Biological evidence shows that animals are capable of evading eminent collision without using depth information, relying solely on looming stimuli. In robotics, collision avoidance among uncooperative vehicles requires measurement of relative distance to the obstacle. Small, low-cost mobile robots and UAVs might be unable to carry distance measuring sensors, like LIDARS and depth cameras. We propose a control framework suitable for a unicycle-like vehicle moving in a 2D plane that achieves collision avoidance. The control strategy is inspired by the reaction of invertebrates to approaching obstacles, relying exclusively on line-of-sight (LOS) angle, LOS angle rate, and time-to-collision as feedback. Those quantities can readily be estimated from a monocular camera vision system onboard a mobile robot. The proposed avoidance law commands the heading angle to circumvent a moving obstacle with unknown position, while the velocity controller is left as a degree of freedom to accomplish other mission objectives. Theoretical guarantees are provided to show that minimum separation between the vehicle and the obstacle is attained regardless of the exogenous tracking controller.
University of Turku（图尔库大学）；Royal Institute of Technology (KTH)（瑞典皇家理工学院（KTH））；King Faisal University（费萨尔国王大学）
Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extrac…
Inspired by the navigational behavior observed in the animal kingdom and especially the navigational behavior of the ants, we attempt to simulate it in an artificial environment by implementing different kinds of biomimetic algorithms.
Technical University of Munich（慕尼黑工业大学）；Sun Yat-sen University（中山大学）
Localization in the environment is an essential navigational capability for animals and mobile robots. In the indoor environment, the global localization problem remains challenging to be perfectly solved with probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, it is intriguing and promising to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based re-localization approach. The experiment …
In this work, we propose a novel, bio-inspired multi-sensory SLAM approach called ViTa-SLAM. Compared to other multisensory SLAM variants, this approach allows for a seamless multi-sensory information fusion whilst naturally interacting with the environment. The algorithm is empirically evaluated in a simulated setting using a biomimetic robot platform called the WhiskEye. Our results show promising performance enhancements over existing bio-inspired SLAM approaches in terms of loop-closure detection.
This paper presents an artificial evolutionbased method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved following a biologically inspired scheme and give a fast, low-cost solution to the obstacle detection problem in mobile robotics.
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimensionality of systems of equations. In supervised learning, such as in image classification tasks, rotation, translation and scale invariances are used to augment training datasets. In this work, we use data augmentation in a similar way, exploiting symmetry in the quadruped domain of the DeepMind control suite (Tassa et al. 2018) to add to the trajectories experienced by the actor in the actor-critic algorithm of Abdolma…
FZI Research Center for Information Technology（FZI信息技术研究中心）
We present a biologically inspired approach for path planning with dynamic obstacle avoidance. Path planning is performed in a condensed configuration space of a robot generated by self-organizing neural networks (SONN). The robot itself and static as well as dynamic obstacles are mapped from the Cartesian task space into the configuration space by precomputed kinematics. The condensed space represents a cognitive map of the environment, which is inspired by place cells and the concept of cognitive maps in mammalian brains. The generation of training data as well as the evaluation were perform…
Ruhr West University of Applied Sciences（鲁尔西区应用科学大学）
The goal of this work is the development of a motion model for sequentially timed movement actions in robotic systems under specific consideration of temporal stabilization, that is maintaining an approximately constant overall movement time (isochronous behavior). This is demonstrated both in simulation and on a physical robotic system for the task of intercepting a moving target in three-dimensional space. Motivated from humanoid motion, timing plays a vital role to generate a naturalistic behavior in interaction with the dynamic environment as well as adaptively planning and executing actio…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；China Agricultural University（中国农业大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
In recent years, the interdisciplinary research between information science and neuroscience has been a hotspot. In this paper, based on recent biological findings, we proposed a new model to mimic visual information processing, motor planning and control in central and peripheral nervous systems of human. Main steps of the model are as follows: 1) Simulating "where" pathway in human: the Selective Search method is applied to simulate the function of human dorsal visual pathway to localize object candidates; 2) Simulating "what" pathway in human: a Convolutional Deep Belief Network is applied …
Edinburgh Centre for Robotics（爱丁堡机器人中心）；University of Edinburgh（爱丁堡大学）；Heriot-Watt University（赫瑞-瓦特大学）
Unforeseen events are frequent in the real-world environments where robots are expected to assist, raising the need for fast replanning of the policy in execution to guarantee the system and environment safety. Inspired by human behavioural studies of obstacle avoidance and route selection, this paper presents a hierarchical framework which generates reactive yet bounded obstacle avoidance behaviours through a multi-layered analysis. The framework leverages the strengths of learning techniques and the versatility of dynamic movement primitives to efficiently unify perception, decision, and act…
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Stinger Ghaffarian Technologies（斯廷格·加法里安技术公司）；NASA Ames Dynamic Tensegrity Robotics Lab（美国国家航空航天局艾姆斯动态张拉整体机器人实验室）
In this paper, we present a light-weight, multi- axis compliant tenegrity joint that is biologically inspired by the human elbow. This tensegrity elbow actuates by shortening and lengthening cable in a method inspired by muscular actuation in a person. Unlike many series elastic actuators, this joint is structurally compliant not just along each axis of rotation, but along other axes as well. Compliant robotic joints are indispensable in unpredictable environments, including ones where the robot must interface with a person. The joint also addresses the need for functional redundancy and flexi…
Technical University of Munich（慕尼黑工业大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用科学大学）
Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D environments where the robot has to solely rely on a single monocular camera. In particular, we are interested in solving this problem without relying on localization, mapping, or planning techniques. Most of the existing work consider obstacle avoidance as two separate problems, namely obstacle detection, and control. Inspired by the recent advantages of deep reinforcement learning in Atari games and understanding hig…
