Space-Time Group Motion Planning

Karamouzas, Ioannis; Gerærts, Roland; Stappen, A. Frank van der

doi:10.1007/978-3-642-36279-8_14

Cited by 12 publications

(10 citation statements)

References 27 publications

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“…[Karamouzas and Overmars 2012] extend this research trying to model macro/microscopic behaviors: for each time step agents follow the group path but solve its own path following problem autonomously. [Karamouzas et al 2013] adopt minimization of space and time and is based on a linear programming technique to resolve congestions and prevent deadlocks. Note that a weak point of these methods is not to consider the full dynamic nature of the social space, e.g.…”

Section: Related Workmentioning

confidence: 99%

Prediction in social path following

Oliva

Vilhjálmsson

2014

Proceedings of the Seventh International Conference on Motion in Games

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Figure 1: A conversation group reacts to a passer-by and rearranges to avoid a collision in a tight space. AbstractPath following in games mostly focuses on avoiding collisions with dynamic physical objects that appear along a chosen path to a given destination. Some work also attempts to humanize the abstract path returned by a path finding algorithm through methods like smoothing. Games typically do not consider social factors during path following, even though many depict social environments. Social path following considers the social environment in particular, carving a trajectory that reflects awareness of other human beings and their social activities. This includes awareness of territories that have social significance but no concrete physical form, such as the space between those having a conversation. This paper describes work that extends a state-of-the-art predictive method for path following with social awareness, predicting and avoiding social collisions. The work builds on a platform for social simulation, which already models social territoriality and gaze behavior. The results appear promising and highlight the importance of perceiving and dealing with the social space along with the physical one.

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Section: Related Workmentioning

confidence: 99%

Prediction in social path following

Oliva

Vilhjálmsson

2014

Proceedings of the Seventh International Conference on Motion in Games

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“…We focus on minimizing the travel time of the agents, but other metrics have been studied in the literature. For example, the work in [48,56,27] addresses the problem of minimizing the total length of the path of the agents, formulating the path planning problem as a mixed integer linear program. Coordinating the motion of a set of pebbles in a graph to minimize the number of moves was studied in [33].…”

Section: Multi-agent Navigationmentioning

confidence: 99%

ALAN: adaptive learning for multi-agent navigation

et al. 2018

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In multi-agent navigation, agents need to move towards their goal locations while avoiding collisions with other agents and static obstacles, often without communication with each other. Existing methods compute motions that are optimal locally but do not account for the aggregated motions of all agents, producing inefficient global behavior especially when agents move in a crowded space. In this work, we develop methods to allow agents to dynamically adapt their behavior to their local conditions. We accomplish this by formulating the multi-agent navigation problem as an action-selection problem, and propose an approach, ALAN, that allows agents to compute time-efficient and collision-free motions. ALAN is highly scalable because each agent makes its own decisions on how to move using a set of velocities optimized for a variety of navigation tasks. Experimental results show that the agents using ALAN, in general, reach their destinations faster than using ORCA, a state-of-the-art collision avoidance framework, the Social Forces model for pedestrian navigation, and a Predictive collision avoidance model.

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“…Approaches [25] and [26] show a combined planning methods based on network flow and medial axis. In particular, the former provides an interesting parallelism among network flow problem on a graph and multi-agent path planning problem, but it fails in providing an applicable solution.…”

Section: A Related Workmentioning

confidence: 99%

Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

Digani

Sabattini

Secchi

et al. 2015

IEEE Trans. Automat. Sci. Eng.

115

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This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants

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Space-Time Group Motion Planning

Cited by 12 publications

References 27 publications

Prediction in social path following

Prediction in social path following

ALAN: adaptive learning for multi-agent navigation

Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

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