Stealthy Path Planning Using Navigation Meshes

Mendonça, Matheus R. F.; Bernardino, Heder S.; Neto, Raul Fonseca

doi:10.1109/bracis.2015.49

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…In the field of adversarial pathfinding, stealth games like Tom Clancy's Splinter Cell (Ubisoft, 2002), Mark of the Ninja (Microsoft Studios, 2012), andMetal Gear Solid V (Konami, 2015) encourage players to follow covert paths and utilize light and sound to avoid enemy detection during navigation. Mendonc ¸a, Bernardino, and Neto (2015), proposed a method to construct such covert paths in realtime using navigation meshes. Other researchers have attempted to strike a balance between path suboptimality and path safety in adversarial navigation.…”

Section: Related Workmentioning

confidence: 99%

Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner

Ray,

Sturtevant

2023

AIIDE

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Real-time strategy games require players to respond to short-term challenges (micromanagement) and long-term objectives (macromanagement) simultaneously to win. However, many players excel at one of these skills but not both. This research is motivated by the question of whether the burden of micromanagement can be reduced on human players through delegation of responsibility to autonomous agents. In particular, this research proposes an adversarial navigation architecture that enables units to autonomously navigate through places densely populated with enemies by learning to micromanage itself. Our approach models the adversarial pathfinding problem as a Markov Decision Process (MDP) and trains an agent with reinforcement learning on this MDP. We observed that our approach resulted in the agent taking less damage from adversaries while travelling shorter paths, compared to previous approaches for adversarial navigation. Our approach is also efficient in memory use and computation time. Interestingly, the agent using the proposed approach also outperformed baseline approaches while navigating through environments that are significantly different from the training environments. Furthermore, when the game design is modified, the agent discovers effective alternate strategies considering the updated design without any changes in the learning framework. This property is particularly useful because in game development the design of a game is often updated iteratively.

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Section: Related Workmentioning

confidence: 99%

Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner

Ray,

Sturtevant

2023

AIIDE

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“…eir focus is on stealth path nding in games and applying that to robotics. Like RRT, the methods they propose do not necessarily nd the shortest path [8,17]. Instead, they try to nd the path where the agent spends most of the time in cover.…”

Section: Applications Of Path Ndingmentioning

confidence: 99%

“…Mendona et al [17] look at path nding both in robotics and digital games. eir focus is on stealth path nding in games and applying that to robotics.…”

Section: Applications Of Path Ndingmentioning

confidence: 99%

The effect of visualising NPC pathfinding on player exploration

Kay

Powley

2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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Foregrounding AI behaviour through visualisation is an underexplored but potentially powerful technique for in uencing player behaviour, particularly in terms of sparking the player's curiosity and encouraging them to spend more time exploring the game environment. is paper looks at visualising various methods of enemy NPC path nding in a 3D Metroidvania game. We demonstrate a number of cases where player exploration and player deaths are a ected by either a change in path nding method or by visualising the path nding process. is suggests that the choice of path nding algorithm and the option for this to a ect the visuals of the game are useful considerations for game designers.

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“…According to Figures 7,8, and 9, the drift has been corrected as the estimated trajectories are more compliant with the building structure and with footpaths in outdoor space. This has been achieved thanks to the proposed particle filter and to an increased conformity with pedestrian motion, demonstrating that the positioning accuracy for the texting scenario has been significantly enhanced using our approach.…”

Section: Trajectory Analysismentioning

confidence: 99%

“…A navigation mesh (NavMesh) is a set of several 2D or 3D polygons reachable by some given user [8]. This structure can be obtained automatically using GIS software.…”

Section: Overview Of Navigation Mesh Generationmentioning

confidence: 99%

Pedestrian Dead Reckoning Navigation with the Help of A⁎-Based Routing Graphs in Large Unconstrained Spaces

Alaoui¹,

Betaille²,

Renaudin³

2017

Wireless Communications and Mobile Computing

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An A* -based routing graph is proposed to assist PDR indoor and outdoor navigation with handheld devices. Measurements are provided by inertial and magnetic sensors together with a GNSS receiver. The novelty of this work lies in providing a realistic motion support that mitigates the absence of obstacles and enables the calibration of the PDR model even in large spaces where GNSS signal is unavailable. This motion support is exploited for both predicting positions and updating them using a particle filter. The navigation network is used to correct for the gyro drift, to adjust the step length model and to assess heading misalignment between the pedestrian's walking direction and the pointing direction of the handheld device. Several datasets have been tested and results show that the proposed model ensures a seamless transition between outdoor and indoor environments and improves the positioning accuracy. The drift is almost cancelled thanks to heading correction in contrast with a drift of 8% for the nonaided PDR approach. The mean error of filtered positions ranges from 3 to 5 m.

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Stealthy Path Planning Using Navigation Meshes

Cited by 7 publications

References 15 publications

Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner

Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner

The effect of visualising NPC pathfinding on player exploration

Pedestrian Dead Reckoning Navigation with the Help of A⁎-Based Routing Graphs in Large Unconstrained Spaces

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