471 research outputs found
Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving
Automated vehicles require efficient and safe planning to maneuver in
uncertain environments. Largely this uncertainty is caused by other traffic
participants, e.g., surrounding vehicles. Future motion of surrounding vehicles
is often difficult to predict. Whereas robust control approaches achieve safe,
yet conservative motion planning for automated vehicles, Stochastic Model
Predictive Control (SMPC) provides efficient planning in the presence of
uncertainty. Probabilistic constraints are applied to ensure that the maximal
risk remains below a predefined level. However, safety cannot be ensured as
probabilistic constraints may be violated, which is not acceptable for
automated vehicles. Here, we propose an efficient trajectory planning framework
with safety guarantees for automated vehicles. SMPC is applied to obtain
efficient vehicle trajectories for a finite horizon. Based on the first
optimized SMPC input, a guaranteed safe backup trajectory is planned, using
reachable sets. The SMPC input is only applied to the vehicle if a safe backup
solution can be found. If no new safe backup solution can be found, the
previously calculated, still valid safe backup solution is applied instead of
the SMPC solution. Recursive feasibility of the safe SMPC algorithm is proved.
Highway simulations show the effectiveness of the proposed method regarding
performance and safety
Reducing Safety Interventions in Provably Safe Reinforcement Learning
Deep Reinforcement Learning (RL) has shown promise in addressing complex
robotic challenges. In real-world applications, RL is often accompanied by
failsafe controllers as a last resort to avoid catastrophic events. While
necessary for safety, these interventions can result in undesirable behaviors,
such as abrupt braking or aggressive steering. This paper proposes two safety
intervention reduction methods: proactive replacement and proactive projection,
which change the action of the agent if it leads to a potential failsafe
intervention. These approaches are compared to state-of-the-art constrained RL
on the OpenAI safety gym benchmark and a human-robot collaboration task. Our
study demonstrates that the combination of our method with provably safe RL
leads to high-performing policies with zero safety violations and a low number
of failsafe interventions. Our versatile method can be applied to a wide range
of real-world robotic tasks, while effectively improving safety without
sacrificing task performance.Comment: 8 pages, 6 figure
Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments
This research aims at developing path and motion planning algorithms for a
tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated
primary robot in unstructured or confined environments. The emerging state of
the practice for nuclear operations, bomb squad, disaster robots, and other
domains with novel tasks or highly occluded environments is to use two robots,
a primary and a secondary that acts as a visual assistant to overcome the
perceptual limitations of the sensors by providing an external viewpoint.
However, the benefits of using an assistant have been limited for at least
three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground
robot assistants are considered, ignoring the rapid evolution of small unmanned
aerial systems for indoor flying, (3) introducing a whole crew for the second
teleoperated robot is not cost effective, may introduce further teamwork
demands, and therefore could lead to miscommunication. This dissertation
proposes to use an autonomous tethered aerial visual assistant to replace the
secondary robot and its operating crew. Along with a pre-established theory of
viewpoint quality based on affordances, this dissertation aims at defining and
representing robot motion risk in unstructured or confined environments. Based
on those theories, a novel high level path planning algorithm is developed to
enable risk-aware planning, which balances the tradeoff between viewpoint
quality and motion risk in order to provide safe and trustworthy visual
assistance flight. The planned flight trajectory is then realized on a tethered
UAV platform. The perception and actuation are tailored to fit the tethered
agent in the form of a low level motion suite, including a novel tether-based
localization model with negligible computational overhead, motion primitives
for the tethered airframe based on position and velocity control, and two
differentComment: Ph.D Dissertatio
Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments
This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or confined environments. The emerging state of the practice for nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the sensors by providing an external viewpoint. However, the benefits of using an assistant have been limited for at least three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground robot assistants are considered, ignoring the rapid evolution of small unmanned aerial systems for indoor flying, (3) introducing a whole crew for the second teleoperated robot is not cost effective, may introduce further teamwork demands, and therefore could lead to miscommunication. This dissertation proposes to use an autonomous tethered aerial visual assistant to replace the secondary robot and its operating crew. Along with a pre-established theory of viewpoint quality based on affordances, this dissertation aims at defining and representing robot motion risk in unstructured or confined environments. Based on those theories, a novel high level path planning algorithm is developed to enable risk-aware planning, which balances the tradeoff between viewpoint quality and motion risk in order to provide safe and trustworthy visual assistance flight.
The planned flight trajectory is then realized on a tethered UAV platform. The perception and actuation are tailored to fit the tethered agent in the form of a low level motion suite, including a novel tether-based localization model with negligible computational overhead, motion primitives for the tethered airframe based on position and velocity control, and two different approaches to negotiate tether with complex obstacle-occupied environments. The proposed research provides a formal reasoning of motion risk in unstructured or confined spaces, contributes to the field of risk-aware planning with a versatile planner, and opens up a new regime of indoor UAV navigation: tethered indoor flight to ensure battery duration and failsafe in case of vehicle malfunction. It is expected to increase teleoperation productivity and reduce costly errors in scenarios such as safe decommissioning and nuclear operations in the Fukushima Daiichi facility
Dynamic obstacles avoidance algorithms for unmanned ground vehicles
En las últimas décadas, los vehículos terrestres no tripulados (UGVs) están siendo cada vez más empleados como robots de servicios. A diferencia de los robots industriales, situados en posiciones fijas y controladas, estos han de trabajar en entornos dinámicos, compartiendo su espacio con otros vehículos y personas. Los UGVs han de ser capaces de desplazarse sin colisionar con ningún obstáculo, de tal manera que puedan asegurar tanto su integridad como la del entorno.
En el estado del arte encontramos algoritmos de navegación autónoma diseñados para UGVs que son capaces de planificar rutas de forma segura con objetos estáticos y trabajando en entornos parcialmente controlados. Sin embargo, cuando estos entornos son dinámicos, se planifican rutas más peligrosas y que a menudo requieren de un mayor consumo de energía y recursos, e incluso pueden llegar a bloquear el UGV en un mínimo local.
En esta tesis, la adaptación de algunos algoritmos disponibles en el estado del arte para trabajar en entornos dinámicos han sido planteados. Estos algoritmos incluyen información temporal tales como los basados en arcos de curvatura (PCVM y DCVM) y los basados en ventanas dinámicas (DW4DO y DW4DOT). Además, se ha propuesto un planificador global basado en Lattice State Planner (DLP) que puede resolver situaciones donde los evitadores de obstáculos reactivos no funcionan.
Estos algoritmos han sido validados tanto en simulación como en entornos reales, utilizando distintas plataformas robóticas, entre las que se incluye un robot asistente (RoboShop) diseñado y construido en el marco de esta tesis
Time-Optimal Path Tracking with ISO Safety Guarantees
One way of ensuring operator's safety during human-robot collaboration is
through Speed and Separation Monitoring (SSM), as defined in ISO standard
ISO/TS 15066. In general, it is impossible to avoid all human-robot collisions:
consider for instance the case when the robot does not move at all, a human
operator can still collide with it by hitting it of her own voluntary motion.
In the SSM framework, it is possible however to minimize harm by requiring
this: \emph{if} a collision ever occurs, then the robot must be in a
\emph{stationary state} (all links have zero velocity) at the time instant of
the collision. In this paper, we propose a time-optimal control policy based on
Time-Optimal Path Parameterization (TOPP) to guarantee such a behavior.
Specifically, we show that: for any robot motion that is strictly faster than
the motion recommended by our policy, there exists a human motion that results
in a collision with the robot in a non-stationary state. Correlatively, we
show, in simulation, that our policy is strictly less conservative than
state-of-the-art safe robot control methods. Additionally, we propose a
parallelization method to reduce the computation time of our pre-computation
phase (down to 0.5 sec, practically), which enables the whole pipeline
(including the pre-computation) to be executed at runtime, nearly in real-time.
Finally, we demonstrate the application of our method in a scenario:
time-optimal, safe control of a 6-dof industrial robot.Comment: 8 pages, submitted to IROS 202
Representing the Unknown - Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation
Even though motion planning for automated vehicles has been extensively
discussed for more than two decades, it is still a highly active field of
research with a variety of different approaches having been published in the
recent years. When considering the market introduction of SAE Level 3+
vehicles, the topic of motion planning will most likely be subject to even more
detailed discussions between safety and user acceptance. This paper shall
discuss parameters of the motion planning problem and requirements to an
environment model. The focus is put on the representation of different types of
uncertainty at the example of sensor occlusion, arguing the importance of a
well-defined interface between decision making and trajectory generation
Autonomous task-based grasping for mobile manipulators
A fully integrated grasping system for a mobile manipulator to grasp an unknown object of interest (OI) in an unknown environment is presented. The system autonomously scans its environment, models the OI, plans and executes a grasp, while taking into account base pose uncertainty and obstacles in its way to reach the object. Due to inherent line of sight limitations in sensing, a single scan of the OI often does not reveal enough information to complete grasp analysis; as a result, our system autonomously builds a model of an object via multiple scans from different locations until a grasp can be performed. A volumetric next-best-view (NBV) algorithm is used to model an arbitrary object and terminates modelling when grasp poses are discovered on a partially observed object. Two key sets of experiments are presented: i) modelling and registration error in the OI point cloud model is reduced by selecting viewpoints with more scan overlap, and ii) model construction and grasps are successfully achieved while experiencing base pose uncertainty. A generalized algorithm is presented to discover grasp pose solutions for multiple grasp types for a multi-fingered mechanical gripper using sensed point clouds. The algorithm introduces two key ideas: 1) a histogram of finger contact normals is used to represent a grasp “shape” to guide a gripper orientation search in a histogram of object(s) surface normals, and 2) voxel grid representations of gripper and object(s) are cross-correlated to match finger contact points, i.e. grasp “size”, to discover a grasp pose. Constraints, such as collisions with neighbouring objects, are incorporated in the cross-correlation computation. Simulations and preliminary experiments show that 1) grasp poses for three grasp types are found in near real-time, 2) grasp pose solutions are consistent with respect to voxel resolution changes for both partial and complete point cloud scans, 3) a planned grasp pose is executed with a mechanical gripper, and 4) grasp overlap is presented as a feature to identify regions on a partial object model ideal for object transfer or securing an object
Enhanced online programming for industrial robots
The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal
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