Visual servoing for teleoperation using a tethered UAV

IEEE Robot. Autom. Lett.

2020

Self Cite

This paper proposes a formal robot motion risk reasoning framework and develops a risk-aware path planner that minimizes the proposed risk. While robots locomoting in unstructured or confined environments face a variety of risk, existing risk only focuses on collision with obstacles. Such risk is currently only addressed in ad hoc manners. Without a formal definition, ill-supported properties, e.g. additive or Markovian, are simply assumed. Relied on an incomplete and inaccurate representation of risk, risk-aware planners use ad hoc risk functions or chance constraints to minimize risk. The former inevitably has low fidelity when modeling risk, while the latter conservatively generates feasible path within a probability bound. Using propositional logic and probability theory, the proposed motion risk reasoning framework is formal. Building upon a universe of risk elements of interest, three major risk categories, i.e. locale-, action-, and traverse-dependent, are introduced. A risk-aware planner is also developed to plan minimum risk path based on the newly proposed risk framework. Results of the risk reasoning and planning are validated in physical experiments in real-world unstructured or confined environments. With the proposed fundamental risk reasoning framework, safety of robot locomotion could be explicitly reasoned, quantified, and compared. The risk-aware planner finds safe path in terms of the newly proposed risk framework and enables more risk-aware robot behavior in unstructured or confined environments.

Section: A Robot Missionmentioning

confidence: 99%

Robot Risk-Awareness by Formal Risk Reasoning and Planning

IEEE Robot. Autom. Lett.

2020

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“…More specifically, this could be expressed as the difficulty of the action and difference between consecutive actions (turn). risk a (a 1 , a 2 , ..., a n ) = w a1 n i=1 a i + w a2 n i=2 a i − a i−1 (12) Here, a i could be viewed as the difficulty of executing a i , such as the length of the action. For example, moving in a 8-connectivity 2-D occupancy grid may have a i = 1 or a i = √ 2. a i − a i−1 is the difference between consecutive actions, such as the risk of turning or change directions.…”

Section: Risk Representationmentioning

confidence: 99%

“…In this section, we provide quantitative risk representations using a particular robot platform as example, a tethered Unmanned Aerial Vehicle (UAV), Fotokite Pro, which was used as a visual assistant to pair with a tele-operated Unmanned Ground Vehicle (UGV) for operations in afterdisaster environments, such as Fukushima Daiichi nuclear decommisioning [11], [12]. Using the formal risk definition and explicit risk representation discussed above, examples of relative likelihood of the UAV not being able to finish In the examples, the planning space is represented as a 2-D occupancy grid for simplicity.…”

Section: Quantitative Examplesmentioning

confidence: 99%

Explicit Motion Risk Representation

2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

2019

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This paper presents a formal definition and explicit representation of robot motion risk. Currently, robot motion risk has not been formally defined, but has already been used in motion and path planning. Risk is either implicitly represented as model uncertainty using probabilistic approaches, where the definition of risk is somewhat avoided, or explicitly modeled as a simple function of states, without a formal definition. In this work, we provide formal reasoning behind what risk is for robot motion and propose a formal definition of risk in terms of a sequence of motion, namely path. Mathematical approaches to represent motion risk are also presented, which is in accordance with our risk definition and properties. The definition and representation of risk provide a meaningful way to evaluate or construct robot motion or path plans. The understanding of risk is even of greater interest for the search and rescue community: the deconstructed environments cast extra risk onto the robot, since they are working under extreme conditions. A proper risk representation has the potential to reduce robot failure in the field.

“…assistant at a third-person-view to enhance situational awareness for the tele-operator of an Unmanned Ground Vehicle (UGV) [9], [10]. Tether is used to match the battery duration of the ground and aerial vehicles in the heterogeneous robot team so as to conduct extended navigation and manipulation tasks in remote environments, such as decommissioning in Fukushima Daiichi nuclear accident.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Tether-based UAV Motion Primitives

2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

2019

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This paper proposes and benchmarks two tetherbased motion primitives for tethered UAVs to execute autonomous flight with proprioception only. Tethered UAVs have been studied mainly due to power and safety considerations. Tether is either not included in the UAV motion (treated same as free-flying UAV) or only in terms of station-keeping and highspeed steady flight. However, feedback from and control over the tether configuration could be utilized as a set of navigational tools for autonomous flight, especially in GPS-denied environments and without vision-based exteroception. In this work, two tether-based motion primitives are proposed, which can enable autonomous flight of a tethered UAV. The proposed motion primitives are implemented on a physical tethered UAV for autonomous path execution with motion capture ground truth. The navigational performance is quantified and compared. The proposed motion primitives make tethered UAV a mobile and safe autonomous robot platform. The benchmarking results suggest appropriate usage of the two motion primitives for tethered UAVs with different path plans.