The snake-like robot without wheels is a bio-inspired robot whose high degree of freedom results in a challenge in autonomous locomotion control. The use of a Spiking Neural Network (SNN) which is a biologically plausible artificial neural network can help to achieve the autonomous locomotion behavior of snake robots in an energy-efficient manner. Approaches that use an SNN without hidden layers have been applied in the single-target tracking task. However, due to the complexity of the 3D gaits on a wheel-less snake robot and the imprecision of the pose control while in motion, they have some fluctuation that adversely affects their performances. In this work, we design two multi-layered SNNs with different topology for a wheel-less snake robot to track a certain moving object. The visual signals obtained from a Dynamic Vision Sensor (DVS) are fed into the SNN to drive the locomotion controller. Furthermore, the Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) learning rule is utilized to train the SNN end-to-end. Compared to the SNN without hidden layers, the proposed multi-layered SNN with a separated hidden layer shows its advantage in terms of robustness.

This paper introduces an end-to-end learning approach based on Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) for a multi-layered spiking neural network (SNN). As a case study, a snake-like robot is used as an agent to perform target tracking tasks on the basis of our proposed approach. Since the key of R-STDP is to use rewards to modulate synapse strengthens, we first propose a general way to propagate the reward back through a multi-layered SNN. Upon the proposed approach, we build up an SNN controller that drives a snake-like robot for performing target tracking tasks. We demonstrate the practicability and advantage of our approach in terms of lateral tracking accuracy by comparing it to other state-of-the-art learning algorithms for SNNs based on R-STDP.

Pedestrian detection has attracted enormous research attention in the field of Intelligent Transportation System (ITS) due to that pedestrians are the most vulnerable traffic participants. So far, almost all pedestrian detection solutions are based on the conventional frame-based camera. However, they cannot perform very well in scenarios with bad light condition and high-speed motion. In this work, a Dynamic and Active Pixel Sensor (DAVIS), whose two channels concurrently output conventional gray-scale frames and asynchronous low-latency temporal contrast events of light intensity, was first used to detect pedestrians in a traffic monitoring scenario. Data from two camera channels were fed into Convolutional Neural Networks (CNNs) including three YOLOv3 models and three YOLO-tiny models to gather bounding boxes of pedestrians with respective confidence map. Furthermore, a confidence map fusion method combining the CNN-based detection results from both DAVIS channels was proposed to obtain higher accuracy. The experiments were conducted on a custom dataset collected on TUM campus. Benefiting from the high speed, low latency and wide dynamic range of the event channel, our method achieved higher frame rate and lower latency than those only using a conventional camera. Additionally, it reached higher average precision by using the fusion approach.

Similar to their counterparts in nature, the flexible bodies of snake-like robots enhance their movement capability and adaptability in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degrees of freedom, where traditional model-based methods usually fail to propel the robots energy-efficiently. In this work, we present a novel approach for designing an energy-efficient slithering gait for a snake-like robot using a model-free reinforcement learning (RL) algorithm. Specifically, we present an RL-based controller for generating locomotion gaits at a wide range of velocities, which is trained using the proximal policy optimization (PPO) algorithm. Meanwhile, a traditional parameterized gait controller is presented and the parameter sets are optimized using the grid search and Bayesian optimization algorithms for the purposes of reasonable comparisons. Based on the analysis of the simulation results, we demonstrate that this RL-based controller exhibits very natural and adaptive movements, which are also substantially more energy-efficient than the gaits generated by the parameterized controller. Videos are shown at https://videoviewsite.wixsite.com/rlsnake .

In this paper, a biologically inspired 3D slithering gait for a snake-like robot is designed and implemented for the purpose of target tracking. First, by balancing the forward speed and the stability of the robot, a straight slithering gait is modelled, under which the robot can march straight, fast, and stably. Then, for the purpose of steering, the straight slithering gait is modified into a biased slithering gait. The relationship between turning radius and gait parameters is analyzed by the resistive force theory. With the head composition algorithm, we investigate the orientation problem of the snake robot's head module to obtain stable visual information during the locomotion process. Finally, with the guidance of the vision sensor mounted in the head module, target tracking simulations and prototype experiments are conducted to demonstrate the practicality and effectiveness of the slithering gait in autonomous locomotion scenarios.