Cooperative Vehicle Environmental Monitoring 19.4 Recent Developments and Future Directions 453
Part B | 19.4
Lekien et al. [19.111] presented a method that uses
Lagrangian coherent structures to coordinate vehicles
robustly in the presence of very strong currents. Other
methods to coordinate gliders and AUVs to maximize
information in the data collected, taking explicit account of challenging ocean currents have been studied,
[19.112–116].
Strategies for coordinated sampling that optimize
information-based metrics have also been further explored [19.117–123].
Advances have been made in decentralized cooperative control strategies that improve or leverage the
communication network structure of mobile robotic
teams. These include new algorithms that use graph theoretic approaches [19.124] for computing, maintaining
or maximizing connectivity [19.125–127], controllability [19.128], and robustness of coordinated motion to
uncertainty [19.129]. Techniques from algebraic topology have also been applied to problems in multivehicle
sensing [19.130]. Advances in cooperative routing and
motion planning for multiple autonomous vehicles have
been extensive, see, for example [19.131, 132].
Another source of inspiration for cooperative control design comes from mechanisms of collective behavior in animal groups such as fish schools and bird
flocks. In these animal groups, remarkable collective
behaviors result without centralized direction from relatively simple individuals who sense and respond to
their local environment, including the relative position,
heading or speed of neighbors in the group [19.133–
136]. Mathematical models have been used to explain
individual decision-making and interactions that lead to
high-performing group behaviors [19.137–141]. These
models can potentially be used to design provable
decision-making feedback laws for individual robotic
vehicles so that robotic teams inherit some of the critical group-level properties observed in nature, notably
the ability of the group to forage efficiently (for information) despite individual-level limitations on sensing
and communication and significant uncertainty in the
environment. Torney et al. [19.142] showed how animal
groups could apply a performance-dependent interaction to efficiently move to the source of a chemical gradient in a turbulent environment, and Wu et al. [19.143]
turned this into a provable algorithm for efficient cooperative search in a noisy distributed field. In [19.144],
Young et al. applied system-theoretic techniques to understand interactions in starling flocks that yield robustness of consensus to uncertainty, and used these
in [19.129] to design decentralized feedback laws that
enable networks of vehicles to improve their robustness
to uncertainty.
Design of dynamics of decision-making teams of
robots and humans is yet another important direction
of research that has the potential to impact the success
of cooperative vehicles in complex tasks. Humans are
capable of intelligent and adaptable decision-making
in response to reasoning about real-world information
in real time, and robotic systems are capable of significant computational speed and memory. Challenging
problems in complex settings, for example, with multiple scales and significant uncertainty, can be well
served by solutions in which humans and robotic systems participate in complementary ways. Benefits and
possible pitfalls of such human and robot collaboration were evidenced in the AOSN II and ASAP experiments. Indeed, with the data visualization tools,
computational aids, and communication mechanisms
accessible through the GCCS and ASAP’s virtual control room, the ASAP field experiment demonstrated
collaboration between a distributed team of humans and
an automated group of underwater robots to perform
adaptive ocean sampling in an uncertain environment
with multiple objectives subject to a variety of safety
and operational constraints. While the autonomous vehicles moved continuously in coordinated patterns, the
human participants contributed in ways the automated
system could not by making rapid decisions in response
to critical environmental changes (sudden excessively
strong currents or unanticipated shallow water) and
operational failures. The human team also made important longer term decisions in response to observed
or predicted indications of change in the environment,
such as new features or locations of interest, or decline
in performance with respect to one or more performance metric. And yet, opportunities may have been
missed when the human participants had difficulty assessing the likelihood of risk in proposed scenarios or
balancing the many competing objectives. Likewise,
human supervisory strategies may have been less than
optimal given that the autonomous robots had no opportunity to provide direct feedback to their human
supervisors.
In this vein, progress has been reported on humans and robots in exploration [19.145–149] and on
humans working together in search tasks [19.150–153].
Progress has also been made on algorithm development
using what cognitive scientists understand about human
decision-making. For example, decision-making algorithms with provable performance in search tasks were
derived in [19.154], drawing on research on the heuristics humans use to trade off between exploiting wellknown alternatives and exploring uncertain but possibly
better alternatives. It has been shown in [19.155] that
humans use their ability to learn correlation structure when it exists among decision alternatives, as
in the case of alternatives representing different locations in a spatially distributed resource. The knowledge
Part B | 19.4
Lekien et al. [19.111] presented a method that uses
Lagrangian coherent structures to coordinate vehicles
robustly in the presence of very strong currents. Other
methods to coordinate gliders and AUVs to maximize
information in the data collected, taking explicit account of challenging ocean currents have been studied,
[19.112–116].
Strategies for coordinated sampling that optimize
information-based metrics have also been further explored [19.117–123].
Advances have been made in decentralized cooperative control strategies that improve or leverage the
communication network structure of mobile robotic
teams. These include new algorithms that use graph theoretic approaches [19.124] for computing, maintaining
or maximizing connectivity [19.125–127], controllability [19.128], and robustness of coordinated motion to
uncertainty [19.129]. Techniques from algebraic topology have also been applied to problems in multivehicle
sensing [19.130]. Advances in cooperative routing and
motion planning for multiple autonomous vehicles have
been extensive, see, for example [19.131, 132].
Another source of inspiration for cooperative control design comes from mechanisms of collective behavior in animal groups such as fish schools and bird
flocks. In these animal groups, remarkable collective
behaviors result without centralized direction from relatively simple individuals who sense and respond to
their local environment, including the relative position,
heading or speed of neighbors in the group [19.133–
136]. Mathematical models have been used to explain
individual decision-making and interactions that lead to
high-performing group behaviors [19.137–141]. These
models can potentially be used to design provable
decision-making feedback laws for individual robotic
vehicles so that robotic teams inherit some of the critical group-level properties observed in nature, notably
the ability of the group to forage efficiently (for information) despite individual-level limitations on sensing
and communication and significant uncertainty in the
environment. Torney et al. [19.142] showed how animal
groups could apply a performance-dependent interaction to efficiently move to the source of a chemical gradient in a turbulent environment, and Wu et al. [19.143]
turned this into a provable algorithm for efficient cooperative search in a noisy distributed field. In [19.144],
Young et al. applied system-theoretic techniques to understand interactions in starling flocks that yield robustness of consensus to uncertainty, and used these
in [19.129] to design decentralized feedback laws that
enable networks of vehicles to improve their robustness
to uncertainty.
Design of dynamics of decision-making teams of
robots and humans is yet another important direction
of research that has the potential to impact the success
of cooperative vehicles in complex tasks. Humans are
capable of intelligent and adaptable decision-making
in response to reasoning about real-world information
in real time, and robotic systems are capable of significant computational speed and memory. Challenging
problems in complex settings, for example, with multiple scales and significant uncertainty, can be well
served by solutions in which humans and robotic systems participate in complementary ways. Benefits and
possible pitfalls of such human and robot collaboration were evidenced in the AOSN II and ASAP experiments. Indeed, with the data visualization tools,
computational aids, and communication mechanisms
accessible through the GCCS and ASAP’s virtual control room, the ASAP field experiment demonstrated
collaboration between a distributed team of humans and
an automated group of underwater robots to perform
adaptive ocean sampling in an uncertain environment
with multiple objectives subject to a variety of safety
and operational constraints. While the autonomous vehicles moved continuously in coordinated patterns, the
human participants contributed in ways the automated
system could not by making rapid decisions in response
to critical environmental changes (sudden excessively
strong currents or unanticipated shallow water) and
operational failures. The human team also made important longer term decisions in response to observed
or predicted indications of change in the environment,
such as new features or locations of interest, or decline
in performance with respect to one or more performance metric. And yet, opportunities may have been
missed when the human participants had difficulty assessing the likelihood of risk in proposed scenarios or
balancing the many competing objectives. Likewise,
human supervisory strategies may have been less than
optimal given that the autonomous robots had no opportunity to provide direct feedback to their human
supervisors.
In this vein, progress has been reported on humans and robots in exploration [19.145–149] and on
humans working together in search tasks [19.150–153].
Progress has also been made on algorithm development
using what cognitive scientists understand about human
decision-making. For example, decision-making algorithms with provable performance in search tasks were
derived in [19.154], drawing on research on the heuristics humans use to trade off between exploiting wellknown alternatives and exploring uncertain but possibly
better alternatives. It has been shown in [19.155] that
humans use their ability to learn correlation structure when it exists among decision alternatives, as
in the case of alternatives representing different locations in a spatially distributed resource. The knowledge