Multi-Heuristic A*

Quadrotors with large thrust-to-weight ratios are able to track aggressive trajectories with sharp turns and high accelerations. In this work, we develop a search-based trajectory planning approach that exploits the quadrotor maneuverability to generate sequences of motion primitives in cluttered environments. We model the quadrotor body as an ellipsoid and compute its flight attitude along trajectories in order to check for collisions against obstacles. The ellipsoid model allows the quadrotor to pass through gaps that are smaller than its diameter with non-zero pitch or roll angles. Without any prior information about the location of gaps and associated attitude constraints, our algorithm is able to find a safe and optimal trajectory that guides the robot to its goal as fast as possible. To accelerate planning, we first perform a lower dimensional search and use it as a heuristic to guide the generation of a final dynamically feasible trajectory. We analyze critical discretization parameters of motion primitive planning and demonstrate the feasibility of the generated trajectories in various simulations and real-world experiments.

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“…By adding control in the z-axis, we are able to plan in 3-D space and relax the constraint in (25) as follows:…”

Section: B 3-d Planningmentioning

confidence: 99%

Search-Based Motion Planning for Aggressive Flight in SE(3)

Liu

Mohta

Atanasov

et al. 2018

IEEE Robot. Autom. Lett.

130

111

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“…However, in this case the communication protocol will become more complicated and extra processing is needed at each agent side to optimize the global path with respect to the presence of other agents. Finally, the work will be extended with different classes of agents (Panagou, 2017), and with multiple heuristics of A * (Aine et al, 2016) to allow more thoroughly investigation of the dependability factors, and constraints on the path planning problems. The intention is also to validate the algorithm using robots and humans in outdoor settings, that resemble the qualities of construction sites.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning

2018

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Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e., safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta* algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta* algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.

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“…The main motivation is that different types of planners can exploit specific properties and guarantees of the cost functions. For example, sampling‐based planners (Elbanhawi & Simic, ; Karaman & Frazzoli, ; Kuffner & LaValle, ) or search‐based planners (Aine, Swaminathan, Narayanan, Hwang, & Likhachev, ; LaValle, ) should ideally use fast‐to‐compute costs so that many different states can be explored during search in high‐dimensional state spaces. Other categories of planners, based on trajectory optimization (Ratliff, Zucker, Bagnell, & Srinivasa, ; Schulman et al, ), usually require cost functions to be differentiable to the first or higher orders.…”

Section: Related Workmentioning

confidence: 99%

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Bonatti

Wang

et al. 2020

Journal of Field Robotics

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Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real-time. Existing approaches do not address all aspects of this problem; they either require high-precision motion-capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (a) vision-based target estimation; (b) 3D signed-distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time-horizon camera motion; and (d) learning-based artistic shot selection.We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in-depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU. K E Y W O R D S aerial robotics, cinematography, computer vision, learning, mapping, motion planning Within the filming context, this cost function measures jerkiness of motion, safety, environmental occlusion of the actor, and shot quality (artistic quality of viewpoints). This cost function depends on the environment and , and on the actor forecast ξ a , all of which are sensed on-the-fly. The changing nature of the environment and ξ a demands replanning at a high frequency. Here we briefly touch upon the four components of the cost function J(ξ q ) (refer to Section 7 for details and mathematical expressions): Smoothness J smooth (ξ q ): Penalizes jerky motions that may lead to camera blur and unstable flight; Safety J obs (ξ q , ): Penalizes proximity to obstacles that are unsafe for the UAV; Occlusion J occ (ξ q , ξ a , ): Penalizes occlusion of the actor by obstacles in the environment; Shot quality J shot (ξ q , ξ a , Ω art ): Penalizes poor viewpoint angles and scales that deviate from the desired artistic guidelines, given by the set of parameters Ω art . In its simplest form, we can express J(ξ q ) as a linear composition of each individual cost, weighted by scalars λ i . The objective is to * = ( ) ()={ } J J J J J J xyz J J J J J J x y z J occ + J obs 99.4 ± 2.2 94.2 ± 7.3 86.9 ± 9.3 J obs 98.8 ± 3.0 87.1 ± 8.5 75.3 ± 11.8 Avg. dist. to ξ shot (m) J occ + J obs 0.4 ± 0.4 6.2 ± 11.2 10.7 ± 13.2 J obs 0.05 ± 0.1 0.3 ± 0.2 0.5 ± 0...

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Multi-Heuristic A*

Cited by 131 publications

References 25 publications

Search-Based Motion Planning for Aggressive Flight in SE(3)

Search-Based Motion Planning for Aggressive Flight in SE(3)

Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

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