Collaborative Planning for Catching and Transporting Objects in Unstructured Environments

Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation.In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form.This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state.Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints.We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance.Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality

Low Dimensional State Representation Learning with Reward-shaped Priors

Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal policy directly from the sensory inputs, i.e the observations, often requires processing and storage of a huge amount of data. In the context of robotics, the cost of data from real robotics hardware is usually very high, thus solutions that achieve high sample-efficiency are needed. We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space. This mapping is learned with unsupervised learning using loss functions shaped to incorporate prior knowledge of the environment and the task. Using the samples from the state space, the optimal policy is quickly and efficiently learned. We test the method on several mobile robot navigation tasks in a simulation environment and also on a real robot.Comment: Paper Accepted at ICPR202

ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty

In this paper, we develop a modular neural network for real-time semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural networks. Although robotic perception is often divided between modern differentiable methods and classical explicit methods, a union of both is necessary for real-time and trustworthy performance. We introduce a novel Convolutional Bayesian Kernel Inference (ConvBKI) layer which incorporates semantic segmentation predictions online into a 3D map through a depthwise convolution layer by leveraging conjugate priors. We compare ConvBKI against state-of-the-art deep learning approaches and probabilistic algorithms for mapping to evaluate reliability and performance. We also create a Robot Operating System (ROS) package of ConvBKI and test it on real-world perceptually challenging off-road driving data.Comment: arXiv admin note: text overlap with arXiv:2209.1066

Development of VR Teaching System for Engine Dis-assembly

With the worldwide ravaging of the covid-19 epidemic, the traditional face-to-face education systems have been interrupted frequently. It is demanded to develop high-quality online education modalities. The webcasting based online classroom is one of the popular education modalities but suffers from poor teacher-student interactions and and low immersive learning experiences. This thesis aims to improve the online education quality by using the virtual reality (VR) technology. For the purpose of automobile engine education, we develop a VR based engine maintenance learning system. The system includes many teaching and learning components in VR enabled by the Unity engine. Users can immersively experience the complete engine disassembly process through the wearable VR display and interactive devices. The system is designed with an interactive layer, a control layer, and a physical data layer. Such a system architecture effectively separates the specific implementations of different domains and improves the R&D efficiency. Once new object models and process profiles are provided, the proposed system architecture requires no modification of codes for changed learning objects and processes. The efficiency and effictiveness of the proposed method are verfied by various experiments. The developed techniques can be useful for many other applications

A Situation-Aware Fear Learning (SAFEL) Model for Robots

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL’s success in generating contextual fear conditioning behaviour with predictive capabilities based on situational information

Ultra-Wideband Communication and Sensor Fusion Platform for the Purpose of Multi-Perspective Localization.

Localization is a keystone for a robot to work within its environment and with other robots. There have been many methods used to solve this problem. This paper deals with the use of beacon-based localization to answer the research question: Can ultra-wideband technology be used to effectively localize a robot with sensor fusion? This paper has developed an innovative solution for creating a sensor fusion platform that uses ultra-wideband communication as a localization method to allow an environment to be perceived and inspected in three dimensions from multiple perspectives simultaneously. A series of contributions have been presented, supported by an in-depth literature review regarding topics in this field of knowledge. The proposed method was then designed, built, and tested successfully in two different environments exceeding its required tolerances. The result of the testing and the ideas formulated throughout the paper were discussed and future work outlined on how to build upon this work in potential academic papers and projects

Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments

This paper presents Deep-PANTHER, a learning-based perception-aware trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments. Given the current state of the UAV, and the predicted trajectory and size of the obstacle, Deep-PANTHER generates multiple trajectories to avoid a dynamic obstacle while simultaneously maximizing its presence in the field of view (FOV) of the onboard camera. To obtain a computationally tractable real-time solution, imitation learning is leveraged to train a Deep-PANTHER policy using demonstrations provided by a multimodal optimization-based expert. Extensive simulations show replanning times that are two orders of magnitude faster than the optimization-based expert, while achieving a similar cost. By ensuring that each expert trajectory is assigned to one distinct student trajectory in the loss function, Deep-PANTHER can also capture the multimodality of the problem and achieve a mean squared error (MSE) loss with respect to the expert that is up to 18 times smaller than state-of-the-art (Relaxed) Winner-Takes-All approaches. Deep-PANTHER is also shown to generalize well to obstacle trajectories that differ from the ones used in training

Assessment of local path planners in a indoor and outdoor robot

Modern mobile robotics are entering commercial use in a variety of non-controlled environments. One such robot is the Roboguide service and guide robot for the visually impaired. For the smooth operation of a service robot in the daily life of its users, it is imperative that the paths along which the robot travels are intuitive, comfortable, and above all, safe. The goal of this thesis is to assess the viability of the Elastic Band, Timed Elastic Band and Dynamic Window Approach path-planners in a dynamic environment. This is accomplished through testing various scenarios typical in dynamic environments, including outright collisions and near-miss scenarios. The testing is done on a simulated platform. In addition to assessing the current viability of the path-planners in question, this thesis also aims to identify challenges and problems caused by the dynamic nature of the environment. The results suggest the Timed Elastic Band is the superior path-planner. Dynamic obstacles create problems for all the tested path-planners, and a future approach to cost-efficient dynamic prediction is suggested. The tests within this thesis are implemented using Robotic Operating System(ROS) and the robot simulation environment Gazebo. Implementations are based on real products and software modules.Nykyaikaista autonomista robotiikkaa on alettu soveltaa kaupallisessa käytössä erilaisissa kontrolloimattomissa toimintaympäristöissä. Yksi näistä sovelluskohteista on Roboguide, näkövammaisille suunnattu opasrobotti. Jotta robottia olisi intuitiivista ja turvallista käyttää, on oleellista, että robotti pystyy toimimaan arkipäivän eri tilanteissa helppokäyttöisesti, mukavasti ja ennen kaikkea turvallisesti. Tämän diplomityön tavoite on arvioida Elastic Band, Timed Elastic Band ja Dynamic Window Approach reittisuunnitelualgoritmien soveltuvuutta dynaamisessa ympäristössä. Tätä varten on toteutettu testisarja, jossa simuloidaan tyypillisiä dynaamisen ympäristön haasteita, kuten törmäys- ja läheltä-ohitustilanteita. Testaus toteutettiin simuloidulla alustalla. Eri reittisuunnittelualgoritmien soveltuvuuden arvioinnin lisäksi diplomityö pyrkii tunnistamaan dynaamisessa ympäristössä liikkumiseen liittyviä haasteita ja uhkakuvia. Testatuista algoritmeista Timed Elastic Band soveltuu selvästi parhaiten dynaamiseen ympäristöön. Lisäksi työssä ehdotetaan lähestymistapaa dynaamisten esteiden sijainnin ennustamiseen laskennallisesti tehokkaasti. Testaus on toteutettu ROS-pohjaisella robotilla ja testit on suoritettu Gazebo-simulointiympäristössä. Testaus ja simuloitu robotti perustuu aitoon tuotteeseen ja sen komponentteihin

Characterisation of a nuclear cave environment utilising an autonomous swarm of heterogeneous robots

As nuclear facilities come to the end of their operational lifetime, safe decommissioning becomes a more prevalent issue. In many such facilities there exist ‘nuclear caves’. These caves constitute areas that may have been entered infrequently, or even not at all, since the construction of the facility. Due to this, the topography and nature of the contents of these nuclear caves may be unknown in a number of critical aspects, such as the location of dangerous substances or significant physical blockages to movement around the cave. In order to aid safe decommissioning, autonomous robotic systems capable of characterising nuclear cave environments are desired. The research put forward in this thesis seeks to answer the question: is it possible to utilise a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment? This is achieved through examination of the three key components comprising a heterogeneous swarm: sensing, locomotion and control. It will be shown that a heterogeneous swarm is not only capable of performing this task, it is preferable to a homogeneous swarm. This is due to the increased sensory and locomotive capabilities, coupled with more efficient explorational prowess when compared to a homogeneous swarm