Sheepdogs smartly herd a flock of sheep and guide them towards a goal. A single dog can herd a fe... more Sheepdogs smartly herd a flock of sheep and guide them towards a goal. A single dog can herd a few hundred sheep in easy to navigate environments. Understanding the interaction space between the sheepdogs, sheep and the environment is important due to the possibility of transferring this knowledge to solve practical swarm robotics problems. This interaction space is a complex mesh of influencing factors. We scrutinize this interaction space to identify areas where the complexity of the herding problem changes from low (easy to solve) to high (harder to solve or becoming unsolvable) complexity. In particular, we study reactive models for shepherding, whereby agents respond directly to stimuli in the environments by fusing the set of force vectors influencing their behaviour. We present an enhanced shepherding model with higher success rate than its predecessor. We investigate four key factors that influence the complexity of the problem: the relative speed between the sheepdog and sheep, the spatial configuration of the sheep at the start of the task, the number of sheepdogs, and the density of obstacles in the environment. We discovered a phase transition in shepherding resulting from the interaction between the number of sheepdogs and obstacles. The phase transition occurs as the density of obstacles range from 0.2% for a single shepherding agent to 5% for 10 shepherding agents. During this phase transition, the problem changes from being an easy problem where the flock gets collected quickly, to a hard one where the overall herding task becomes utterly not achievable using reactive approaches.
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Shepherding is a biologically inspired swarm control methodology based on the herding of sheep by... more Shepherding is a biologically inspired swarm control methodology based on the herding of sheep by sheepdogs. The approach is applicable not only to sheep herding, but also to the guidance of one group of agents (eg.robots) by another. The pertinent research questions involve modeling the dynamics of the sheep and sheep-dog agent behaviours, in order to fully understand the sources of complexity in the problem and improve shepherding performance. To this end, we examine an existing model by Strömbom et al. and present a preliminary study towards improving it.
The Australian Defence Force (ADF) plans to invest a sizeable portion of its ten-year Defence Cap... more The Australian Defence Force (ADF) plans to invest a sizeable portion of its ten-year Defence Capability Program (DCP) budget in migrating to a Network-Centric Warfare (NCW)-capable combat force. But what defines being NCW-capable? And how do we measure how ready the ADF is to adopt NCW? The NCW Prioritisation and Integration (NPI) methodology has been developed to enable characterisation of the NCW readiness of individual planned military capabilities and how well they are likely to integrate into the planned force-level systems-of-systems. Individual capabilities are assessed against metrics covering support for human-centric military business processes, sophistication of information management and support for applicable standards. Force integration is assessed through analysis of how well communications and other infrastructure services will support each capability. The methodology was initially developed and successfully applied to address the tactical land domain, and has now e...
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Shepherding involves herding a swarm of agents (sheep) by another a control agent (sheepdog) towa... more Shepherding involves herding a swarm of agents (sheep) by another a control agent (sheepdog) towards a goal. Multiple approaches have been documented in the literature to model this behaviour. In this paper, we present a modification to a well-known shepherding approach, and show, via simulation, that this modification improves shepherding efficacy. We then argue that given complexity arising from obstacles laden environments, path planning approaches could further enhance this model. To validate this hypothesis, we present a 2-stage evolutionary-based path planning algorithm for shepherding a swarm of agents in 2D environments. In the first stage, the algorithm attempts to find the best path for the sheepdog to move from its initial location to a strategic driving location behind the sheep. In the second stage, it calculates and optimises a path for the sheep. It does so by using way points on that path as the sequential sub-goals for the sheepdog to aim towards. The proposed algorithm is evaluated in obstacle laden environments via simulation with further improvements achieved.
Autonomous Vehicles are becoming a reality in places with advanced infrastructure to support thei... more Autonomous Vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, Advanced-Vehicle Systems (AVSs) need to be better contextually aware. The vast literature referring to "context-aware systems" is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realise contextual awareness in AVSs. This paper uses a Human-AVSs (HAVSs) lens to polarise the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextualawareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.
Sheepdogs smartly herd a flock of sheep and guide them towards a goal. A single dog can herd a fe... more Sheepdogs smartly herd a flock of sheep and guide them towards a goal. A single dog can herd a few hundred sheep in easy to navigate environments. Understanding the interaction space between the sheepdogs, sheep and the environment is important due to the possibility of transferring this knowledge to solve practical swarm robotics problems. This interaction space is a complex mesh of influencing factors. We scrutinize this interaction space to identify areas where the complexity of the herding problem changes from low (easy to solve) to high (harder to solve or becoming unsolvable) complexity. In particular, we study reactive models for shepherding, whereby agents respond directly to stimuli in the environments by fusing the set of force vectors influencing their behaviour. We present an enhanced shepherding model with higher success rate than its predecessor. We investigate four key factors that influence the complexity of the problem: the relative speed between the sheepdog and sheep, the spatial configuration of the sheep at the start of the task, the number of sheepdogs, and the density of obstacles in the environment. We discovered a phase transition in shepherding resulting from the interaction between the number of sheepdogs and obstacles. The phase transition occurs as the density of obstacles range from 0.2% for a single shepherding agent to 5% for 10 shepherding agents. During this phase transition, the problem changes from being an easy problem where the flock gets collected quickly, to a hard one where the overall herding task becomes utterly not achievable using reactive approaches.
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Shepherding is a biologically inspired swarm control methodology based on the herding of sheep by... more Shepherding is a biologically inspired swarm control methodology based on the herding of sheep by sheepdogs. The approach is applicable not only to sheep herding, but also to the guidance of one group of agents (eg.robots) by another. The pertinent research questions involve modeling the dynamics of the sheep and sheep-dog agent behaviours, in order to fully understand the sources of complexity in the problem and improve shepherding performance. To this end, we examine an existing model by Strömbom et al. and present a preliminary study towards improving it.
The Australian Defence Force (ADF) plans to invest a sizeable portion of its ten-year Defence Cap... more The Australian Defence Force (ADF) plans to invest a sizeable portion of its ten-year Defence Capability Program (DCP) budget in migrating to a Network-Centric Warfare (NCW)-capable combat force. But what defines being NCW-capable? And how do we measure how ready the ADF is to adopt NCW? The NCW Prioritisation and Integration (NPI) methodology has been developed to enable characterisation of the NCW readiness of individual planned military capabilities and how well they are likely to integrate into the planned force-level systems-of-systems. Individual capabilities are assessed against metrics covering support for human-centric military business processes, sophistication of information management and support for applicable standards. Force integration is assessed through analysis of how well communications and other infrastructure services will support each capability. The methodology was initially developed and successfully applied to address the tactical land domain, and has now e...
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Shepherding involves herding a swarm of agents (sheep) by another a control agent (sheepdog) towa... more Shepherding involves herding a swarm of agents (sheep) by another a control agent (sheepdog) towards a goal. Multiple approaches have been documented in the literature to model this behaviour. In this paper, we present a modification to a well-known shepherding approach, and show, via simulation, that this modification improves shepherding efficacy. We then argue that given complexity arising from obstacles laden environments, path planning approaches could further enhance this model. To validate this hypothesis, we present a 2-stage evolutionary-based path planning algorithm for shepherding a swarm of agents in 2D environments. In the first stage, the algorithm attempts to find the best path for the sheepdog to move from its initial location to a strategic driving location behind the sheep. In the second stage, it calculates and optimises a path for the sheep. It does so by using way points on that path as the sequential sub-goals for the sheepdog to aim towards. The proposed algorithm is evaluated in obstacle laden environments via simulation with further improvements achieved.
Autonomous Vehicles are becoming a reality in places with advanced infrastructure to support thei... more Autonomous Vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, Advanced-Vehicle Systems (AVSs) need to be better contextually aware. The vast literature referring to "context-aware systems" is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realise contextual awareness in AVSs. This paper uses a Human-AVSs (HAVSs) lens to polarise the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextualawareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.
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Papers by Anthony Perry