| 1. |
- Barbosa, Fernando S., 1992-, et al.
(författare)
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Formal Methods for Robot Motion Planning with Time and Space Constraints (Extended Abstract)
- 2021
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer Nature. ; , s. 1-14
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Konferensbidrag (refereegranskat)abstract
- Motion planning is one of the core problems in a wide range of robotic applications. We discuss the use of temporal logics to include complex objectives, constraints, and preferences in motion planning algorithms and focus on three topics: the first one addresses computational tractability of Linear Temporal Logic (LTL) motion planning in systems with uncertain non-holonomic dynamics, i.e. systems whose ability to move in space is constrained. We introduce feedback motion primitives and heuristics to guide motion planning and demonstrate its use on a rover in 2D and a fixed-wing drone in 3D. Second, we introduce combined motion planning and hybrid feedback control design in order to find and follow trajectories under Metric Interval Temporal Logic (MITL) specifications. Our solution creates a path to be tracked, a sequence of obstacle-free polytopes and time stamps, and a controller that tracks the path while staying in the polytopes. Third, we focus on motion planning with spatio-temporal preferences expressed in a fragment of Signal Temporal Logic (STL). We introduce a cost function for a of a path reflecting the satisfaction/violation of the preferences based on the notion of STL spatial and temporal robustness. We integrate the cost into anytime asymptotically optimal motion planning algorithm RRT ⋆ and we show the use of the algorithm in integration with an autonomous exploration planner on a UAV.
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| 2. |
- Barbosa, Fernando S., 1992-, et al.
(författare)
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Provably safe control of Lagrangian systems in obstacle-scattered environments
- 2020
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Ingår i: 2020 59th IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- We propose a hybrid feedback control law that guarantees both safety and asymptotic stability for a class of Lagrangian systems in environments with obstacles. Rather than performing trajectory planning and implementing a trajectory-tracking feedback control law, our approach requires a sequence of locations in the environment (a path plan) and an abstraction of the obstacle-free space. The problem of following a path plan is then interpreted as a sequence of reach-avoid problems: the system is required to consecutively reach each location of the path plan while staying within safe regions. Obstacle-free ellipsoids are used as a way of defining such safe regions, each of which encloses two consecutive locations. Feasible Control Barrier Functions (CBFs) are created directly from geometric constraints, the ellipsoids, ensuring forward-invariance, and therefore safety. Reachability to each location is guaranteed by asymptotically stabilizing Control Lyapunov Functions (CLFs). Both CBFs and CLFs are then encoded into quadratic programs (QPs) without the need of relaxation variables. Furthermore, we also propose a switching mechanism that guarantees the control law is correct and well-defined even when transitioning between QPs. Simulations show the effectiveness of the proposed approach in two complex scenarios.
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| 3. |
- Barbosa, Fernando S., 1992-, et al.
(författare)
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Risk-Aware Motion Planning in Partially Known Environments
- 2021
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Ingår i: 2021 60th IEEE conference on decision and control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 5220-5226
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Konferensbidrag (refereegranskat)abstract
- Recent trends envisage robots being deployed inareas deemed dangerous to humans, such as buildings with gasand radiation leaks. In such situations, the model of the underlying hazardous process might be unknown to the agent a priori, giving rise to the problem of planning for safe behaviour inpartially known environments. We employ Gaussian Process regression to create a probabilistic model of the hazardous process from local noisy samples. The result of this regression is then used by a risk metric, such as the Conditional Value-at-Risk, to reason about the safety at a certain state. The outcome is a risk function that can be employed in optimal motion planning problems. We demonstrate the use of the proposed function in two approaches. First is a sampling-based motion planning algorithm with an event-based trigger for online replanning. Second is an adaptation to the incremental Gaussian Process motion planner (iGPMP2), allowing it to quickly react and adapt to the environment. Both algorithms are evaluated in representative simulation scenarios, where they demonstrate the ability of avoiding high-risk areas.
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| 4. |
- Barbosa, Fernando S., 1992-, et al.
(författare)
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Risk-Aware Navigation on Smooth Approximations of Euclidean Distance Fields Among Dynamic Obstacles
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Reasoning about probabilistic outcomes on stochastic models is of essence to safety-critical systems. In this paper we focus on risk-aware collision avoidance approaches in workspaces with static and dynamic obstacles. More specifically, for Lagrangian systems operating in workspaces without perfect information about the obstacle-space. In order to avoid collision with static obstacles, we build smooth approximations of the Euclidean distance field, along with its first and second derivatives, using Gaussian Process implicit surfaces. Since the predictive distance returned by such an approximation is a normal distribution, rather than simply using its mean value, we propose a risk-aware Control Barrier function. Risk metrics provide more coherent measures than chance constraint, with the benefit of distinguishing between tail events. We prove that by using the proposed approach, the Lagrangian system is bound to a smaller, but safer (in terms of risk-awareness), subset of the obstacle-free space. Besides that, we also propose a controller for avoiding collisions with ellipsoidal dynamic obstacles. We compose all the controllers together into a nonsmooth barrier function, and design a Quadratic Program-based optimization controller. The proposed approach is a step-forward towards closer integration between mapping algorithms and feedback controllers. Numerical simulations on synthetic environments highlight the capabilities of the approach proposed.
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| 5. |
- Barbosa, Fernando S., 1992-
(författare)
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Towards Safer and Risk-aware Motion Planning and Control for Robotic Systems
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Safety and risk-awareness are important properties for robotic systems, be it for protecting them from potentially dangerous internal states, or for avoiding collisions with obstacles and environmental hazards in disaster scenarios. Ensuring safety may be the role of more than one algorithmic layer in a system, each with varying assumptions and guarantees. This thesis investigates how to provide safety and risk-awareness in a robotic system by leveraging temporal logics, motion planning algorithms, and control theory.Traditional control theory approaches interpret the collision avoidance safety task as a `stay-away' task; obstacles are abstracted as collections of geometric shapes, and controllers are designed to avoid each shape individually. We propose interpreting the collision avoidance problem as a `stay-within' task: the obstacle-free space is abstracted into safe regions. We propose control laws based on Control Barrier functions that guarantee that the system remains within such safe regions throughout its mission. Our results demonstrate that our controller indirectly avoids obstacles while providing the system the freedom to move within the safe regions, without the necessity to plan and track a safe trajectory. Furthermore, by extending our idea with Metric Interval Temporal Logic, we are able to consider missions with explicit time bounds. Temporal logics are often used to define hard constraints on motion plans for robotic systems. However, some missions may require the system to violate constraints to make progress. Therefore, we propose to soften the hard constraints when necessary. Such soft constraints, here coined as spatial preferences, are used to account for relations between the system and the environment, such as distance from obstacles. The proposed minimally-violating motion planning algorithm attempts to find trajectories that satisfy the spatial preferences as much as possible, but violate them when needed. We demonstrate the use of spatial preferences on 3D exploration scenarios with Unmanned Aerial Vehicles, where we provide safer trajectories to the system while improving exploration efficiency. In the last part of the thesis, we address safety in scenarios where a precise model of the environment is not available. In such scenarios, the system is required to fulfil the mission while minimizing risk, considering the imprecise model. We leverage Gaussian Processes to build approximate models of the environment, and use their posterior distributions in a risk metric. This risk metric allows us to consider less likely but possible events along the missions. To this end, we propose an online risk-aware motion planning approach, and validate it on disaster scenarios, where exposure to the unmodeled hazards might damage the system. Moreover, we explore risk-awareness between the control and mapping layers, by considering smooth approximations of Euclidean Distance Fields.Our results indicate that our algorithms provide robotic systems with i) provably-safe controllers, ii) soft safety constraints, and iii) risk-awareness in unmodeled environments. These three properties contribute to safer and risk-aware robotic systems in the real world.
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| 6. |
- Grover, Kush, et al.
(författare)
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Semantic Abstraction-Guided Motion Planningfor scLTL Missions in Unknown Environments
- 2021
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Ingår i: Robotics: Science and Systems. - : RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION.
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Konferensbidrag (refereegranskat)abstract
- Complex mission specifications can be often specifiedthrough temporal logics, such as Linear Temporal Logic and itssyntactically co-safe fragment, scLTL. Finding trajectories thatsatisfy such specifications becomes hard if the robot is to fulfilthe mission in an initially unknown environment, where neitherlocations of regions or objects of interest in the environmentnor the obstacle space are known a priori. We propose an algorithmthat, while exploring the environment, learns importantsemantic dependencies in the form of a semantic abstraction,and uses it to bias the growth of an Rapidly-exploring randomgraph towards faster mission completion. Our approach leadsto finding trajectories that are much shorter than those foundby the sequential approach, which first explores and then plans.Simulations comparing our solution to the sequential approach,carried out in 100 randomized office-like environments, showmore than 50% reduction in the trajectory length.
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| 7. |
- Tajvar, Pouria, 1991-, et al.
(författare)
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Safe Motion Planning for an Uncertain Non-Holonomic System with Temporal Logic Specification
- 2020
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Ingår i: Proceedings 16th IEEE International Conference on Automation Science and Engineering, CASE 2020. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 349-354
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Konferensbidrag (refereegranskat)abstract
- We propose a sampling-based motion planning algorithm for systems with complex dynamics and temporal logic specifications allowing to tackle sophisticated missions. By complex dynamics we refer to non-holonomy and disturbance that prevent implementation of an exact steer function. We instead construct a set of feedback motion primitives guaranteeing bounded state uncertainty (and thus safety) allowing the system to follow an arbitrarily long trajectory without replanning. The motion primitives allow to use A*-based algorithm to provably accomplish the temporal logic mission. We propose a heuristics for the A*-based algorithm via construction of backward trees. We illustrate the approach on several case studies, including simulations of a rover and fixed wing drone. We further show that construction of backward trees allows for faster re-planning compared to the state-of-the-art.
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