A discrete optimisation approach for target path planning whilst evading
sensors
- URL: http://arxiv.org/abs/2106.08826v1
- Date: Wed, 16 Jun 2021 14:42:52 GMT
- Title: A discrete optimisation approach for target path planning whilst evading
sensors
- Authors: J.E. Beasley
- Abstract summary: We deal with a practical problem that arises in military situations.
The problem is to plan a path for one (or more) agents to reach a target without being detected by enemy sensors.
Agent actions are path dependent and time limited.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper we deal with a practical problem that arises in military
situations. The problem is to plan a path for one (or more) agents to reach a
target without being detected by enemy sensors.
Agents are not passive, rather they can (within limits) initiate actions
which aid evasion, namely knockout (completely disable sensors) and confusion
(reduce sensor detection probabilities). Agent actions are path dependent and
time limited. Here by path dependent we mean that an agent needs to be
sufficiently close to a sensor to knock it out. By time limited we mean that a
limit is imposed on how long a sensor is knocked out or confused before it
reverts back to its original operating state.
The approach adopted breaks the continuous space in which agents move into a
discrete space. This enables the problem to be represented (formulated)
mathematically as a zero-one integer program with linear constraints. The
advantage of representing the problem in this manner is that powerful
commercial software optimisation packages exist to solve the problem to proven
global optimality.
Computational results are presented for a number of randomly generated test
problems.
Related papers
- Sensor Deprivation Attacks for Stealthy UAV Manipulation [51.9034385791934]
Unmanned Aerial Vehicles autonomously perform tasks with the use of state-of-the-art control algorithms.
In this work, we propose a multi-part.
Sensor Deprivation Attacks (SDAs), aiming to stealthily impact.
process control via sensor reconfiguration.
arXiv Detail & Related papers (2024-10-14T23:03:58Z) - Multi-Armed Bandits with Abstention [62.749500564313834]
We introduce a novel extension of the canonical multi-armed bandit problem that incorporates an additional strategic element: abstention.
In this enhanced framework, the agent is not only tasked with selecting an arm at each time step, but also has the option to abstain from accepting the instantaneous reward before observing it.
arXiv Detail & Related papers (2024-02-23T06:27:12Z) - Model Checking for Closed-Loop Robot Reactive Planning [0.0]
We show how model checking can be used to create multistep plans for a differential drive wheeled robot so that it can avoid immediate danger.
Using a small, purpose built model checking algorithm in situ we generate plans in real-time in a way that reflects the egocentric reactive response of simple biological agents.
arXiv Detail & Related papers (2023-11-16T11:02:29Z) - Unsupervised Adaptation from Repeated Traversals for Autonomous Driving [54.59577283226982]
Self-driving cars must generalize to the end-user's environment to operate reliably.
One potential solution is to leverage unlabeled data collected from the end-users' environments.
There is no reliable signal in the target domain to supervise the adaptation process.
We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain.
arXiv Detail & Related papers (2023-03-27T15:07:55Z) - On Avoiding Power-Seeking by Artificial Intelligence [93.9264437334683]
We do not know how to align a very intelligent AI agent's behavior with human interests.
I investigate whether we can build smart AI agents which have limited impact on the world, and which do not autonomously seek power.
arXiv Detail & Related papers (2022-06-23T16:56:21Z) - Prioritized SIPP for Multi-Agent Path Finding With Kinematic Constraints [0.0]
Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence.
We present a method that mitigates this issue to a certain extent.
arXiv Detail & Related papers (2021-08-11T10:42:11Z) - Bandit Quickest Changepoint Detection [55.855465482260165]
Continuous monitoring of every sensor can be expensive due to resource constraints.
We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions.
We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
arXiv Detail & Related papers (2021-07-22T07:25:35Z) - PRANK: motion Prediction based on RANKing [4.4861975043227345]
Predicting the motion of agents is one of the most critical problems in the autonomous driving domain.
We introduce the PRANK method, which produces the conditional distribution of agent's trajectories plausible in the given scene.
We evaluate PRANK on the in-house and Argoverse datasets, where it shows competitive results.
arXiv Detail & Related papers (2020-10-22T19:58:02Z) - Lenient Regret for Multi-Armed Bandits [72.56064196252498]
We consider the Multi-Armed Bandit (MAB) problem, where an agent sequentially chooses actions and observes rewards for the actions it took.
While the majority of algorithms try to minimize the regret, i.e., the cumulative difference between the reward of the best action and the agent's action, this criterion might lead to undesirable results.
We suggest a new, more lenient, regret criterion that ignores suboptimality gaps smaller than some $epsilon$.
arXiv Detail & Related papers (2020-08-10T08:30:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.