Cooperative Motion Planning for Multiple Autonomous Marine Vehicles

Häusler, Andreas J.; Saccon, Alessandro; Aguiar, A. Pedro; Hauser, John; Pascoal, António

doi:10.3182/20120919-3-it-2046.00042

Cited by 15 publications

(4 citation statements)

References 16 publications

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“…We have proceeded with trajectory exploration of the reduced model using projection operator based trajectory optimization techniques, [21], [22], [23]. These techniques have been used in many situations see, e.g., [24], [25]. Noting that forces in the reduced model depend only on relative positions and velocities (and the control input), we used a 4 state, 1 input model and have found the system trajectory that minimizes the integral of a diagonally weighted quadratic incremental cost.…”

Section: Trajectory Explorationmentioning

confidence: 99%

Modeling and exploration of an active helmet design

Meyer

Hauser

2013

2013 American Control Conference

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Thousands of mild traumatic brain injuries (mBTI's) occur in US football each year. In some instances, injuries are very serious. New NFL rules are an attempt to address this problem. Of interest is whether an active helmet design, employing feedback, can also reduce the indicidence of mBTI's in football. Medical evidence and research suggests that peak center of gravity (CG) head acceleration is well correlated with onset and severity of mBTI. In this paper, an active helmet design is introduced, modeled, and explored from the perspective of using control to reduce maximum CG head acceleration during a typical football impact.

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Section: Trajectory Explorationmentioning

confidence: 99%

Modeling and exploration of an active helmet design

Meyer

Hauser

2013

2013 American Control Conference

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“…Collision avoidance methods for ASVs are categorized into path/motion planning approaches [17]- [20] and optimizationbased algorithms [14], [21]- [24]. In the path/motion planning approaches, a collision-free reference trajectory or motion is generated based on either off-line or on-line methods, e.g., A * [17], RRT * [18], [20], and potential field methods [25], etc.…”

Section: Introductionmentioning

confidence: 99%

Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

Zhang

Pan

Reppa

2020

Preprint

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This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed control design, a nominal system is considered for the design of a baseline tracking controller using a conventional control approach. The nominal system also defines the desired behaviour of uncertain autonomous surface vehicles in an obstacle-free environment. Thanks to reinforcement learning, the overall tracking controller is capable of compensating for model uncertainties and achieving collision avoidance at the same time in environments with obstacles. In comparison to traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm using an example of autonomous surface vehicles.

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“…Techniques from algebraic topology have also been applied to problems in multi-vehicle sensing [131]. Advances in cooperative routing and motion planning for multiple autonomous vehicles have been extensive, see for example [132,133].…”

Section: Recent Developments and Future Directionsmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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Cooperative Motion Planning for Multiple Autonomous Marine Vehicles

Cited by 15 publications

References 16 publications

Modeling and exploration of an active helmet design

Modeling and exploration of an active helmet design

Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

Cooperative Vehicle Environmental Monitoring

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