Optimizing Evasive Strategies for an Evader with Imperfect Vision Capacity

Di, Kai; Yang, Shaofu; Wang, Wanyuan; Yan, Fuhan; Xing, Haokun; Jiang, Jiuchuan; Jiang, Yichuan

doi:10.1007/s10846-019-00996-1

Cited by 5 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Papers in the robotic literature that consider the presence of risk factors include [14,22,47,72,73], and they are primarily concerned with presenting algorithms and methods for risk avoidance. These studies focus on the path planning problem for an individual robot to bypass and avoid risks.…”

Section: Risk Factors In Environment: New Challenges For the Robot Fo...mentioning

confidence: 99%

A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments

Zhou

Yan

et al. 2022

ACM Trans. Intell. Syst. Technol.

Self Cite

View full text Add to dashboard Cite

As an essential problem in robotics, foraging means that robots collect objects from a given environment and return them to a specified location. On many occasions, robots are required to perform foraging tasks in adversarial environments, such as battlefield rescue, where potential adversaries may damage robots with a certain probability. The longer an individual robot moves through adversarial environments, the higher the probability of being damaged by adversaries. The robot system can gain utility only when the robot brings carried objects back to a predetermined home station. Such a risk of being damaged makes returning home at different locations potentially relevant to the expected utility produced by the robot. Thus, the individual robot faces a dilemma when it responds to the potential risks in adversarial environments: whether to return the carried resources home or continue foraging tasks. In this paper, two fundamental environment settings are discussed, homogeneous cases and heterogeneous cases. The former is analyzed as having both the optimal substructure property and the non-aftereffect property. Then, we present a dynamic programming algorithm that can find an optimal solution with polynomial time complexity. For the latter, it is proven that finding an optimal solution is \mathcal {NP} -hard. We then propose a heuristic algorithm: a division hierarchical path planning algorithm that is based on the idea of dividing the foraging routes generated initially into a certain number of subroutes to dilute risks. Finally, these algorithms are extensively evaluated in simulations, concluding that in adversarial environments, they can significantly improve the productivity of an individual robot before it is damaged.

show abstract

Section: Risk Factors In Environment: New Challenges For the Robot Fo...mentioning

confidence: 99%

A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments

Zhou

Yan

et al. 2022

ACM Trans. Intell. Syst. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…(2) Another research direction of pursuit-evasion game is sensor limitation. Most of these studies focus on how to maximize the efficiency of search [37] or field of view [38]. Generally, these methods only provide the strategy of pursuer but rarely introduce the strategy of evader, so it is difficult to present a complete pursuit-evasion game in simulation.…”

Section: Simulation and Analysismentioning

confidence: 99%

Collaborative Pursuit-Evasion Strategy of UAV/UGV Heterogeneous System in Complex Three-Dimensional Polygonal Environment

2020

View full text Add to dashboard Cite

The UAV/UGV heterogeneous system combines the air superiority of UAV (unmanned aerial vehicle) and the ground superiority of UGV (unmanned ground vehicle). The system can complete a series of complex tasks and one of them is pursuit-evasion decision, so a collaborative strategy of UAV/UGV heterogeneous system is proposed to derive a pursuit-evasion game in complex three-dimensional (3D) polygonal environment, which is large enough but with boundary. Firstly, the system and task hypothesis are introduced. Then, an improved boundary value problem (BVP) is used to unify the terrain data of decision and path planning. Under the condition that the evader knows the position of collaborative pursuers at any time but pursuers just have a line-of-sight view, a worst case is analyzed and the strategy between the evader and pursuers is studied. According to the state of evader, the strategy of collaborative pursuers is discussed in three situations: evader is in the visual field of pursuers, evader just disappears from the visual field of pursuers, and the position of evader is completely unknown to pursuers. The simulation results show that the strategy does not guarantee that the pursuers will win the game in complex 3D polygonal environment, but it is optimal in the worst case.

show abstract

“…In these studies, pursuing platforms are assumed to be equipped with error-free identification and measurement systems that allow them to acquire precise information about the position, velocity, and other characteristics of evaders and cooperators [6,23]. However, sensors and other equipment configured in an unmanned system encounter positioning, sensing, and actuator error in reality [24,25]. These errors cause the environment to become uncertain, thereby affecting the strategies of the pursuers and evaders and making their performance worse.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Wan

Zhai

et al. 2021

Entropy

View full text Add to dashboard Cite

A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit–evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

show abstract

Optimizing Evasive Strategies for an Evader with Imperfect Vision Capacity

Cited by 5 publications

References 30 publications

A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments

A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments

Collaborative Pursuit-Evasion Strategy of UAV/UGV Heterogeneous System in Complex Three-Dimensional Polygonal Environment

An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Contact Info

Product

Resources

About