UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether

This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a flying sensor but also as a tether attachment device. Two robots are connected with a tether, allowing the UAV to anchor the tether to a structure located at the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the poor traversability of the UGV by not only providing a wider range of scanning and mapping from the air, but also by allowing the UGV to climb steep terrains with the winding of the tether. In addition, we present an autonomous framework for the collaborative navigation and tether attachment in an unknown environment. The UAV employs visual inertial navigation with 3D voxel mapping and obstacle avoidance planning. The UGV makes use of the voxel map and generates an elevation map to execute path planning based on a traversability analysis. Furthermore, we compared the pros and cons of possible methods for the tether anchoring from multiple points of view. To increase the probability of successful anchoring, we evaluated the anchoring strategy with an experiment. Finally, the feasibility and capability of our proposed system were demonstrated by an autonomous mission experiment in the field with an obstacle and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government