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Deep-Reinforcement-Learning-Based Path Planning for Industrial Robots Using Distance Sensors as Observation

Deep-Reinforcement-Learning-Based Path Planning for Industri...

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IEEE International Conference on Control and Robotics engineering (ICCRE)

作者： Teham Bhuiyan Linh Kästner Yifan Hu Benno Kutschank Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

Traditionally, collision-free path planning for industrial robots is realized by sampling-based algorithms such as RRT (Rapidly-exploring Random Tree), PRM (Probabilistic Roadmap), etc. Sampling-based algorithms require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing these approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time- and cost-intensive. On the other hand, DRL (Deep Reinforcement Learning) has shown remarkable results in dealing with complex environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We propose an easily reproducible method to train an agent in randomized scenarios achieving generalization for unknown environments. We evaluated our model against state-of-the-art sampling- and DRL-based planners in several experiments containing static and dynamic obstacles. Results show the adaptability of our agent in new environments and the superiority in terms of path length and execution time compared to conventional methods. Our code is available on GitHub [1].

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Evaluating Optimization Approaches for Deep-Reinforcement-Learning-based Navigation Agents

Evaluating Optimization Approaches for Deep-Reinforcement-Le...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Linh Kästner Liam Roberts Teham Bhuiyan Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, Deep Reinforcement learning has made remarkable progress in various application areas such as control of robots and vehicles, simulation, and natural language processing. In recent years, various research works applied DRL to conduct different kinds of tasks for autonomous navigation of vehicles and robots such as lane changing, cruise control, or obstacle avoidance. However, DRL training is still a tedious and difficult process due to long training times, catastrophic forgetfulness and the myopic nature of DRL agents. In this paper, we integrate and explore the effect of a variety of state-of-the-art optimization approaches for DRL agents such as imitation learning, behavior cloning, frame stacking, and hindsight replay for autonomous obstacle avoidance. We evaluate the effect of each of these changes to the DRL agent in terms of training- as well as navigational performance. The resulting agents are compared against baseline approaches without those optimizations and we found an increase in navigational performance for some of these methods while other optimization approaches surprisingly resulted in decreased performing. The findings of this paper should aid in development of DRL approaches for autonomous navigation approaches.

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HabitatDyn Dataset: Salient Object Detection to Kinematics Estimation

HabitatDyn Dataset: Salient Object Detection to Kinematics E...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Zhengcheng Shen Yi Gao Linh Kästner Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

The advancement of computer vision and machine learning has made datasets crucial for further research and applications. However, the creation and development of indoor mobile robots with advanced recognition capabilities are hindered by the lack of appropriate datasets. Existing image or video processing datasets are unable to depict observations from a moving robot accurately, and they do not contain the kinematics information necessary for robotic tasks. Synthetic data, on the other hand, are cost-effective to create and offer greater flexibility for adapting to various applications. Hence, they are widely utilized in both research and industry. In this paper, we propose the dataset HabitatDyn, which contains synthetic RGB videos, semantic labels, depth information, as well as kinetics information. HabitatDyn was created from the perspective of a mobile robot with a moving camera and contains 30 scenes featuring six different types of moving objects with varying velocities. To demonstrate the usability of our dataset, two existing algorithms are used for evaluation and an approach to estimate the distance between the object and camera is implemented based on these segmentation methods and evaluated through the dataset. With the availability of this dataset, we aspire to foster further advancements in the field of mobile robotics, leading to more capable and intelligent robots that can navigate and interact with their environments more effectively. The code is publicly available at https://***/ignc-research/HabitatDyn.

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Imagination-Augmented Cost Map for Local Navigation

Imagination-Augmented Cost Map for Local Navigation

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International Conference on Ubiquitous Robots (UR)

作者： Zhengcheng Shen Linh Kästner Magdalena Yordanova Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

Autonomous navigation of mobile robots is an essential task for various industries. Sensor data is crucial to ensure safe and reliable navigation. However, sensor observations are often limited by different factors like field of view, sensor height or occlusion. Spatial imagination can assist to enhance the view and aid navigation in complex or unknown situations where only limited sensor observation is available. In this paper, we propose an imagination-enhanced navigation based on 2D laser scan data and semantic information to show the benefits of the imagination on navigation. The system contains an imagination module, which can predict the entire restricted area of the object, either occupied area or restricted area for common sense, based on laser scanner data with semantic label. The imagination module is trained in a supervised manner using a collected training dataset from a 2D simulator. Four different imagination models are trained, and the imagination results are evaluated. Subsequently, the imagination results are integrated into the local and global cost map to benefit the navigation procedure. The approach is validated on three different test maps, with seven different paths for each map. The quality and numeric results showed that the agent with the imagination module could generate more reliable paths without passing beneath the object or access the predefined restricted area.

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Predicting Navigational Performance of Dynamic Obstacle Avoidance Approaches Using Deep Neural Networks

Predicting Navigational Performance of Dynamic Obstacle Avoi...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Linh Kästner Alexander Christian Ricardo Sosa Mello Bo Li Bassel Fatloun Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

Over the past decades, countless autonomous navigation and dynamic obstacle avoidance approaches have been proposed by various research works. However, to bridge the gap between research and industries, these approaches are required to be extensively evaluated and benchmarked within various different setting, scenarios, and maps. However, conducting these test runs is tedious and time-consuming. Furthermore, simulation runs and test on real robots can not always cover all potentially occurring scenarios or are inaccurate in certain settings and circumstances especially when a high number of pedestrians or other dynamic entities are involved. In this paper, we propose an approach to predict the navigational performance of navigation approaches for new and unknown maps, scenarios, and robots without the necessity to conduct the actual test runs. Therefore, we acquire a large dataset consisting of thousands of evaluation runs within crowded environments from both simulation and real-world runs, which were conducted using the arena-bench platform of our previous works [1] and trained several neural network architectures to predict relevant navigational performance metrics such as collision rates or path efficiency. We demonstrate the feasibility of our neural networks by predicting the most relevant metrics with up to 95 percent accuracy compared to the groundtruth data acquired by an actual simulation run. Using this approach could prove beneficial for a number of applications and save valuable time and costs in that the performance of new navigation algorithms for crowded environments can be estimated and predicted on new maps, scenarios, and on new robots. We made the code publicly available at https://***/ignc-research/navprediction.

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Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks

Integrative Object and Pose to Task Detection for an Augment...

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International Conference on Communications and Electronics, ICCE

作者： Linh Kästner Leon Eversberg Marina Mursa Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

ISBN: (数字)9781728154718

ISBN: (纸本)9781728154725

As a result of an increasingly automatized and digitized industry, processes are becoming more complex. Augmented Reality has shown considerable potential in assisting workers with complex tasks by enhancing user understanding and experience with spatial information. However, the acceptance and integration of AR into industrial processes is still limited due to the lack of established methods and tedious integration efforts. Meanwhile, deep neural networks have achieved remarkable results in computer vision tasks and bear great prospects to enrich Augmented Reality applications. In this paper, we propose an Augmented-Reality-based human assistance system to assist workers in complex manual tasks where we incorporate deep neural networks for computer vision tasks. More specifically, we combine Augmented Reality with object and action detectors to make workflows more intuitive and flexible. To evaluate our system in terms of user acceptance and efficiency, we conducted several user studies. We found a significant reduction in time to task completion in untrained workers and a decrease in error rate. Furthermore, we investigated the users learning curve with our assistance system.

关键词： computer vision Error analysis Neural networks Manuals computer architecture Task analysis Augmented reality

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All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners

All-in-One: A DRL-based Control Switch Combining State-of-th...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Linh Kastner Johannes Cox Teham Buiyan Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

Autonomous navigation of mobile robots is an es-sential aspect in use cases such as delivery, assistance or logistics. Although traditional planning methods are well integrated into existing navigation systems, they struggle in highly dynamic en-vironments. On the other hand, Deep-Reinforcement-Learning-based methods show superior performance in dynamic obstacle avoidance but are not suitable for long-range navigation and struggle with local minima. In this paper, we propose a Deep-Reinforcement-Learning-based control switch, which has the ability to select between different planning paradigms based solely on sensor data observations. Therefore, we develop an interface to efficiently operate multiple model-based, as well as learning-based local planners and integrate a variety of state-of-the-art planners to be selected by the control switch. Subsequently, we evaluate our approach against each planner individually and found improvements in navigation performance especially for highly dynamic scenarios. Our planner was able to prefer learning-based approaches in situations with a high number of obstacles while relying on the traditional model-based planners in long corridors or empty spaces.

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HabitatDyn 2.0: Dataset for Spatial Anticipation and Dynamic Object Localization

HabitatDyn 2.0: Dataset for Spatial Anticipation and Dynamic...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhengcheng Shen Linh Kästner Yi Gao Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The ability of a robot to perceive and understand its environment is crucial for its actions and behavior. Humans are adept at using semantic information for object localization and path planning, a skill that robots need to emulate for intelligent adaptation in dynamic settings. Training of the spatial anticipation ability, which can enhance spatial perception through semantic understanding, necessitates the availability of appropriate data. Although extensive research has been conducted on datasets for outdoor environments, especially in the context of autonomous driving, there is still a notable lack of datasets specifically designed for indoor environments, with a focus on dynamic object localization. This paper introduces HabitatDyn 2.0, a dataset specifically designed for enhancing object localization capabilities with semantic information from a robot’s perspective. Besides RGB videos, semantic annotations, and depth information, HabitatDyn 2.0 also features top-down view labels for dynamic objects, which is required for training the spatial anticipation ability based on semantic information. Additionally, an algorithm that leverages spatial anticipation for dynamic object localization is presented, trained, and evaluated on the dataset.

关键词： Location awareness Training Heuristic algorithms Semantics Robot sensing systems Spatial databases Path planning

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Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation

Enhancing Navigational Safety in Crowded Environments using ...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Linh Kästner Junhui Lil Zhengcheng Shen Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

ISBN: (纸本)9781665456814

Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emerged as an alternative planning method to conservative approaches and promises more efficient and flexible navigation. However, in highly dynamic environments employing different kinds of obstacle classes, safe navigation still presents a grand challenge. In this paper, we propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information. In particular, the agent learns object-specific behavior by contemplating the specific danger zones to enhance safety around vulnerable object classes. We tested the approach against a benchmark obstacle avoidance approach and found an increase in safety. Furthermore, we demonstrate that the agent could learn to navigate more safely by keeping an individual safety distance dependent on the semantic information.

关键词： Training Navigation Semantics Reinforcement learning Robustness Safety Behavioral sciences

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Autonomous Navigation in Complex Environments using Memory-Aided Deep Reinforcement Learning

Autonomous Navigation in Complex Environments using Memory-A...

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IEEE/SICE International Symposium on System Integration

作者： Linh Kästner Zhengcheng Shen Cornelius Marx Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

ISBN: (数字)9781728176581

ISBN: (纸本)9781728176598

Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to navigate in unknown and complex environments is paramount in industrial robotics. Reinforcement learning approaches have shown remarkable success in dealing with unknown situations and react accordingly without manually engineered guidelines and overconservative measures. However, these approaches are often restricted to short range navigation and are prone to local minima due to a lack of a memory module. Thus, the navigation in complex environments such as mazes, long corridors or concave areas is still an open frontier. In this paper, we incorporate a variety of recurrent neural networks to cope with these challenges. We train a reinforcement learning based agent within a 2D simulation environment of our previous work and extend it with a memory module. The agent is able to navigate solely on sensor data observations which are directly mapped to actions. We evaluate the performance on different complex environments and achieve enhanced results within complex environments compared to memory-free baseline approaches.

关键词： Training Navigation Two dimensional displays Reinforcement learning Memory modules System integration Task analysis

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