Search and pursuit-evasion in mobile robotics

By Timothy H. Chung, Geoffrey A. Hollinger, Volkan Isler. 2011

607 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Gerhard Neumann
2. Maximilian Hüttenrauch
3. Adrian Šošić

In Computer Networks and Communications

1. Saeid Nahavandi
2. Thanh Thi Nguyen
3. Ngoc Duy Nguyen
4. Gerhard Neumann
5. Maximilian Hüttenrauch
6. Adrian Šošić

In Computer Vision and Pattern Recognition

1. Saeid Nahavandi
2. Maxim Egorov Stanford
3. Gerhard Neumann
4. Thanh Thi Nguyen
5. Maximilian Hüttenrauch
6. Adrian Šošić
7. Ngoc Duy Nguyen

Citations in the corpus

• Deep Reinforcement Learning for Swarm Systems (2018, background): “A survey on pursuit-evasion in mobile robotics in general can be found in [Chung et al., 2011].”
• Multi-Agent Deep Reinforcement Learning (2016, background): “To accomplish this, we pick the pursuitevasion problem [3] as our evaluation domain.”
• Solving Partially Observable Stochastic Games with Public Observations (2019, background): “…Amigoni 2009; Vorobeychik et al. 2014; Basilico, Nittis, and Gatti 2016; Brazdil, Kucera, and Rehak 2018), where a defender protects a set of targets against an attacker, pursuit-evasion (Chung, Hollinger, and Isler 2011), or search games, where a defender is trying to find and capture an attacker.”
• Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (2018, background): “The experiments carried out on the pursuit-evasion problem [88] show the effectiveness of the transfer learning approach in the multiagent domain.”