Rt-Rrt*

Naderi, Kourosh; Rajamäki, Joose; Hämäläinen, Perttu

doi:10.1145/2822013.2822036

Cited by 117 publications

(36 citation statements)

References 7 publications

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“…The vehicles can automatically swap in order to continue tactics when vehicle battery levels become too low (Diehl and Adams, 2022). Once a tactic is assigned, the vehicles conduct on-board real-time navigation planning using extensions to the real-time, rapidly exploring random tree star (RT-RRT*) algorithm (Naderi et al, 2015). The RT-RRT* algorithm incorporates randomness when searching for potential paths, resulting in vehicles identifying different paths to achieve the swarm's mission objectives.…”

Section: Ccast Swarm System Architecturementioning

confidence: 99%

Congestion Analysis for the DARPA OFFSET CCAST Swarm

Brown¹,

Adams²

2023

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The Defense Advanced Research Projects Agency (DARPA) OFFensive Swam-Enabled Tactics program’s goal of launching 250 unmanned aerial and ground vehicles from a limited sized launch zone was a daunting challenge. The swarm’s aerial vehicles were primarily multirotor platforms, which can efficiently be launched en masse. Each field exercise expected the deployment of an even larger swarm. While the launch zone’s spatial area increased with each field exercise, the relative space for each vehicle was not necessarily increased, considering the increasing size of the swarm and the vehicles’ associated GPS error; however, safe mission deployment and execution were expected. At the same time, achieving the mission goals required maximizing the efficiency of the swarm’s performance by reducing congestion that blocked vehicles from completing tactic assignments. Congestion analysis conducted before the final field exercise focused on adjusting various constraints to optimize the swarm’s deployment without reducing safety. During the field exercise, data was collected that permitted analyzing the number and durations of individual vehicle blockages’ impact on the resulting congestion. After the field exercise, additional analyses used the mission plan to validate the use of simulation for analyzing congestion.

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Section: Ccast Swarm System Architecturementioning

confidence: 99%

Congestion Analysis for the DARPA OFFSET CCAST Swarm

Brown¹,

Adams²

2023

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“…The remaining nodes S new \S added are included in graph G, and they can be added to the tree in the future by rewiring. The rewiring process is similar to that in [20] except for the hybrid state space and constraints on transition relations. Note that not only tree nodes V T but also isolated vertices V iso can be rewired.…”

Section: B Initial Plan Computationmentioning

confidence: 99%

“…During the execution of the plan, we keep the traveled edges alive by moving the root along with the robot motion. Instead of setting the next un-visited node as root like in [18], [20], we transform the classical tree into a dual-root tree during the robot movement. Fig.…”

Section: Robot State Updatementioning

confidence: 99%

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Cloud-based Robot Path Planning in Dynamic Environments

Chen

Wang

Gao

et al. 2021

2021 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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Replanning in temporal logic tasks is extremely difficult during the online execution of robots. This study introduces an effective path planner that computes solutions for temporal logic goals and instantly adapts to non-static and partially unknown environments. Given prior knowledge and a task specification, the planner first identifies an initial feasible solution by growing a sampling-based search tree. While carrying out the computed plan, the robot maintains a solution library to continuously enhance the unfinished part of the plan and store backup plans. The planner updates existing plans when meeting unexpected obstacles or recognizing flaws in prior knowledge. Upon a high-level path is obtained, a trajectory generator tracks the path by dividing it into segments of motion primitives. Our planner is integrated into an autonomous mobile robot system, further deployed on a multicopter with limited onboard processing power. In simulation and realworld experiments, our planner is demonstrated to swiftly and effectively adjust to environmental uncertainties.

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“…The environment was designed carefully in such a way that it adds the common challenges AI pathfinders face in a dynamic environment. A dynamic RRT* variant RT-RRT* is chosen as it provides the flexibility to add upon different patrolling events [1]. The algorithm derives from RRT* and Informed RRT* variants.…”

Section: B Rapidly Exploring Random Trees (Rrt)mentioning

confidence: 99%

Patrolling AI Systems in Video Games

Balapriya*,

Srinivasan

2019

IJRTE

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Artificial Intelligence is a common element in digital video games and it is one of the most essential component in modern games. Modern games are populated with Non-Player Characters (AI Characters) that performs different activities. Realism is dominating in games and AI behavio rs are expected to be more realistic in games. Games that has poor unrealistic AI elements are facing heavy criticism from the player bases. Further, modern games are highly dynamic. Classical games had static environment with less or no changes in it. Such environments made implementation of AI easy and simple. In modern games, Progressive terrain generation and other such content generation methods increases the complexities of building an efficient AI for games that has many changes in real time. One of the most common AI in games is Patrolling AI especially in Shooter and Adventure Games. Patroling AI involves path finding and obstacle attack or defense. RRT algorithm and its variants are highly successful Probabilistic Determination AI that produced effective results in real time robotic movement. In order to build efficient Patrolling AI for games, a real time RRT* variant called RT-RRT* algorithm was employed. The algorithm is flexible enough to add various behaviors in addition to path finding which makes it more suitable for games. The algorithm takes samples from the environment and construct the efficient path. Also the algorithm inspect the environment in run time to ensure that no moving obstacle blocks the path. In such case, it rewires and create a new path. In order to manage the dynamic obstacles, a Real Time Obstacle Handling Algorithm was designed and employed in a dynamic game environment. The algorithms inputs the obstacles types and parameters. On identifying the obstacle approaching the AI in terrain, it tells the agent to perform the needed actions. The simulations was carried out using Unity Game Engine. The model proposed helped to create efficient patrolling AI that handle two major aspects of patrolling which is Path finding and Obstacle handling. The model will be highly suitable for dynamic game environments with lots of uncertainty and emergence.

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Rt-Rrt*

Cited by 117 publications

References 7 publications

Congestion Analysis for the DARPA OFFSET CCAST Swarm

Congestion Analysis for the DARPA OFFSET CCAST Swarm

Cloud-based Robot Path Planning in Dynamic Environments

Patrolling AI Systems in Video Games

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