Robust local navigation is a critical capability for any mobile robot operating in a real-world, unstructured environment, especially when there are humans or other moving obstacles in the workspace. One of the most commonly used methods for local navigation is the Dynamic Window Approach (DWA), which does not address the problem of dynamic obstacles and depends heavily on the settings of the parameters in its cost function. Thus, it is a static approach that does not adapt to the characteristics of the environment, which can change significantly. On the other hand, data-driven deep learning approaches attempt to adapt to the characteristics of the environment by predicting the appropriate robot motion based on the current observation. However, they cannot guarantee collision-free trajectories for unseen inputs. In this work, we combine the best of both worlds. We propose a neural network to predict the weights of the DWA, which is then used for safe local navigation. To address the problem of dynamic obstacles the proposed method considers a short sequence of observations to allow the network to model the motion of the obstacles and adjust the DWA weights accordingly. The network is trained using the Proximal Policy Optimization (PPO) in a reinforcement learning setting in a simulated dynamic environment. We perform a comprehensive evaluation of the proposed approach in realistic scenarios using range scans of real 3D spaces and show that it outperforms both DWA and purely Deep Learning approaches.

Local navigation is an essential ability of any mobile robot working in a real-world environment. One of the most commonly used methods for local navigation is the Dynamic Window Approach (DWA), which heavily depends on the settings of the parameters in its cost function. Since the optimal choice of the parameters depends on the environment that may significantly vary and change at any time, the parameters should be chosen dynamically in a data-driven way. To cope with this problem, we propose a novel deep convolutional neural network, which dynamically predicts these parameters considering the sensor readings. The network is trained using a state-of-the art reinforcement learning algorithm. In this way, we combine the power of data-driven learning and the dynamic model of the robot, enabling adaptation to the current environment as well as guaranteeing collision-free movement and smooth trajectories of the mobile robot. The experimental results show that the proposed method outperforms the DWA method as well as its recent extension.

There is an increasing need for everyday communications to be both secure and resistant to external perturbations. We have, therefore, created an experimental implementation of the coupling-function-based secure communication protocol in order to assess its robustness to channel noise. The transmitter and receiver are implemented on single-board computers, thereby facilitating communication of the analog electronic signals. The information signals are encrypted at the transmitter as the time-variability of nonlinear coupling functions between dynamical systems. This results in a complicated nonlinear mixing and scrambling of the information. To replicate the channel noise, analog white noise is added to the encrypted signals. After digitization at the receiver, the decryption is performed with dynamical Bayesian inference to take account of time-varying dynamics in the presence of noise. The dynamical Bayesian approach effectively separates the deterministic information signals from the perturbations of dynamical channel noise. The experimental realization has demonstrated the feasibility, and established the performance, of the protocol for secure, reliable, communication even with high levels of channel noise.