Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios Article Swipe
Rahul Tallamraju
,
Sujit Rajappa
,
Michael J. Black
,
Kamalakar Karlapalem
,
Aamir Ahmad
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/ssrr.2018.8468655
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/ssrr.2018.8468655
In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M}
Related Topics
Concepts
Obstacle avoidance
Robot
Computer science
Motion planning
Context (archaeology)
Obstacle
Collision avoidance
Model predictive control
Control theory (sociology)
Trajectory
Field (mathematics)
Mobile robot
Maxima and minima
Artificial intelligence
Mathematical optimization
Mathematics
Control (management)
Pure mathematics
Collision
Law
Computer security
Astronomy
Political science
Physics
Paleontology
Biology
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/ssrr.2018.8468655
- OA Status
- green
- Cited By
- 37
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2803245620
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2803245620Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/ssrr.2018.8468655Digital Object Identifier
- Title
-
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking ScenariosWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-01Full publication date if available
- Authors
-
Rahul Tallamraju, Sujit Rajappa, Michael J. Black, Kamalakar Karlapalem, Aamir AhmadList of authors in order
- Landing page
-
https://doi.org/10.1109/ssrr.2018.8468655Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1805.09633Direct OA link when available
- Concepts
-
Obstacle avoidance, Robot, Computer science, Motion planning, Context (archaeology), Obstacle, Collision avoidance, Model predictive control, Control theory (sociology), Trajectory, Field (mathematics), Mobile robot, Maxima and minima, Artificial intelligence, Mathematical optimization, Mathematics, Control (management), Pure mathematics, Collision, Law, Computer security, Astronomy, Political science, Physics, Paleontology, Biology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
37Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 4, 2023: 6, 2022: 4, 2021: 10Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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