Abstract: Local obstacle avoidance is critical for mobile robot
navigation. It is a challenging task to ensure path optimality and
safety in cluttered environments. We proposed an Environment
Aware Dynamic Window Approach in this paper to cope with
the issue. The method integrates environment characterization into
Dynamic Window Approach (DWA). Two strategies are proposed
in order to achieve the integration. The local goal strategy guides
the robot to move through openings before approaching the final
goal, which solves the local minima problem in DWA. The adaptive
control strategy endows the robot to adjust its state according
to the environment, which addresses path safety compared with
DWA. Besides, the evaluation shows that the path generated from
the proposed algorithm is safer and smoother compared with
state-of-the-art algorithms.
Abstract: Back propagation algorithm (BP) is a widely used
technique in artificial neural network and has been used as a tool
for solving the time series problems, such as decreasing training
time, maximizing the ability to fall into local minima, and optimizing
sensitivity of the initial weights and bias. This paper proposes an
improvement of a BP technique which is called IM-COH algorithm
(IM-COH). By combining IM-COH algorithm with cuckoo search
algorithm (CS), the result is cuckoo search improved control output
hidden layer algorithm (CS-IM-COH). This new algorithm has a
better ability in optimizing sensitivity of the initial weights and bias
than the original BP algorithm. In this research, the algorithm of
CS-IM-COH is compared with the original BP, the IM-COH, and the
original BP with CS (CS-BP). Furthermore, the selected benchmarks,
four time series samples, are shown in this research for illustration.
The research shows that the CS-IM-COH algorithm give the best
forecasting results compared with the selected samples.