publications

2023

1. Human-Flow-Aware Long-Term Mobile Robot Task Planning Based on Hierarchical Reinforcement Learning

   Yuchen, L., Palmieri, L., Georgievski, I., and Aiello, M.

   IEEE Robotics and Automation Letters 2023

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   The difficulty in finding long-term planning policies for a mobile robot increases when operating in crowded and dynamic environments. State-of-the-art approaches do not consider cues of human-robot-shared dynamic environments. Aiming to fill this gap, we present a novel Human-Flow-Aware Guided Hierarchical Dyna-Q (HA-GHDQ) algorithm, which solves long-term robot task planning problems by using human motion patterns encoded in Maps of Dynamics (MoDs). To tackle the complexity of long-term robot operation in dynamic environments, we propose a combination of symbolic planning and Hierarchical Reinforcement Learning (HRL) that generates robot policies considering cost information derived from MoDs. We evaluated HA-GHDQ in a factory environment with two simulation and one real-world datasets to complete a transportation-and-assembly task. Our approach outperforms the baselines with respect to sample efficiency and final plan quality. Moreover, we show that it is more adaptable and robust against environmental changes than the baselines.

2. A Benchmark for Multi-Robot Planning in Realistic, Complex and Cluttered Environments

   Schaefer, Simon, Palmieri, Luigi, Heuer, Lukas, Dillmann, Ruediger, Koenig, Sven, and Kleiner, Alexander

   IEEE International Conference on Robotics and Automation 2023

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   Several successful approaches exist for solving the complex problem of multi-robot planning and coordination. Due to the lack of adequate benchmarking tools, comparing these approaches and judging their suitability for use in realistic scenarios is currently difficult. Therefore, we propose an open-source benchmark suite that aims to close this gap. Unlike existing benchmarks, our approach uses full-stack multi-robot navigation systems in realistic 3D simulated environments from the intralogistic and household domains. Using the open-source frameworks ROS 2, Gazebo and RMF allows the user to add other robot platforms easily. The framework provides easy-to-use abstractions, typical metrics and interfaces to several established planning libraries for multi-robot systems. With all these features, our framework successfully aids practitioners and researchers in comparing multi-robot planning and coordination systems to the state of the art. Our experiments show how the proposed benchmark simplifies gaining insights on relevant close to real-life robotics use cases.

3. The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots

   Molina, Sergi, Mannucci, Anna, Magnusson, Martin, Adolfsson, Daniel, Andreasson, Henrik, Hamad, Mazin, Abdolshah, Saeed, Chadalavada, Ravi Teja, Palmieri, Luigi, Linder, Timm, and others,

   IEEE Robotics & Automation Magazine 2023

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   Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

4. Survey of maps of dynamics for mobile robots

   Kucner, Tomasz Piotr, Magnusson, Martin, Mghames, Sariah, Palmieri, Luigi, Verdoja, Francesco, Swaminathan, Chittaranjan Srinivas, Krajnı́k, Tomáš, Schaffernicht, Erik, Bellotto, Nicola, Hanheide, Marc, and others,

   The International Journal of Robotics Research 2023

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   Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

5. THOR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction

   Almeida, Tiago Rodrigues, Rudenko, Andrey, Schreiter, Tim, Zhu, Yufei, Maestro, Eduardo Gutierrez, Morillo-Mendez, Lucas, Kucner, Tomasz P., Mozos, Oscar Martinez, Magnusson, Martin, Palmieri, Luigi, Arras, Kai O., and Lilienthal, Achim J.

   Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops 2023

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   Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.

2022

1. The Atlas Benchmark: an Automated Evaluation Framework for Human Motion Prediction

   Rudenko, A., Palmieri, L., Huang, W., Lilienthal, A. J., and Arras, K. O.

   In 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2022

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   Human motion trajectory prediction, an essential task for autonomous systems in many domains, has been on the rise in recent years. With a multitude of new methods proposed by different communities, the lack of standardized benchmarks and objective comparisons is increasingly becoming a major limitation to assess progress and guide further research. Existing benchmarks are limited in their scope and flexibility to conduct relevant experiments and to account for contextual cues of agents and environments. In this paper we present Atlas, a benchmark to systematically evaluate human motion trajectory prediction algorithms in a unified framework. Atlas offers data preprocessing functions, hyperparameter optimization, comes with popular datasets and has the flexibility to setup and conduct underexplored yet relevant experiments to analyze a method’s accuracy and robustness. In an example application of Atlas, we compare five popular model- and learning-based predictors and find that, when properly applied, early physics-based approaches are still remarkably competitive. Such results confirm the necessity of benchmarks like Atlas.

2. The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized

   Schreiter, Tim, Almeida, Tiago Rodrigues, Zhu, Yufei, Maestro, Eduardo Gutierrez, Morillo-Mendez, Lucas, Rudenko, Andrey, Kucner, Tomasz P, Mozos, Oscar Martinez, Magnusson, Martin, Palmieri, Luigi, and others,

   IEEE ROMAN 2022 SIRRW Workshop 2022

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   Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.

3. Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation

   Goel, Yash, Vaskevicius, Narunas, Palmieri, Luigi, Chebrolu, Nived, and Stachniss, Cyrill

   IEEE IROS Workshop PNARUDE 2022

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   We investigate the task of object goal navigation in unknown environments where the target is specified by a semantic label (e.g. find a couch). Such a navigation task is especially challenging as it requires understanding of semantic context in diverse settings. Most of the prior work tackles this problem under the assumption of a discrete action policy whereas we present an approach with continuous control which brings it closer to real world applications. We propose a deep neural network architecture and loss function to predict dense cost maps that implicitly contain semantic context and guide the robot towards the semantic goal. We also present a novel way of fusing mid-level visual representations in our architecture to provide additional semantic cues for cost map prediction. The estimated cost maps are then used by a sampling-based model predictive controller (MPC) for generating continuous robot actions. The preliminary experiments suggest that the cost maps generated by our network are suitable for the MPC and can guide the agent to the semantic goal more efficiently than a baseline approach. The results also indicate the importance of mid-level representations for navigation by improving the success rate by 7 percentage points.

4. Benchmarking the utility of maps of dynamics for human-aware motion planning.

   Swaminathan, Chittaranjan Srinivas, Kucner, Tomasz Piotr, Magnusson, Martin, Palmieri, Luigi, Molina, Sergi, Mannucci, Anna, Pecora, Federico, and Lilienthal, Achim J

   Frontiers in Robotics and AI 2022

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   Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

5. Towards Reliable Benchmarking for Multi-Robot Planning in Realistic, Cluttered and Complex Environments

   Schaefer, Simon, Palmieri, Luigi, Heuer, Lukas, Duijkeren, Niels, Dillmann, Ruediger, Koenig, Sven, and Kleiner, Alexander

   IEEE IROS Workshop Evaluating Motion Planning Performance 2022

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   Multi-robot planning and coordination is a hard task to solve particularly in cluttered and complex environ- ments. Several methods exist for solving such task. Due to the lack of adequate benchmarking tools, comparing these approaches and judging their suitability for use in realistic scenarios is hardly possible. To this end, in this work we propose a novel benchmark toolchain that aims to close this gap. Differently from the related works, our benchmark uses full- stack multi-robot navigation systems in realistic 3D simulated intralogistic and household environments. The usage of open- source frameworks ROS2, Gazebo and RMF gives the user the possibility to easily add novel robot platforms. The frame- work provides easy-to-use and to-extend abstractions, common metrics and interfaces to several well-known planning libraries for multi-robot systems. With all these features our framework successfully aids practitioners and researchers in comparing multi-robot planning and coordination algorithms against the state of the art.

2021

1. Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots

   Heiden, E., Palmieri, L., Bruns, L., Arras, K. O., Sukhatme, G., and Koenig, S.

   IEEE Robotics and Automation Letters 2021

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   Planning smooth and energy-efficient motions for wheeled mobile robots is a central task in applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, a wide variety of sampling-based motion planners, extend functions, and path-improvement techniques have been proposed for such systems. Choosing the best combination of components that fit an application is a tedious exercise, even for expert users. With the goal of aiding researchers in designing novel planning algorithms and evaluating path finding solutions, we present Bench-MR: the first open-source sampling-based motion-planning benchmark designed for nonholonomic, wheeled mobile robots. Unlike related benchmarks, Bench-MR offers an extensive number and a variety of algorithm families, post-smoothing techniques, steer functions, optimization criteria, complex environments resembling real-world applications (such as navigating warehouses, moving in cluttered cities and parking) and performance metrics that make it a comprehensive comparison and analysis framework. Bench-MR is easy to use and extend, and, as shown in the detailed examples, it significantly helps practitioners and researchers to analyze and compare their work against the state of the art.

2. Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments

   Rudenko, A., Palmieri, L., Doellinger, J., Lilienthal, A. J., and Arras, K. O.

   IEEE Robotics and Automation Letters 2021

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   Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we infer occupancy priors of human motion using only semantic environment information as input. To this end, we apply and discuss a traditional Inverse Optimal Control approach, and propose a novel approach based on Convolutional Neural Networks (CNN) to predict future occupancy maps. Our CNN method produces flexible context-aware occupancy estimations for semantically uniform map regions and generalizes well already with small amounts of training data. Evaluated on synthetic and real-world data, it shows superior results compared to several baselines, marking a qualitative step-up in semantic environment assessment.

2020

1. 

   Human motion trajectory prediction: A survey

   Rudenko, Andrey, Palmieri, Luigi, Herman, Michael, Kitani, Kris M, Gavrila, Dariu M, and Arras, Kai O

   The International Journal of Robotics Research 2020

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   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.

2. Maps of Dynamics

   Kucner, Tomasz Piotr, Lilienthal, Achim J, Magnusson, Martin, Palmieri, Luigi, and Swaminathan, Chittaranjan Srinivas

   2020

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3. An nmpc approach using convex inner approximations for online motion planning with guaranteed collision avoidance

   Schoels, Tobias, Palmieri, Luigi, Arras, Kai O, and Diehl, Moritz

   In 2020 IEEE International Conference on Robotics and Automation (ICRA) 2020

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   Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remains subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a model predictive control (MPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds a dynamically feasible and collision free trajectory in few iterations, typically one, and preserves feasibility during further iterations. CIAO scales to high-dimensional systems, is computationally efficient, and guarantees both kinodynamic feasibility and continuous-time collision avoidance. Our experimental evaluation shows that the approach outperforms state of the art baselines in terms of planning efficiency and path quality. Furthermore real-world experiments show its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments.

4. CIAO*: MPC-based Safe Motion Planning in Predictable Dynamic Environments

   Schoels, Tobias, Rutquist, Per, Palmieri, Luigi, Zanelli, Andrea, Arras, Kai O, and Diehl, Moritz

   arXiv preprint arXiv:2001.05449 2020

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   Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore, it generalizes CIAO’s free region concept to arbitrary norms and proposes a cost function to approximate time optimal motion planning. The proposed method, CIAO*, finds kinodynamically feasible and collision free trajectories for constrained single body robots using model predictive control (MPC). It optimizes the motion of one agent and accounts for the predicted movement of surrounding agents and obstacles. The experimental evaluation shows that CIAO* reaches close to time optimal behavior.

2019

1. 

   Dispertio: Optimal sampling for safe deterministic motion planning

   Palmieri, Luigi, Bruns, Leonard, Meurer, Michael, and Arras, Kai O

   IEEE Robotics and Automation Letters 2019

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   A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.

2. Informed information theoretic model predictive control

   Kusumoto, Raphael, Palmieri, Luigi, Spies, Markus, Csiszar, Akos, and Arras, Kai O

   In 2019 International Conference on Robotics and Automation (ICRA) 2019

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   The problem of minimizing cost in nonlinear control systems with uncertainties or disturbances remains a major challenge. Model predictive control (MPC), and in particular sampling-based MPC has recently shown great success in complex domains such as aggressive driving with highly nonlinear dynamics. Sampling-based methods rely on a prior distribution to generate samples in the first place. Obviously, the choice of this distribution highly influences efficiency of the controller. Existing approaches such as sampling around the control trajectory of the previous time step perform suboptimally, especially in multi-modal or highly dynamic settings. In this work, we therefore propose to learn models that generate samples in low-cost areas of the state-space, conditioned on the environment and on contextual information of the task to solve. By using generative models as an informed sampling distribution, our approach exploits guidance from the learned models and at the same time maintains robustness properties of the MPC methods. We use Conditional Variational Autoencoders (CVAE) to learn distributions that imitate samples from a training dataset containing optimized controls. An extensive evaluation in the autonomous navigation domain suggests that replacing previous sampling schemes with our learned models considerably improves performance.

2018

1. Joint long-term prediction of human motion using a planning-based social force approach

   Rudenko, Andrey, Palmieri, Luigi, and Arras, Kai O

   In 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018
2. Human motion prediction under social grouping constraints

   Rudenko, Andrey, Palmieri, Luigi, Lilienthal, Achim J, and Arras, Kai O

   In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018

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   Accurate long-term prediction of human motion in populated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety of factors including the person’s intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members’ motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics.

3. Gradient-informed path smoothing for wheeled mobile robots

   Heiden, Eric, Palmieri, Luigi, Koenig, Sven, Arras, Kai O, and Sukhatme, Gaurav S

   In 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018
4. Down the cliff: Flow-aware trajectory planning under motion pattern uncertainty

   Swaminathan, Chittaranjan Srinivas, Kucner, Tomasz Piotr, Magnusson, Martin, Palmieri, Luigi, and Lilienthal, Achim J

   In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018

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   In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.

2017

1. Hybrid curvature steer: A novel extend function for sampling-based nonholonomic motion planning in tight environments

   Banzhaf, Holger, Palmieri, Luigi, Nienhüser, Dennis, Schamm, Thomas, Knoop, Steffen, and Zöllner, J Marius

   In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) 2017
2. Kinodynamic motion planning on Gaussian mixture fields

   Palmieri, Luigi, Kucner, Tomasz P, Magnusson, Martin, Lilienthal, Achim J, and Arras, Kai O

   In 2017 IEEE International Conference on Robotics and Automation (ICRA) 2017

2016

1. Spencer: A socially aware service robot for passenger guidance and help in busy airports

   Triebel, Rudolph, Arras, Kai, Alami, Rachid, Beyer, Lucas, Breuers, Stefan, Chatila, Raja, Chetouani, Mohamed, Cremers, Daniel, Evers, Vanessa, Fiore, Michelangelo, and others,

   In Field and service robotics 2016

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   We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

2. RRT-based nonholonomic motion planning using any-angle path biasing

   Palmieri, Luigi, Koenig, Sven, and Arras, Kai O

   In 2016 IEEE International Conference on Robotics and Automation (ICRA) 2016
3. A fast random walk approach to find diverse paths for robot navigation

   Palmieri, Luigi, Rudenko, Andrey, and Arras, Kai O

   IEEE Robotics and Automation Letters 2016

2015

1. 

   Distance metric learning for RRT-based motion planning with constant-time inference

   Palmieri, Luigi, and Arras, Kai O

   In 2015 IEEE International Conference on Robotics and Automation (ICRA) 2015

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   The distance metric is a key component in RRT-based motion planning that deeply affects coverage of the state space, path quality and planning time. With the goal to speed up planning time, we introduce a learning approach to approximate the distance metric for RRT-based planners. By exploiting a novel steer function which solves the two-point boundary value problem for wheeled mobile robots, we train a simple nonlinear parametric model with constant-time inference that is shown to predict distances accurately in terms of regression and ranking performance. In an extensive analysis we compare our approach to an Euclidean distance baseline, consider four alternative regression models and study the impact of domain-specific feature expansion. The learning approach is shown to be faster in planning time by several factors at negligible loss of path quality.

2014

1. A novel RRT extend function for efficient and smooth mobile robot motion planning

   Palmieri, Luigi, and Arras, Kai O

   In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems 2014
2. Distance metric learning for RRT-based motion planning for wheeled mobile robots

   Palmieri, Luigi, and Arras, Kai O

   In 2014 IROS Machine Learning in Planning and Control of Robot Motion Workshop 2014

2013

1. WiFi-based passive bistatic radar by using moving target indicator and least square adaptive filtering

   Buonanno, Aniello, D’Urso, Michele, and Palmieri, Luigi

   In 2013 IEEE International Symposium on Phased Array Systems and Technology 2013

2012