Pl2pl n impulses

doc/epoch.rst

@@ -9,7 +9,7 @@ seamlessly both to the c++ `std::chrono <https://en.cppreference.com/w/cpp/heade
 library and to the python `datetime <https://docs.python.org/3/library/datetime.html>`_ module. 
 
 .. note::
-    In `pykep`` the default Julian Date is the Modified Julian Date, defined as a `float` representing the number
+    In `pykep` the default Julian Date is the Modified Julian Date, defined as a `float` representing the number
     of days since the start of 2000-1-1. 
 
 .. note::

doc/gym.rst

@@ -3,6 +3,12 @@
 Trajectory Optimization Gym
 ###########################
 
+A number of interplanetary trajectory problems are provided in `pykep` in the form of 
+User Defined Problems (UDP) compatible with the `pygmo <https://esa.github.io/pygmo2/>`_ :cite:p:`pagmo` python package.
+All of the problems are instantiated upon import of the `pykep` module and can be used directly. The collection of all
+problems is called the "pykep gym" and hopes to become an established benchmark set to test the performances of evolutionary
+and traditional optimisation techniques on trajectory design problems.
+
 .. currentmodule:: pykep.trajopt.gym
 
 MGA problems
@@ -89,3 +95,25 @@ This is the same MGA-1DSM problem as :class:`~pykep.trajopt.gym.juice`, but with
 the alpha encoding for the time of flight variables (see :class:`~pykep.trajopt.gym.cassini1_a`).
 The problem is also muulti-objective, with the total time of flight as a second objective, leveragng the 
 alpha encoding to control the total time of flight (at the cost of evolvability).
+
+.. autoattribute:: pykep.trajopt.gym.messenger
+
+This an MGA-1DSM problem inspired by the messenger mission that orbited the planet Mercury between 2011 and 2015, studying Mercury's chemical composition,
+geology, and magnetic field. The name is a backronym for Mercury Surface, Space Environment, Geochemistry, and Ranging, and a reference to the messenger
+god Mercury from Roman mythology. The selected fly-by sequence, E-VVMeMeMe-Me, and other parameters are concident to the actual Messenger mission.
+We have only omitted the first Earth fly-by that was used to correct for launcher performances, since we here do not make use of a launcher model.
+As far as chemical propelled interplanetary trajectories go, this particular one is particularly complex and difficult to design. 
+The time of flights among successive Mercury fly-bys allow for multiple rvolutions and resonances, making optimization techniques struggle to find the correct combination.
+The amount of specialistic knowledge that needs to be used to obtain a successful design is significant.
+Finding a global optimization approach able to find a good trajectory in complete autonomy without making
+use of additional problem knowledge is challenging but possible, but limiting the number of fitness call is difficult.
+
+Multiple impulse problem
+************************
+
+.. autoattribute:: pykep.trajopt.gym.e2m_3imp
+
+.. autoattribute:: pykep.trajopt.gym.e2m_5imp
+
+.. autoattribute:: pykep.trajopt.gym.e2m_7imp
+

doc/notebooks/udp_mga.ipynb

@@ -16,6 +16,19 @@
     ":::{note}\n",
     "{class}`pykep.trajopt.mga` also implements different (unpublished) encodings for the time of flights, namely called $\\alpha$-encoding and $\\eta$-encoding which, albeit less studied and creating a more complex fitness landscape, are interesting in multiple ways: a) they show how the encoding influences evolvability in a drastic way b) they allow to box-bound the total trajectory time of flight c) they remove problem knowledge exploitation.\n",
     "\n",
+    "In the $\\alpha$-encoding the $n$ time of flights $T_i$ are defined as:\n",
+    "\n",
+    "$$T_i = T \\frac{\\log\\alpha_i }{ \\sum_n\\log\\alpha_n}$$\n",
+    "\n",
+    "where $T$ is the total time of flight. This encoding ensures that all possible grids are equally probable if the $\\alpha$ are uniformly distributed in [0,1].\n",
+    "\n",
+    "In the $\\eta$ encoding $n$ time of flights $T_i$ are defined as:\n",
+    "\n",
+    "$$\n",
+    "T_i = \\left(T_{max} - \\sum_{j=0}^{(i-1)}T_j\\right) \\eta_i\n",
+    "$$\n",
+    "where $T_{max}$ is the maximum total time of flight, which will only happen if $\\eta_{n}=1$. This encoding DOES NOT ensure that all possible grids are equally probable if the $\\eta$ are uniformly distributed in [0,1].\n",
+    "\n",
     "We start, as often, with some fundamental imports:"
    ]
   },

doc/notebooks/udp_mga_1dsm.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multiple Gravity Assist with deep space manouvres (MGA-1DSM)\n",
+    "# Multiple Gravity Assist with one DSM (MGA-1DSM)\n",
     "\n",
     "In this tutorial, we use the {class}`pykep.trajopt.mga_1dsm` to design an interplanetary trajectory. The MGA-1DSM encoding of an interplanetary trajectory models multiple (pre-defined) planetary encounters, and enables for propulsion manovures in the form of one instantaneous $\\Delta V$ at any time along each leg.\n",
     "\n",

doc/refs.bib

@@ -99,4 +99,14 @@ @article{izzo2010global
   pages={178--200},
   year={2010},
   publisher={Cambridge University Press, New York, NY, USA}
+}
+
+@article{pagmo,
+  title={A parallel global multiobjective framework for optimization: pagmo},
+  author={Biscani, Francesco and Izzo, Dario},
+  journal={Journal of Open Source Software},
+  volume={5},
+  number={53},
+  pages={2338},
+  year={2020}
 }

doc/tut_trajopt.rst

@@ -29,4 +29,5 @@ Trajectory Optimization
   notebooks/udp_point2point
   notebooks/udp_pl2pl
   notebooks/udp_mga
-  notebooks/udp_mga_1dsm
+  notebooks/udp_mga_1dsm
+  notebooks/udp_pl2pl_Nimpulses

pykep/test_trajopt.py

@@ -217,6 +217,22 @@ def test_juice_mo(self):
         f = udp.fitness(x)
         self.assertTrue(float_rel_error(f[0], 427.31998557200325) < 1e-13)
         self.assertTrue(float_rel_error(f[1], 2270.0472019876433) < 1e-13)
+
+    def test_messenger(self):
+        import pykep as pk
+
+        udp = pk.trajopt.gym.messenger
+        # Ground truth checked by the old pykep code (up to 8 digits only as per differences with constants and all)
+        x = [ 2.03241398e+03,  6.40762059e-01,  6.63357785e-01,  4.04989271e+03,
+        6.63732323e-01,  4.50068524e+02, -3.86553343e+00,  3.52631372e+00,
+        5.57888828e-01,  2.24619580e+02, -4.45910441e+00,  1.22736521e+00,
+        7.08063036e-01,  2.17965497e+02, -2.47894274e+00,  1.43586128e+00,
+        5.88391838e-01,  2.62423586e+02, -2.40594385e-02,  2.45470457e+00,
+        7.25370468e-01,  3.58067954e+02,  1.47192632e+00,  1.05000000e+00,
+        9.02984391e-01,  5.38436770e+02]
+
+        f = udp.fitness(x)
+        self.assertTrue(float_rel_error(f[0], 5855.81434335236) < 1e-13)
 
 class trajopt_mga1dsm_tests(_ut.TestCase):
     def test_construction(self):

pykep/trajopt/CMakeLists.txt

@@ -1,5 +1,4 @@
-set(PYKEP_TRAJOPT_PYTHON_FILES __init__.py _direct_point2point.py _direct_pl2pl.py _mga.py _mga_1dsm.py _launchers.py)
+set(PYKEP_TRAJOPT_PYTHON_FILES __init__.py _direct_point2point.py _direct_pl2pl.py _mga.py _mga_1dsm.py _launchers.py _pl2pl_N_impulses.py)
 install(FILES ${PYKEP_TRAJOPT_PYTHON_FILES} DESTINATION ${_PYKEP_INSTALL_DIR}/trajopt)
 
-ADD_SUBDIRECTORY(gym)
-
+ADD_SUBDIRECTORY(gym)

pykep/trajopt/__init__.py

@@ -16,10 +16,11 @@
 # Evolutionary encodings for high energy transfers (chemical propulsion)
 from ._mga import mga
 from ._mga_1dsm import mga_1dsm
+from ._pl2pl_N_impulses import pl2pl_N_impulses
 
 # The launchers models
 from ._launchers import _launchers
 launchers = _launchers()
 
 # The interplanetary trajectory gym
-from . import gym 
+from . import gym