US2004148268A1PendingUtilityA1
Artificial multiped and motion controller therefor
Priority: Feb 28, 2001Filed: Jan 25, 2002Published: Jul 29, 2004
Est. expiryFeb 28, 2021(expired)· nominal 20-yr term from priority
Inventors:Torsten Reil
G05B 2219/39273B62D 57/032B25J 9/161G05B 2219/39284
26
PatentIndex Score
0
Cited by
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0
Claims
Abstract
An artificial multiped is constructed (either in simulation or embodied) in such a way that its natural body dynamics allow the lower part of each leg to swing naturally under the influence of gravity. The upper part of each leg is actively actuated in the sagittal plane. The necessary input to drive the above-mentioned actuators is derived from a neural network controller. The latter is arranged as two bi-directionally coupled chains of neural oscillators, the number of which equals twice that of the legs to be actuated. Parameter optimisation of the controllers is achieved by evolutionary computation in the form of a genetic algorithm.
Claims
exact text as granted — not AI-modified1 . An artificial multiped, comprising:
a body trunk; at least two legs depending from the trunk, each leg having at least a first, upper part and a second, relatively lower part pivotable about the said first upper part; a plurality of hip joints each connecting the upper part of an associated leg to the body trunk; an actuator arranged to move the upper part of each leg in an arc about the hip joint; a neural controller arranged to control the movement of the said actuator such that, as each upper leg part is moved through the said arc, the corresponding lower part thereof is arranged to describe an apparently substantially free-swinging movement over at least a part of the said upper leg part movement.
2 . The multiped of claim 1 , in which the second relatively lower part of each leg is freely pivotable about the corresponding first upper part of that leg, and in which the lower part of each leg is unactuated such that it swings substantially freely over at least a part of the said upper leg part movement.
3 . The multiped of claim 1 or claim 2 , in which the actuator is arranged to receive an output from the neural controller and to generate a torque in response thereto.
4 . The multiped of claim 3 , in which the actuator is a force limited actuator.
5 . The multiped of claim 3 , in which the actuator is a proportional derivative actuator in which the torque at time T, Γ T , applied to the upper part of each leg is a function of the angle, Θ, of the upper part of each said leg relative to another body part.
6 . The multiped of claim 3 , in which the actuator is a proportional-integral-derivative actuator in which the torque at time T, Γ T , applied to the upper part of each leg is a function of the angle, Θ, of the upper part of each said leg relative to another body part.
7 . The multiped of claim 6 , in which the torque applied to the upper part of each leg is related to the angle thereof via the equation
Γ
T
=
k
p
(
θ
d
-
θ
)
-
k
D
θ
t
+
k
I
∫
T
-
Δ
T
T
+
Δ
T
(
θ
d
-
θ
)
t
where k p , k D and k I are the proportional, derivative and integral coefficients respectively, t is time, Γ T is the torque at time T, and Θ D is a desired angle of the upper part of each leg relative to the body trunk.
8 . The multiped of claim 3 , in which the actuator is a force limited velocity constraint (FLVC) actuator, wherein the torque generated is a function of the desired velocity of movement of an upper leg part of a first leg relative to the body trunk; and wherein the desired velocity of movement is related to a difference between the actual and the desired angle between the upper part of the said first leg and the body trunk, the desired relative angle being derived by the said neural controller.
9 . The multiped of any preceding claim comprising at least one actuator per leg, arranged to actuate the associated hip joint so as to cause the corresponding upper leg part to move.
10 . The multiped of claim 9 , in which each leg comprises a single actuator, associated with the first, upper part of the respective leg.
11 . The multiped of any one of the preceding claims, in which each hip joint has only one degree of freedom such that the corresponding upper leg part is constrained to move only sagitally.
12 . The multiped of any preceding claim, in which each lower leg part is freely pivotable about the upper leg part but wherein the angle between the said upper and lower leg parts is limited in use.
13 . The multiped of any preceding claim, further comprising a plurality of feet each connected to an end of the lower part of a corresponding leg via an ankle joint which acts in use as a damped torsional spring.
14 . The multiped of any preceding claim, in which the neural controller comprises a neural network having a plurality of neurones, at least two of the neurones acting as motor neurones each arranged to control the movement of the actuator so as to cause locomotion of the said trunk via the legs.
15 . The multiped of claim 14 , in which the neural network is arranged to act as two bi-directionally coupled chains of identical oscillators.
16 . The multiped of claim 15 in which the neural network is arranged to act as two bi-directionally coupled pairs of identical oscillators.
17 . The multiped of claim 14 , claim 15 or claim 16 , in which each oscillator in the said chains is embodied as a fully recurrent neural network (RNN) having a plurality of nodes.
18 . The multiped of claim 17 in which each RNN has six nodes.
19 . The multiped of claim 17 or claim 18 , in which each RNN oscillator in one of the chains is connected to a laterally adjacent RNN oscillator in the other of the chains via symmetrical weights so as to provide bi-directional coupling therebetween, and in which each neurone in each RNN oscillator is arranged to synapse with an equivalent neurone in the said laterally adjacent RNN oscillator.
20 . The multiped of claim 19 , in which each neurone within each RNN oscillator in one of the chains is asymmetrically (unidirectionally) connected to a corresponding neurone within a next RNN oscillator in the same chain.
21 . The multiped of any one of claims 14 to 20 , in which the properties of the RNNs within the controller are evolved via a genetic algorithm.
22 . The multiped of any preceding claim, comprising two legs and two associated hips such that the multiped is a biped.
23 . A controller for an artificial biped, the biped comprising two legs depending from a trunk and being capable of actuation via an actuator, the controller comprising a neural network having a plurality of neurones, at least two of the neurones acting as motor neurones which are arranged to control the movement of the actuator so as to cause locomotion of the trunk via the legs.
24 . The controller of claim 23 , in which the neural network is arranged to act as two bi-directionally coupled pairs of identical oscillators.
25 . The controller of claim 23 or claim 24 , in which each oscillator in the said chains is embodied as a fully recurrent neural network (RNN) having a plurality of nodes.
26 . The controller of claim 25 , in which each RNN has six nodes.
27 . The controller of claim 25 or claim 26 , in which each RNN oscillator in one of the chains is connected to a laterally adjacent RNN oscillator in the other of the chains via symmetrical weights so as to provide bi-directional coupling therebetween, and in which each neurone in each RNN oscillator is arranged to synapse with an equivalent neurone in the said laterally adjacent RNN oscillator.
28 . The controller of claim 27 , in which each neurone within each RNN oscillator in one of the chains is asymmetrically (unidirectionally) connected to a corresponding neurone within a next RNN oscillator in the same chain.
29 . The controller of any one of claims 23 to 28 , in which the properties of the RNNs within the controller are evolved via a genetic algorithm.
30 . An artificial biped comprising two legs depending from a trunk and being capable of actuation via an actuator, in combination with the controller of any of claims 23 to 29 .
31 . An artificial biped comprising:
a body trunk; two legs depending from the trunk, each leg having a first, upper part and a second, lower part freely pivotable about the said first upper part; two hip joints, each one connecting the upper part of an associated one of the two legs to the body trunk; a controller, comprising two bi-directionally coupled pairs of identical oscillators embodied as a neural network having a plurality of neurones, at least two neurones acting as motor neurones; and a force-limited velocity constraint actuator arranged to move the upper part of each leg in an arc about its associated hip joint, and in which the torque, PT, applied to the upper part of each of the two legs is a function of the desired velocity of movement of the upper part of each leg relative to the body trunk, and wherein the desired velocity of movement is related to a difference between the actual and the desired angle between each upper leg part and the body trunk, the desired relative angle being derived from the neural controller; the controller being arranged to control the actuation of the actuator such that, as each upper leg part is moved through the said arc, the corresponding lower part thereof describes an apparently substantially free-swinging movement over at least a part of the said upper leg part movement.
32 . A computer system comprising:
processing means arranged (i) to generate an image, on a visual display unit, of a multiped having a trunk and at least two legs depending therefrom, each leg including at least a first, upper part and a second, relatively lower part; (ii) to simulate the Newtonian forces that would be acting upon each separate part of the multiped at a given time and to apply these simulated forces to each said separate part so that, when displayed, each part moves under the influence of these simulated forces; (iii) to cause the image of each upper leg part to pivot about the trunk; (iv) to cause the image of the upper part of each leg to be moved via a simulated actuator in an arc relative to the said trunk; and (v) to cause the image of each relatively lower leg part to describe an apparently substantially free-swinging movement relative to its respective upper part; (vi) to provide a neural network having a plurality of neurones, the output of which neural network controls the actuator movement of each upper leg part such that the image of the multiped appears to walk.
33 . The system of claim 32 , in which the processing means is further arranged to simulate the actuator by calculating the simulated torque, Γ T , at a time T that is required to move the image of the upper part of each leg, under the effect of the said simulated Newtonian forces, in response to an output from the neural network.
34 . The system of claim 33 , in which the actuator is a proportional derivative actuator, wherein the simulated torque, Γ T , applied to the upper part of each leg is a function of the angle, Θ, of the upper part of each said leg relative to another body part.
35 . The system of claim 33 , in which the actuator is a proportional-integral-derivative actuator wherein the simulated torque, Γ T , applied to the upper part of each leg is a function of the angle, Θ, of the upper part of each said leg relative another body part.
36 . The system of claim 35 , in which the torque Γ T , at time T is related to the angle Θ in accordance with the equation
Γ
T
=
k
p
(
θ
d
-
θ
)
-
k
D
θ
t
+
k
I
∫
T
-
Δ
T
T
+
Δ
T
(
θ
d
-
θ
)
t
where k p , k D and k I are the proportional, derivative and integral coefficients, and t is time.
37 . The system of claim 32 , in which the processing means is further arranged to simulate the actuator as a force limited velocity constraint actuator in which the torque, Γ T , applied to the upper part of each of the two legs is a function of the desired velocity of movement of the upper part of a leg relative to the body trunk, and wherein the desired velocity of movement is related to a difference between the actual and the desired angle between each upper leg part and the body trunk, the desired relative angle being derived from the neural network.
38 . The system of any one of claims 32 to 37 , in which the processing means is arranged to generate a simulated hip joint for each leg, to link the upper part of a given leg to the trunk and to simulate at least one actuator per leg, each actuator being arranged to act upon the hip joint associated therewith so as to cause the image of the corresponding upper leg part to move.
39 . The system of claim 38 , in which the processing means is arranged to simulate one actuator per leg.
40 . The system of claim 38 or claim 39 , in which the processor constrains the simulated hip joint to have only one degree of freedom such that the image of the corresponding upper leg part is constrained to move only sagitally.
41 . The system of any one of claims 32 to 40 , in which the processing means is further arranged to generate an image of a plurality of feet, each of which appears to be connected to an end of the image of the lower part of a corresponding leg via an ankle joint, and wherein the processing means simulates the action of a damped torsional spring to govern the movement of each said ankle joint.
42 . The system of any one of claims 32 to 41 , in which the processing means limits the angle between the image of the upper part and the image of the lower part of each leg.
43 . The system of any of claims 32 to 42 , in which the neurones of the neural network are arranged as a recurrent neural network (RNN) so as to form two bi-directionally coupled chains of identical oscillators, at least two of the neurones acting as motor neurones whose output controls the said actuator associated with the upper part of each said leg.
44 . The system of claim 43 , in which the RNN comprises a plurality of neurones arranged so as to form two bi-directionally coupled pairs of identical oscillators.
45 . The system of claim 44 , in which the RNN comprises one recurrent subnet per oscillator, each of which has six nodes.
46 . The system of claim 45 , in which each recurrent subnet in one of the chains is connected to a laterally adjacent recurrent subnet in the other of the chains via symmetrical weights so as to provide bi-directional coupling therebetween, and in which each neurone in each recurrent subnet is arranged to synapse with an equivalent neurone in the said laterally adjacent recurrent subnet.
47 . The system of claim 46 , in which each neurone within each recurrent subnet in one of the chains is asymmetrically (unidirectionally) connected to a corresponding neurone within a next recurrent subnet in the same chain.
48 . The system of any one of claims 32 to 47 , in which the processing means is further configured to determine the properties of the neurones and their connections within the said neural network on the basis of a genetic algorithm.
49 . The system of claim 48 , in which the processing means determines the said properties on the basis of a genetic algorithm in which the said properties are optimized by the identification of a fittest array of property values from a plurality of different arrays of property values in accordance with the criterion that the distance over which an end of the lower part of the rearmost leg in the image of the multiple, relative to an origin, should have travelled a maximum distance from the said origin.
50 . The system of claim 49 , in which the processing means is further arranged to abort the consideration of a given one of the arrays of property values if it is determined that a point attractor has been reached such that the image of the multiped stops moving away from the origin.
51 . The system of claim 49 or claim 50 , in which the processing means is further arranged to abort the consideration of a particular one of the arrays if it is determined that the centre of mass of the trunk of the multiped, as calculated via the simulation of the Newtonian forces thereupon, falls below a predetermined height.
53 . The system of any of claims 32 to 52 , in which the processor is arranged to generate an image of a biped.
54 . A controller for a computer-generated image of a biped, the image comprising two legs depending from a trunk, each leg being actuated by a computer simulated actuator, the controller comprising a neural network having a plurality of neurones, the output of the neural network being arranged to control the computer simulated actuator such that the image of the trunk is caused to move as the image of the legs is moved.
55 . The controller of claim 54 , in which the neural network is arranged to act as two bi-directionally coupled pairs of identical oscillators.
56 . The controller of claim 54 or claim 55 , in which each oscillator in the said chains is embodied as a fully recurrent neural network (RNN) having a plurality of nodes.
57 . The controller of claim 56 , in which each RNN has six nodes.
58 . The controller of claim 56 or claim 57 , in which each RNN oscillator in one of the chains is connected to a laterally adjacent RNN oscillator in the other of the chains via symmetrical weights so as to provide bi-directional coupling therebetween, and in which each neurone in each RNN oscillator is arranged to synapse with an equivalent neurone in the said laterally adjacent RNN oscillator.
59 . The controller of claim 58 , in which each neurone within each RNN oscillator in one of the chains is asymmetrically (unidirectionally) connected to a corresponding neurone within the other RNN oscillator in the same chain.
60 . The controller of any of claims 54 to 59 , in which the properties of the RNNs within the controller are evolved via a genetic algorithm.
61 . The controller of claim 60 , in which the said properties are determined on the basis of a genetic algorithm in which said properties are optimized by the identification of a fittest array of property values from a plurality of different arrays of property values in accordance with the criterion that the distance over which an end of the lower part of a rearmost leg in the image of the multipedal device, relative to an origin, should have travelled a maximum distance from the said origin.
62 . A computer system comprising processing means arranged
(i) to generate an image, on a visual display unit, of a biped having a trunk and legs depending therefrom, each leg including at least a first, upper part and a second, relatively lower part; (ii) to simulate the Newtonian forces that would be acting upon each separate part of the biped at a given time and to apply these simulated forces to each said separate part so that, when displayed, each part moves under the influence of these simulated forces; (iii) to cause the image of each upper leg part to pivot about the trunk; (iv) to provide a neural network having a plurality of neurones together arranged to act as two bi-directionally coupled pairs of identical oscillators; (v) to cause the image of the upper parts of each of the two legs to be moved by a force limited velocity constraint actuator, in an arc relative to the said trunk, the actuator being simulated by the processing means to calculate the simulated torque Γ T , at a time T, required to move the image of the upper part of its associated leg, under the effect of the said simulated Newtonian forces, as a function of the desired velocity of movement of the upper part of each leg relative to the body trunk, and wherein the desired velocity of movement is related to a difference between the actual and the desired angle between each upper leg part and the body trunk, the desired relative angle being derived from the neural network; and (vi) to cause the image of each lower leg part to describe an apparently substantially free-swinging movement relative to its respective upper part as the said upper part moves in its said arc; the output of the neural network being arranged to control the simulated actuator such that the image of the trunk is caused to move as the image of the legs is moved.
63 . A computer program comprising program elements which, when executed on a digital computer, cause a processor thereof:
(i) to generate an image, on a visual display unit, of a multiped having a trunk and at least two legs depending therefrom, each leg including at least a first, upper part and a second, relatively lower part; (ii) to simulate the Newtonian forces that would be acting upon each separate part of the multiped at a given time and to apply these simulated forces to each said separate part so that, when displayed, each part moves under the influence of these simulated forces; (iii) to cause the image of each upper leg part to pivot about the trunk; (iv) to provide a neural network having a plurality of neurones together arranged to act as two bi-directionally coupled pairs of identical oscillators; (v) to cause the image of the upper parts of each of the two legs to be moved by an actuator, in an arc relative to the said trunk; and (vi) to cause the image of each lower leg part to describe an apparently substantially free-swinging movement relative to its respective upper part as the said upper part moves in its said arc; the output of the neural network being arranged to control the simulated actuator such that the image of the trunk is caused to move as the image of the legs is moved.
64 . The computer program of claim 63 , further comprising a program element which causes the actuator to be simulated by calculating the simulated torque, Γ T , required to move the image of the upper part of each leg, under the effect of the said simulated Newtonian forces, in response to an output from the neural network.
65 . The computer program of claim 64 , in which the actuator is a proportional derivative actuator, wherein the simulated torque, Γ T , at a time T, applied to the upper part of each leg is a function of the angle, Θ, of the upper part of each said leg relative to another body part.
66 . The computer program of claim 64 , in which the actuator is a proportional-integral-derivative actuator wherein the simulated torque, Γ T , at a time T, applied to the upper part of each leg is a function of the angle, Θ, of the upper part of each said leg relative to another body part.
67 . The computer program of claim 66 , in which the torque Γ T is related to the angle Θ in accordance with the equation
Γ
T
=
k
p
(
θ
d
-
θ
)
-
k
D
θ
t
+
k
I
∫
T
-
Δ
T
T
+
Δ
T
(
θ
d
-
θ
)
t
where k p , k D and k I are the proportional, derivative and integral coefficients, and t is time.
68 . The computer program of claim 64 , in which the actuator is simulated as a force limited velocity constraint actuator in which the torque Γ T at a time T, applied to the upper part of each of the two legs is a function of the desired velocity of movement of the upper part of a leg relative to the body trunk, and wherein the desired velocity of movement is related to a difference between the actual and the desired angle between each upper leg part and the trunk, the desired relative angle being derived from the neural network.
69 . The computer program of any one of claims 63 to 68 , further comprising one or more program elements which cause the processor to generate a simulated hip joint for each leg, to link the upper part of a given leg to the trunk and to simulate at least one actuator per leg, each actuator being arranged to act upon the hip joint associated therewith so as to cause the image of the corresponding upper leg part to move.
70 . The computer program of claim 69 , in which the or each program element is caused to simulate a single actuator for each leg.
71 . The computer program of claim 70 , in which the simulated hip joint is constrained to have a single degree of freedom such that the image of the corresponding upper leg part is constrained to move only sagitally.
72 . The computer program of any of claims 63 to 71 , further comprising one or more program elements that cause an image of a plurality of feet to be generated, each of which appears to be connected to an end of the image of the lower part of a corresponding leg via an ankle joint, and wherein the or each program element is arranged to cause the action of a damped torsional spring to be simulated so as to govern the movement of each said ankle joint.
73 . The computer program of any of claims 63 to 72 , in which the angle between the image of the upper part and the image of the lower part of each leg is limited.
74 . The computer program of any of claims 63 to 73 , in which the neurones of the neural network are arranged as a recurrent neural network (RNN) so as to form two bi-directionally coupled chains of identical oscillators, at least two of the neurones acting as motor neurones whose output controls the said actuator associated with the upper part of each said leg.
75 . The computer program of claim 74 , in which the RNN comprises a plurality of neurones arranged so as to form two bi-directionally coupled pairs of identical oscillators.
76 . The computer program of claim 75 , in which the RNN comprises one recurrent subnet per oscillator, each of which has six nodes.
77 . The computer program of claim 76 , in which each recurrent subnet in one of the chains is connected to a laterally adjacent recurrent subnet in the other of the chains via symmetrical weights so as to provide bi-directional coupling therebetween, and in which each neurone in each recurrent subnet is arranged to synapse with an equivalent neurone in the said laterally adjacent recurrent subnet.
78 . The computer program of claim 76 , in which each neurone within each recurrent subnet in one of the chains is asymmetrically (unidirectionally) connected to a corresponding neurone within a next recurrent subnet in the same chain.
79 . The computer program of any of claims 63 to 78 , further comprising one or more program elements which, when executed, determine the properties of the neurones and their connections within the said neural network on the basis of a genetic algorithm.
80 . A computer readable storage medium upon which is stored the computer program of any one of claims 63 to 79 .
81 . An electromagnetic signal carrying the computer program of any of claims 63 to 79 .
82 . A method of controlling the locomotion of a computer-generated biped, comprising the steps of:
(a) establishing a neural network having a plurality of neurones; (b) generating a computer image of a body trunk and at least two legs, each leg depending from the trunk; (c) simulating the Newtonian forces that would be acting upon each separate part of the computer-generated object at a given time; (d) applying those simulated forces to each said separate part so that, when displayed on a visual display unit, each part appears to move under the influence of those simulated forces; (e) simulating an actuator for actuation of each said leg; and (f) controlling the operation of the actuator via an output from the neural network.
83 . The method of claim 82 , in which the step (a) of establishing a neural network further comprises establishing a neural network having a plurality of neurones arranged to act as two bi-directionally coupled pairs of identical oscillators.
84 . The method of claim 82 or claim 83 , in which the step (a) of establishing a neural network further comprises establishing fully recurrent neural networks (RNNs), having a plurality of nodes, for each of the plurality of oscillators.
85 . The method of claim 84 , in which the step (a) comprises establishing RNNs each comprising six nodes.
86 . The method of claim 84 or claim 85 , further comprising connecting each RNN in one of the chains to a laterally adjacent RNN in the other of the chains via symmetrical weights so as to provide bi-directional coupling therebetween.
87 . The method of claim 86 , further comprising connecting each neurone within each RNN in one of the chains asymmetrically (uni-directionally) with a corresponding neurone within a next RNN in the same chain.
88 . The method of any of claims 82 to 87 , further comprising:
evolving the properties of the neural network via a genetic algorithm.
89 . The method of claim 88 , in which the neural network properties are evolved by:
(i) generating a plurality of different trial arrays of network property values; (ii) applying the different trial arrays to the neural network to create different trial neural networks; (iii) identifying those arrays selected from the plurality of different arrays which, when applied to the neural networks, form trial neural networks which optimize the control of the said image of the multiped in accordance with at least the criterion that the distance, d, over which an end of the lower part of a rearmost leg of the computer-generated image should have travelled from an origin should be maximized.
90 . The method of claim 89 , further comprising:
aborting, as non-optimized, the consideration of those arrays forming trial neural networks once a point attractor has been reached, such that the image when controlled by that trial network stops moving away from the origin.
91 . The method of claim 89 or claim 90 , further comprising aborting, as non-optimized, the consideration of those arrays forming trial neural networks once the centre of mass of the multiped thus controlled drops below a predetermined threshold height.
92 . The method of claim 89 , claim 90 or claim 91 . further comprising aborting, as non-optimized, the consideration of those arrays forming trial neural networks if the thus controlled multiped does not move relative to the origin.
93 . The method of any one of claims 81 to 84 , further comprising:
ranking each trial neural networks in order of fitness;
discarding those arrays representing the less fit trial neural networks;
generating further trial neural networks on the basis of the remaining trial arrays; and mutating the resulting trial arrays so as to generate a further mutated set of trial arrays;
generating further trial neural networks on the basis of the mutated trial arrays so as to allow identification of still further optimized trial arrays.
94 . The method of claim 93 , further comprising:.
applying an ancillary stability controller to the computer-generated biped so as to stabilize sagittal and/or lateral stability during initial identification of arrays; and subsequently reducing the effect of the ancillary controller prior to identification of the said still further optimized trial arrays.
95 . A method of optimizing the network parameters in a neural network controller for controlling the locomotion of a computer-generated image of a multiped, the method comprising:
(i) generating a plurality of different trial arrays of network property values; (ii) applying the different trial arrays to the neural network to create different trial neural networks; (iii) identifying those arrays selected from the plurality of different arrays which, when applied to the neural networks, form trial neural networks which optimize the control of the said image of the multiped in accordance with at least the criterion that the distance, d, over which an end of the lower part of a rearmost leg of the computer-generated image should have travelled from the origin should be maximized; the method further comprising: aborting, as non-optimized, the consideration of those arrays forming trial neural networks when at least one of the following conditions is met: (a) a point attractor is reached such that the image when controlled by that trial network stops moving away from the origin, (b) the centre of mass or trunk of the multiped, as determined by applying Newtonian mechanics to the object, drops below a threshold height; and/or (c) the multiped does not move away from the origin and/or falls over prior to moving away therefrom.
96 . The method of claim 95 , further comprising:
ranking each trial neural networks in order of fitness; discarding those arrays representing the less fit trial neural networks; generating further trial neural networks on the basis of the remaining trial arrays; and mutating the resulting trial arrays so as to generate a further mutated set of trial arrays; so as to allow identification of still further optimized trial arrays.
97 . The method of claim 96 , further comprising:
applying an ancillary stability controller to the computer-generated multiped so as to stabilize sagittal and/or lateral stability during initial identification of arrays; and subsequently reducing the effect of the ancillary controller prior to identification of the said still further optimized trial arrays.
98 . A controller for control of cyclical movement of an appendage of an artificial or computer-simulated body, the controller comprising a neural network having a plurality of neurones together arranged to act as two bi-directionally coupled arrays of identical oscillators.
99 . A computer system substantially as herein described with reference to and as illustrated in the accompanying drawings.
100 . A controller for a computer-generated image substantially as herein described with reference to and as illustrated in the accompanying drawings.
101 . A computer program substantially as herein described with reference to and as illustrated in the accompanying drawings.
102 . A method of controlling the locomotion of a computer-generated multiped substantially as herein described with reference to and as illustrated in the accompanying drawings.
103 . A method of optimizing the network parameters in a neural network controller substantially as herein described with reference to and as illustrated in the accompanying drawings.Join the waitlist — get patent alerts
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