28 research outputs found
MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT
This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions:
First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control.
Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle.
Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence.
The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios
A Bio-inspired Distributed Control Architecture: Coupled Artificial Signalling Networks
This thesis studies the applicability of computational models inspired by the structure and dynamics of signalling networks to the control of complex control problems. In particular, this thesis presents two different abstractions that aim to capture the signal processing abilities of biological cells: a stand-alone signalling network and a coupled signalling network. While the former mimics the interacting relationships amongst the components in a signalling pathway, the latter replicates the connectionism amongst signalling pathways. After initially investigating the feasibility of these models for controlling two complex numerical dynamical systems, Chirikov's standard map and the Lorenz system, this thesis explores their applicability to a difficult real world control problem, the generation of adaptive rhythmic locomotion patterns within a legged robotic system. The results highlight that the locomotive movements of a six-legged robot could be controlled in order to adapt the robot's trajectory in a range of challenging environments. In this sense, signalling networks are responsible for the robot adaptability and inter limb coordination as they self-adjust their dynamics according to the terrain's irregularities. More generally, the results of this thesis highlight the capacity of coupled signalling networks to decompose non-linear problems into smaller sub-tasks, which can then be independently solved
Versatile Locomotion Control of a Hexapod Robot Using a Hierarchical Network of Nonlinear Oscillator Circuits
A novel hierarchical network based on coupled nonlinear oscillators is proposed for motor pattern generation in hexapod robots. Its architecture consists of a central pattern generator (CPG), producing the global leg coordination pattern, coupled with six local pattern generators, each devoted to generating the trajectory of one leg. Every node comprises a simple nonlinear oscillator and is well-suited for implementation in a standard field-programmable analog array device. The network enables versatile locomotion control based on five high-level parameters which determine the inter-oscillator coupling pattern via simple rules. The controller was realized on dedicated hardware, deployed to control an ant-like hexapod robot, and multi-sensory telemetry was performed. As a function of a single parameter, it was able to stably reproduce the canonical gaits observed in six-legged insects, namely the wave, tetrapod, and tripod gaits. A second parameter enabled driving the robot in ant-like and cockroach-like postures. Three further parameters enabled inhibiting and resuming walking, steering, and producing uncoordinated movement. Emergent phenomena were observed in the form of a multitude of intermediate gaits and of hysteresis and metastability close to a point of gait transition. The primary contributions of this paper reside in the hierarchical controller architecture and associated approach for collapsing a large set of low-level parameters, stemming from the complex hexapod kinematics, into only five high-level parameters. Such parameters can be changed dynamically, an aspect of broad practical relevance opening new avenues for driving hexapod robots via afferent signals from other circuits representing higher brain areas, or by means of suitable brain-computer interfaces. An additional contribution is the detailed characterization via telemetry of the physical robot, involving the definition of parameters which may aid future comparison with other controllers. The present results renew interest into analog CPG architectures and reinforce the generality of the connectionist approach
Biologically-inspired control framework for insect animation.
Insects are common in our world, such as ants, spiders, cockroaches etc. Virtual representations of them have wide applications in Virtual Reality (VR), video games and films. Compared with the large volume of works in biped animation, the problem of insect animation was less explored. Their small body parts, complex structures and high-speed movements challenge the standard techniques of motion synthesis. This thesis addressed the aforementioned challenge by presenting a framework to efficiently automate the modelling and authoring of insect locomotion. This framework is inspired by two key observations of real insects: fixed gait pattern and distributed neural system. At the top level, a Triangle Placement Engine (TPE) is modelled based on the double-tripod gait pattern of insects, and determines the location and orientation of insect foot contacts, given various user inputs. At the low level, a Central Pattern Generator (CPG) controller actuates individual joints by mimicking the distributed neural system of insects. A Controller Look-Up Table (CLUT) translates the high-level commands from the TPE into the low-level control parameters of the CPG. In addition, a novel strategy is introduced to determine when legs start to swing. During high-speed movements, the swing mode is triggered when the Centre of Mass (COM) steps outside the Supporting Triangle. However, this simplified mechanism is not sufficient to produce the gait variations when insects are moving at slow speed. The proposed strategy handles the case of slow speed by considering four independent factors, including the relative distance to the extreme poses, the stance period, the relative distance to the neighbouring legs, the load information etc. This strategy is able to avoid the issues of collisions between legs or over stretching of leg joints, which are produced by conventional methods. The framework developed in this thesis allows sufficient control and seamlessly fits into the existing pipeline of animation production. With this framework, animators can model the motion of a single insect in an intuitive way by specifying the walking path, terrains, speed etc. The success of this framework proves that the introduction of biological components could synthesise the insect animation in a naturalness and interactive fashion
Pattern generators with sensory feedback for the control of quadruped locomotion
Central Pattern Generators (CPGs) are becoming a popular model for the control of locomotion of legged robots. Biological CPGs are neural networks responsible for the generation of rhythmic movements, especially locomotion. In robotics, a systematic way of designing such CPGs as artificial neural networks or systems of coupled oscillators with sensory feedback inclusion is still missing. In this contribution, we present a way of designing CPGs with coupled oscillators in which we can independently control the ascending and descending phases of the oscillations (i.e. the swing and stance phases of the limbs). Using insights from dynamical system theory, we construct generic networks of oscillators able to generate several gaits under simple parameter changes. Then we introduce a systematic way of adding sensory feedback from touch sensors in the CPG such that the controller is strongly coupled with the mechanical system it controls. Finally we control three different simulated robots (iCub, Aibo and Ghostdog) using the same controller to show the effectiveness of the approach. Our simulations prove the importance of independent control of swing and stance duration. The strong mutual coupling between the CPG and the robot allows for more robust locomotion, even under non precise parameters and non-flat environmen
Chaotic exploration and learning of locomotion behaviours
We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage
Chaotic exploration and learning of locomotor behaviours
Recent developments in the embodied approach to understanding the generation of
adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic
motor patterns should not be done in isolation from an appreciation, and indeed
exploitation, of neural-body-environment interactions. Utilising spontaneous mutual
entrainment between neural systems and physical bodies provides a useful passage
to the regions of phase space which are naturally structured by the neuralbody-
environmental interactions. A growing body of work has provided evidence
that chaotic dynamics can be useful in allowing embodied systems to spontaneously
explore potentially useful motor patterns. However, up until now there has
been no general integrated neural system that allows goal-directed, online, realtime
exploration and capture of motor patterns without recourse to external monitoring,
evaluation or training methods. For the first time, we introduce such a system
in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics,
for the exploration and learning of the possible locomotion patterns of an articulated
robot of an arbitrary morphology in an unknown environment. The controller
is modelled as a network of neural oscillators which are coupled only through physical
embodiment, and goal directed exploration of coordinated motor patterns is
achieved by a chaotic search using adaptive bifurcation. The phase space of the
indirectly coupled neural-body-environment system contains multiple transient or
permanent self-organised dynamics each of which is a candidate for a locomotion
behaviour. The adaptive bifurcation enables the system orbit to wander through
various phase-coordinated states using its intrinsic chaotic dynamics as a driving
force and stabilises the system on to one of the states matching the given goal
criteria. In order to improve the sustainability of useful transient patterns, sensory
homeostasis has been introduced which results in an increased diversity of motor outputs,
thus achieving multi-scale exploration. A rhythmic pattern discovered by this
process is memorised and sustained by changing the wiring between initially disconnected
oscillators using an adaptive synchronisation method. The dynamical nature
of the weak coupling through physical embodiment allows this adaptive weight learning
to be easily integrated, thus forming a continuous exploration-learning system.
Our result shows that the novel neuro-robotic system is able to create and learn a
number of emergent locomotion behaviours for a wide range of body configurations
and physical environment, and can re-adapt after sustaining damage. The implications
and analyses of these results for investigating the generality and limitations of
the proposed system are discussed