This paper presents a framework for multi-agent navigation in structured but dynamic environments, integrating three key components: a shared semantic map encoding metric and semantic environmental knowledge, a claim policy for coordinating access to areas within the environment, and a Model Predictive Controller for generating motion trajectories that respect environmental and coordination constraints. The main advantages of this approach include: (i) enforcing area occupancy constraints derived from specific task requirements; (ii) enhancing computational scalability by eliminating the need for collision avoidance constraints between robotic agents; and (iii) the ability to anticipate and avoid deadlocks between agents. The paper includes both simulations and physical experiments demonstrating the framework's effectiveness in various representative scenarios.

Robotization is considered a key solution to labor shortages in the agri-food industry. However, deploying robots in natural environments is challenging due to unpredictable factors such as plant variances and occlusions. This paper focuses on the localization of tomato trusses for autonomous harvesting by servoing a robot-mounted camera to different viewpoints. We build on previous work where the robot is provided with prior knowledge of the tomato plant. Specifically, the geometric relations between the trusses are modeled as ranges, which reflect uncertainty. Our main contribution is an approach that represents this uncertainty as polytope volumes. Polytopes enable scalable reasoning that facilitates likelihood estimation for viewpoint selection. Our method first constructs polytope hypotheses regarding the truss locations based on prior plant knowledge. It then refines the polytope shapes using Bayesian updates based on camera observations. Finally, the polytopes are used to select the next viewpoint that maximizes the chance of observing a new tomato truss. Experiments show that polytope-based viewpoint selection speeds up truss localization compared to earlier methods, advancing robotic harvesting.

This work presents the implementation and evaluation of a real-time collision-avoiding motion planning algorithm for highly dynamic environments. By combining short-horizon obstacle estimation with the robot constraints, our method implements collision-avoiding situational-aware motion planning by heuristically exploring multiple relevant paths. Directly feeding the current motion setpoint of the path into the low-level controller closes the loop between motion planning and low-level control, ensuring constraint-aware execution. Its practical implementation in physical robots in dynamic RoboCup-like scenarios validated its effectiveness, with low computational costs enabling fast adaptation to changing environments. Furthermore, the capabilities of our motion planner were demonstrated during RoboCup 2024 and practice matches1.

We propose a shared semantic map architecture to construct and configure Model Predictive Controllers (MPC) dynamically, that solve navigation problems for multiple robotic agents sharing parts of the same environment. The navigation task is represented as a sequence of semantically labeled areas in the map, that must be traversed sequentially, i.e. a route. Each semantic label represents one or more constraints on the robots’ motion behaviour in that area. The advantages of this approach are: (i) an MPC-based motion controller in each individual robot can be (re-)configured, at runtime, with the locally and temporally relevant parameters; (ii) the application can influence, also at runtime, the navigation behaviour of the robots, just by adapting the semantic labels; and (iii) the robots can reason about their need for coordination, through analyzing over which horizon in time and space their routes overlap. The paper provides simulations of various representative situations, showing that the approach of runtime configuration of the MPC drastically decreases computation time, while retaining task execution performance similar to an approach in which each robot always includes all other robots in its MPC computations.

Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the horizon of these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been conducted on how varying prediction horizons affect AV safety and other vehicle performance metrics, resulting in undefined horizon requirements for prediction methods. Our study addresses this gap by examining the effects of different prediction horizons on AV performance, focusing on safety, comfort, and efficiency. Through multiple experiments using a state-of-the-art, risk-based predictive trajectory planner, we simulated predictions with horizons up to 20 seconds. Based on our simulations, we propose a framework for specifying the minimum required and optimal prediction horizons depending on specific AV performance criteria and application needs. Our results indicate that a horizon of 1.6 seconds is required to prevent collisions with crossing pedestrians, horizons of 7-8 seconds yield the best efficiency, and horizons up to 15 seconds improve passenger comfort. We conclude that prediction horizon requirements are application-dependent, and recommend aiming for a prediction horizon of 11.8 seconds as a general guideline for applications involving crossing pedestrians.

In recent years, machine learning (ML) based digital twins (DTs) have seen widespread application in the anomaly detection domain. A search-based literature survey revealed that the majority of the case studies focus on large-scale systems (i.e., nuclear power plant, aerospace, and power grid) producing extensive data. Our work aims to investigate the performance of this technology in smaller-scale systems that generate less data. In this case study, we developed a ML-based DT of the mobility system, the omni wheels, of the Turtle soccer robots. The DT is capable of analyzing historical data collected from the physical robots and differentiating between damaged and undamaged wheels. Our experiments suggest that ML-based DT of small-scale systems is indeed capable of achieving relatively accurate results for anomaly detection use-cases.

An automated vehicle (AV) must always have a correct representation of the drivable space to position itself accurately and operate safely. To determine the drivable space, current research focuses on single sources of information, either using pre-computed high-definition maps, or mapping the environment online with sensors such as LiDARs or cameras. However, each of these information sources can fail, some are too costly, and maps could be outdated. In this work a new method for situation-aware drivable space (SDS) estimation combining multiple information sources is proposed, which is also suitable for AVs equipped with inexpensive sensors. Depending on the situation, semantic information of sensed objects is combined with domain knowledge to estimate the drivability of the space surrounding each object (e.g. traffic light, another vehicle). These estimates are modeled as probabilistic graphs to account for the uncertainty of information sources, and an optimal spatial configuration of their elements is determined via graph-based simultaneous localization and mapping (SLAM). To investigate the robustness of SDS towards potentially unreliable sensors and maps, it has been tested in a simulation environment and real world data. Results on different use cases (e.g. straight roads, curved roads, and intersections) show considerable robustness towards unreliable inputs, and the recovered drivable space allows for accurate in-lane localization of the AV even in extreme cases where no prior knowledge of the road network is available.

This paper proposes a method to integrate the rich semantic data-set provided by Building Information Modeling (BIM) with robotics world models, taking as use case indoor semantic localization in a large university building. We convert a subset of semantic entities with associated geometry present in BIM models and represented in the Industry Foundation Classes (IFC) data format to a robot-specific world model representation. This representation is then stored in a spatial database from which the robot can query semantic objects in its immediate surroundings. The contribution of this work is that, from this query, the robot’s feature detectors are configured and used to make explicit data associations with semantic structural objects from the BIM model that are located near the robot’s current position. A graph-based approach is then used to localize the robot, incorporating the explicit map-feature associations for localization. We show that this explainable model-based approach allows a robot equipped with a 2D LiDAR and odometry to track its pose in a large indoor environment for which a BIM model is available.

Vitreoretinal eye surgery requires ultimate surgeon's precision and skills. Significant variation exists in the approaches between different surgeons. Procedures are often indicated as difficult to perform. The surgeon's personal skill level is considered critical, including hand motion stability and left/right hand dexterity. Precision of instrument manipulation is a key requirement and a limiting factor in such procedures. The collaboration between high-precision research at the Eindhoven University of Technology and a spin-off company PRECEYES has produced a working prototype for teleoperation robot-assistance during vitreoretinal surgery. This PRECEYES Surgical System enables the surgeon to achieve a higher level of precision and decrease the time span of procedures.

This paper introduces a three-dimensional volumetric representation for safe navigation. It is based on the OctoMap representation framework that probabilistically fuses sensor measurements to represent the occupancy probability of volumes. To achieve safe navigation in a domestic environment this representation is extended with a model of the occupancy probability if no sensor measurements are received, and a proactive approach to deal with unpredictably moving obstacles that can arise from behind occlusions by always expecting obstacles to appear on the robot's path. By combining the occupancy probability of volumes with the position uncertainty of the robot, a probability of collision is obtained. It is shown that by relating this probability to a safe velocity limit a robot in a real domestic environment can move close to a certain maximum velocity but decides to attain a slower safe velocity limit when it must, analogous to velocity limits and warning signs in traffic.

Bilateral control architectures include multiple control elements. In general, the relation between a single control element and the stability of the entire system is non-linear. Therefore, stability is standard evaluated a posteriori, rendering the control design process to be complex and highly iterative. A priori understanding of stability constraints would simplify the design of control elements and, as performance is fundamentally limited by stability, could provide specific guidelines whether and how performance of the bilateral teleoperation system can be optimized. This paper presents a numerical visualization method that enables stability-based control design using classical loopshaping techniques: Frequency-domain Mapping of Bilateral Stability (FMBS). Unlike current stability-based control design approaches, the FMBS method i) is not limited to a fixed control element, a fixed control architecture or system dynamics and ii) enables the implementation of all often used stability criteria. The advantages of the FMBS method are theoretically validated through the use of two test cases, extracted from literature. Using the FMBS method, it is shown that control elements can be redesigned to achieve superior performance.

The inherent transparency-stability trade-off in bilateral teleoperation poses a challenge to design controllers that find a proper balance between both requirements. Furthermore, when the environment of the teleoperation system varies within a wide range, a single controller might not be sufficient to achieve both stability and transparency. Therefore, we propose the synthesis of a switching robust controller that increases the environment stiffness range for which both stability and transparency is achieved. During the design we take into account the fact that environment estimators will have limited accuracy due to noise and uncertainty. Additionally, we use a parametric model of the human operator based on measurements of an operator's hand. We show the applicability of the approach by experiments on a 1-DOF teleoperated system interacting with a virtual spring.

This paper presented an approach to create 3D object models for robotic and vision applications in a fast and inexpensive way compared to established approaches. By using the RoboEarth system for storing the created object models users have world-wide access to the data and can immediately reuse a model as soon as it was created and uploaded. The approach shows general applicability for different kinds of cameras. In this work this was shown by two example implementations for the recognition process of objects. The quality of the recognition can be verified in the video. Combined with the knowledge saved in the RoboEarth database the objects can also be properly classified.

Robots increasingly operate in dynamic environments and in order to operate safely, reliable world models are indispensable. A world model is the robot's view of the world and contains information about obstacle locations and velocities. A two level algorithm is proposed. It is of particular use for teams of cooperating robots and the algorithm is based on a multiple hypotheses filter. Each robot features a low level world model with a fast update rate which can be used for obstacle avoidance. The local world models are combined to one global view of the world that is shared between all robots and can be used for the implementation of team strategies. Labeling and tracking is added to the multiple hypotheses filter in order to reduce the sensitivity to track loss in case of temporary occlusions of objects or false measurements. The algorithm was extensively tested during the 2010 RoboCup Middle Size League world championships in Singapore, the results of which are presented.

This paper describes a new active ball handling method for the RoboCup mid-size league as used by team Tech United at Eindhoven University of Technology. A theoretical model is derived followed by the control design including a feedback controller and a feedforward controller. The proposed control design is validated with the Tech United soccer robots. The results of several tests show the effectiveness of the new ball handling design.