length ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Planning Algorithms

Conclusion

2012 ◽  
pp. 282-288
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Hybrid Algorithms ◽  
General Algorithm ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Planning Algorithm ◽  
Planning Algorithms

Having covered a large number of concepts, algorithms, issues, and challenges in robot motion planning, there is little left to state as the conclusion. In this chapter, the authors simply jot down some last words, final thoughts, and closing remarks. While a large number of algorithms exist for the same problem, it is hard to pick out one algorithm and use it upright for a given modeling scenario. It becomes important to understand what features the algorithm has, what is the level of each of these features, and whether these features suit the problem in hand. Optimality and completeness are important factors provided by the algorithm in real time, which may, however, not always be possible. Hybrid algorithms are more tailored in the sense that these algorithms can be customized as per needs. Again, the choice of algorithm plays an important role, as the limitations get added up along with the advantages which we need by design. Hence, it is always important to know what is desired from the robot and the planning algorithm and what scenarios it may face. Most practical scenarios may be simple, such that these can be simply solved by any general algorithm. Other aspects of robotics may be more challenging than a planning algorithm. However, it is certain the ever-rising use of robotics shall bring forth challenging scenarios for robots to work in, in which case the planning algorithms would be tested to best of their capability.

scholarly journals Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

2021 ◽  
Vol 6 (2) ◽  
pp. 2256-2263
Author(s):  
Hai Zhu ◽  
Francisco Martinez Claramunt ◽  
Bruno Brito ◽  
Javier Alonso-Mora
Keyword(s):  
Motion Planning ◽  
Dynamic Environments ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Multi Robot
Sign in / Sign up

Export Citation Format

Share Document