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Space Robotics and Autonomous Systems: Technologies, advances and applications [Kõva köide]

Edited by (University of Surrey, Surrey Space Centre, UK)
  • Formaat: Hardback, 486 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 19-Oct-2021
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1839532254
  • ISBN-13: 9781839532252
Teised raamatud teemal:
Space Robotics and Autonomous Systems: Technologies, advances and applicationsSuurem pilt
  • Formaat: Hardback, 486 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 19-Oct-2021
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1839532254
  • ISBN-13: 9781839532252
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781839532252.html
Märksõnad:

Space robotics and autonomous systems (Space RAS) play a critical role in the current and future development of mission-defined machines that can survive in space while performing exploration, assembly, construction, maintenance and servicing tasks. They represent a multi-disciplinary emerging field at the intersection of space engineering, terrestrial robotics, computer science and materials. The field is essential to humankind's ability to explore or operate in space; providing greater access beyond human spaceflight limitations in the harsh environment of space, and offering greater operational handling that extends astronauts' capabilities. Space RAS covers all types of robotics for the exploration of planet surfaces as well as robotics used in orbit around the Earth and the sensors needed by the platform for navigation or control.

Written by a team of International experts on space RAS, this book covers advanced research, technologies and applications including: sensing and perception to provide situational awareness for space robotic agents, explorers and assistants; mobility to reach and operate at sites of scientific interest on extra-terrestrial surfaces or free space environments using locomotion; manipulations to make intentional changes in the environment or objects using locomotion such as placing, assembling, digging, trenching, drilling, sampling, grappling and berthing; high-level autonomy for system and sub-systems to provide robust and safe autonomous navigation, rendezvous and docking capabilities and to enable extended-duration operations without human interventions to improve overall performance of human and robotic missions; human-robot interaction and multi-modal interaction; system engineering to provide a framework for understanding and coordinating the complex interactions of robots and achieving the desired system requirements; verification and validation of complex adaptive systems; modelling and simulation; and safety and trust.



This edited book covers space robotics and autonomous systems (space RAS) from technologies to advances and applications including sensing and perception, mobility, manipulations, high-level autonomy, human-robot interaction, multi-modal interaction, modelling and simulation, and safety and trust.

List of figures
xvii
List of tables
xxix
Foreword xxxi
Alistair Scott
About the editor xxxv
1 Introduction
1(12)
Yang Gao
1.1 Technologies
1(2)
1.2 Applications
3(1)
1.3 Recent advances
4(6)
1.3.1 Part I: mobility and mechanisms
5(1)
1.3.2 Part II: sensing, perception, and GNC
6(2)
1.3.3 Part III: astronaut-robot interaction
8(1)
1.3.4 Part IV: system engineering
9(1)
1.4 Acknowledgements
10(3)
References
10(3)
PART I Mobility and mechanisms
2 Wheeled planetary rover locomotion design, scaling, and analysis
13(30)
Andrew Thoesen
Hamidreza Marvi
2.1 Background: modeling the granular environment
13(4)
2.2 Methods
17(6)
2.2.1 Theory: wheel parameter scaling for output parameters of mechanical power and translational velocity in granular media
17(4)
2.2.2 Experimentation: planetary regolith simulants and testing environments
21(2)
2.3 Studies giving context to scaling theory
23(13)
2.3.1 Study one: mechanical power and translational velocity prediction variance by granular material and wheel shape
23(3)
2.3.2 Study two: mechanical power prediction variance by mass, velocity, and motor placement
26(3)
2.3.3 Study three: context of deviations and examination of scaling law application sinkage threshold
29(3)
2.3.4 Study four: investigating gravity-variant scaling using MBD-DEM simulations
32(4)
2.4 Recommendations and future work
36(7)
References
38(5)
3 Compliant pneumatic muscle structures and systems for extra-vehicular and intra-vehicular activities in space environments
43(34)
Samuel Wandai Khara
Alaa Al-Ibadi
Hassanin S. H. Al-Fahaam
Haitham El-Hussieny
Steve Davis
Samia-Nefti Meziani
Olivier Patrouix
3.1 Introduction
43(1)
3.2 Robotic solutions for space environments
44(3)
3.2.1 Soft robotic systems as an alternative robotic solution for space environments
45(2)
3.3 Soft robotic systems based on PMA as an alternative to rigid robotic systems for space environments
47(11)
3.3.1 Modeling of a pneumatic muscle actuator
49(2)
3.3.2 Characterization of contractor PMA
51(1)
3.3.3 Analysis of contractor PMA
52(5)
3.3.4 Modeling of extensor PMA
57(1)
3.4 PMA designs that can be adapted as robotic manipulators for space
58(7)
3.4.1 Self-bending contraction actuator and extensor-bending pneumatic artificial muscles
59(2)
3.4.2 Double-bending pneumatic muscle actuator
61(2)
3.4.3 Extensor-contraction pneumatic muscle actuator
63(2)
3.4.4 Circular pneumatic muscle actuator
65(1)
3.5 PMA applications in developing novel grippers, manipulators, and power assistive glove for space environments
65(7)
3.5.1 Three fingers gripper base on SBCA
66(1)
3.5.2 Extension-circular gripper
67(1)
3.5.3 Three CPMAs gripper
68(1)
3.5.4 Soft robot manipulators
69(2)
3.5.5 Power assistive soft glove
71(1)
3.6 Recommendations and future works
72(5)
References
74(3)
4 Biologically-inspired mechanisms for space applications
77(48)
Craig Pitcher
Mohamed Alkalla
Xavier Pang
Yang Gao
4.1 Subsurface exploration
78(3)
4.1.1 Ovipositor drilling
78(2)
4.1.1.1 Dual-reciprocating drill
80(1)
4.1.2 Peristaltic motion
81(1)
4.2 Surface mobility inspired by animals
81(7)
4.2.1 Gecko and spider adhesion
82(1)
4.2.1.1 Waalbot
83(1)
4.2.1.2 Abigaille
84(1)
4.2.1.3 Legged excursion mechanical utility rover
84(1)
4.2.1.4 Additional concepts
85(1)
4.2.2 Legged locomotion
85(1)
4.2.2.1 Abigaille
85(1)
4.2.2.2 SCORPION
86(1)
4.2.2.3 Additional concepts
86(1)
4.2.3 Hopping locomotion
87(1)
4.3 Object capture
88(2)
4.3.1 Adhesive grippers
88(1)
4.3.2 Kangaroo vibration suppression
89(1)
4.4 Mobility inspired by plants
90(3)
4.4.1 Seed dispersal
90(1)
4.4.1.1 Mars Tumbleweed
90(1)
4.4.2 Vine and tendril climbing
91(1)
4.4.2.1 Tendril
92(1)
4.4.3 Plant root growth
93(1)
4.5 Artificial muscle actuators
93(2)
4.5.1 Ionic polymer metal composites
93(1)
4.5.2 Dielectric elastomers
94(1)
4.6 Aerial mobility
95(1)
4.6.1 Wing-flapping mechanisms
95(1)
4.7 Navigation systems for mobility
96(5)
4.7.1 Natural and invasive interfacing
96(2)
4.7.1.1 Insect/machine hybrid controller
98(1)
4.7.2 Honeybee optics
98(1)
4.7.2.1 Bio-inspired engineering of exploration systems
99(1)
4.7.3 Optic flow landing
99(1)
4.7.3.1 Elementary motion detectors
100(1)
4.7.3.2 Additional concepts
100(1)
4.8 Multi-agent spacecraft system architectures
101(4)
4.8.1 Swarm intelligence
101(1)
4.8.1.1 Autonomous Nano Technology Swarm
102(1)
4.8.1.2 Additional concepts
102(1)
4.8.2 Cellular spacecraft architecture
103(1)
4.8.2.1 Cell apoptosis
103(1)
4.8.2.2 Satellite Stem Cell
104(1)
4.9 Hibernation for human spaceflight
105(1)
4.10 Summary and future
105(20)
References
108(17)
PART II Sensing, perception and GNC
5 Autonomous visual navigation for spacecraft on-orbit operations
125(34)
Arunkumar Rathinam
Zhou Hao
Yang Gao
5.1 Introduction
125(3)
5.2 Theoretical foundation
128(6)
5.2.1 The equations of relative motion
128(3)
5.2.2 Camera pose estimation
131(1)
5.2.3 Relative pose estimation
132(1)
5.2.4 Emerging trends
133(1)
5.3 Deep-learning-based spacecraft pose estimation
134(5)
5.3.1 Keypoint-based pose estimation
134(1)
5.3.1.1 Object detection
134(2)
5.3.1.2 Landmark regression
136(1)
5.3.1.3 PnP + RANSAC
137(1)
5.3.2 Non-keypoint-based pose estimation
138(1)
5.4 Advancements in simulators and experimental testbeds
139(11)
5.4.1 Digital simulators
139(3)
5.4.2 Ground-based physical testbeds
142(4)
5.4.3 The methodology of simulating relative motion
146(4)
5.5 Analytical results and comparison
150(2)
5.6 Recommendations and future trends
152(7)
References
154(5)
6 Inertial parameter identification, reactionless path planning and control for orbital robotic capturing of unknown objects
159(44)
Chu Zhongyi
Hai Xiao
Ma Ye
6.1 Introduction
160(5)
6.1.1 Relative work and development status
162(1)
6.1.1.1 Method for identifying inertial parameters of space non-cooperative targets
162(1)
6.1.1.2 Reactionless path planning for non-cooperative objects capture
163(2)
6.1.1.3 Attitude stable control method of spacecraft-manipulator-target system
165(1)
6.2 Joint kinetic model of spacecraft and unknown object
165(4)
6.2.1 System kinematic analysis
166(1)
6.2.1.1 System position vector analysis
166(1)
6.2.1.2 System velocity vector analysis
166(1)
6.2.1.3 Velocity Jacobian matrix
167(1)
6.2.1.4 System linear and angular momentum calculation
167(1)
6.2.2 System kinetic analysis
168(1)
6.3 Unknown object inertial parameter identification
169(6)
6.3.1 Basic theory of identification
169(3)
6.3.2 Identification scheme incorporating information of contact force together with force/torque of end-effector
172(2)
6.3.3 Solution of the modified identification equation using the hybrid RLS-APSA algorithm
174(1)
6.4 Adaptive reactionless control strategy during manipulation of unknown obj ect
175(5)
6.4.1 Adaptive reactionless path planning via SW-RLS
175(2)
6.4.2 Robust adaptive control strategy via the PSO-ELM algorithm
177(1)
6.4.2.1 Adaptive control term via PSO-ELM algorithm
177(2)
6.4.2.2 Robust control strategy
179(1)
6.4.2.3 Stability analysis of the proposed control strategy
180(1)
6.5 Numerical simulation
180(12)
6.5.1 Inertial parameter identification simulation
180(6)
6.5.2 Path planning and control simulation
186(6)
6.6 Experimental results
192(5)
6.7 Recommendations and future work
197(6)
References
198(5)
7 Autonomous robotic grasping in orbital environment
203(34)
Nikos Mavrakis
Yang Gao
7.1 Introduction
203(1)
7.2 Human grasping in space
204(2)
7.3 Applications of orbital grasping
206(3)
7.3.1 On-Orbit Servicing
206(1)
7.3.2 In-space telescope assembly
207(1)
7.3.3 Active debris removal
207(1)
7.3.4 Astronaut-robot interaction
208(1)
7.4 Robotic hardware for orbital grasping
209(9)
7.4.1 Coupling interfaces
210(3)
7.4.2 Engine nozzle probing
213(1)
7.4.3 Robotic grapples
214(2)
7.4.4 Dexterous hands
216(2)
7.5 Latest R & D on orbital grasping
218(7)
7.5.1 Alternative gripper designs
218(2)
7.5.2 Adhesive grasping
220(1)
7.5.3 Affordance-based grasping
221(1)
7.5.4 Grasp synthesis
222(3)
7.6 Related missions
225(2)
7.6.1 ETS-7
225(1)
7.6.2 OSAM-1
225(1)
7.6.3 ELSA-d
226(1)
7.6.4 MEV-1
226(1)
7.6.5 ClearSpace-1
226(1)
7.7 Technical challenges of orbital grasping
227(10)
7.7.1 Algorithmic modelling -- design challenges
227(1)
7.7.1.1 Target state estimation
227(1)
7.7.1.2 Identification of grasping point
227(1)
7.7.1.3 Grasp analysis and modelling
227(1)
7.7.1.4 Machine learning
228(1)
7.7.1.5 Gripper design
228(1)
7.7.2 Physical challenges
228(1)
7.7.2.1 Space environment
228(1)
7.7.2.2 Impact mitigation
228(1)
7.7.2.3 Debris generation
229(1)
7.7.3 Operational -- verification challenges
229(1)
7.7.3.1 Post-capture operations
229(1)
7.7.3.2 Standardisation and benchmarking
229(1)
7.7.3.3 Verification and validation
229(1)
References
230(7)
PART III Astronaut-robot interaction
8 BCI for mental workload assessment and performance evaluation in space teleoperations
237(38)
Fani Deligianni
Daniel Freer
Yao Guo
Guang-Zhong Yang
8.1 Human--robot interaction in space -- what we learn from simulators
237(6)
8.1.1 Soyuz-TMA
239(1)
8.1.2 Canadarm2 and Dextre
240(3)
8.2 Cognitive models underlying neuroergonomics in space flight
243(2)
8.2.1 Neuroergonomics and spatial attention
244(1)
8.3 Workload and performance measures in human-robot collaborative tasks
245(3)
8.4 BCIs in workload and attention
248(10)
8.4.1 EEG-based BCI
248(3)
8.4.2 Fnirs-based BCI
251(1)
8.4.3 Eye-tracking-based BCI
252(1)
8.4.3.1 Point of gaze and eye movements
252(1)
8.4.3.2 Eye-tracking systems
253(2)
8.4.3.3 Eye-tracking-based mental workload detection
255(1)
8.4.3.4 Eye-tracking-based skill assessment
256(1)
8.4.4 Neurolmaging in space
256(2)
8.5 Artificial intelligence in BCI-based workload detection
258(2)
8.6 Cognitive workload estimation during simulated teleoperations -- a case study
260(6)
8.7 Recommendations and future work
266(9)
References
268(7)
9 Physiological adaptations in space and wearable technology for biosignal monitoring
275(66)
Shamas U. E. Khan
Bruno G. Rosa
Panagiotis Kassanos
Claire F. Miller
Fani Deligianni
Guang-Zhong-Yang
9.1 Introduction
275(3)
9.2 Cardiovascular system
278(14)
9.2.1 Blood pressure, haemodynamic response and orthostatic intolerance
278(1)
9.2.1.1 Heart rate, blood pressure and cardiac output
278(2)
9.2.1.2 Central venous pressure and hypovolemia
280(2)
9.2.1.3 Orthostatic intolerance
282(1)
9.2.2 Electrocardiographic variations
283(1)
9.2.3 Cardiac remodelling in space
284(1)
9.2.4 Vascular function and cell adaptations in space
285(1)
9.2.5 Jugular venous blood flow and thrombus formation
286(1)
9.2.6 Biomarkers of cardiovascular diseases
287(2)
9.2.7 Cardiovascular disease mortality and radiation risks in astronauts
289(3)
9.3 Other physiological adaptations in microgravity
292(14)
9.3.1 Gastrointestinal system and nutrition
292(2)
9.3.2 Respiratory system
294(1)
9.3.3 Brain and peripheral nervous system
295(1)
9.3.3.1 Adaptations to neuro-vestibular, visual and somatosensory systems
295(3)
9.3.4 Thermoregulation in space
298(1)
9.3.5 The stress response in astronauts
299(2)
9.3.6 Lymphatic and urinary systems
301(2)
9.3.7 Endocrine system
303(2)
9.3.7.1 Sweat as a biosignalling fluid
305(1)
9.4 Musculoskeletal system modifications in space
306(6)
9.4.1 Muscle atrophy in space
306(2)
9.4.2 Bone demineralization
308(2)
9.4.3 Markers of bone health
310(1)
9.4.4 Bone health monitoring
311(1)
9.4.5 Cartilage
311(1)
9.5 Wearable technology for space biosignal monitoring
312(12)
9.5.1 Wearable systems for thermoregulation
321(3)
9.6 Recommendations and future trends
324(17)
References
325(16)
10 Future of human-robot interaction in space
341(36)
Stephanie Sze Ting Pau
Judith-Irina Buchheim
Daniel Freer
Guang-Zhong Yang
10.1 The challenge of human-robot interaction in space
342(15)
10.1.1 Humans, the complexity of spaceoperations
344(4)
10.1.2 Space robots, a technological challenge
348(5)
10.1.3 Interaction, theory and practice
353(4)
10.2 Future of interaction with autonomous robotics in space
357(6)
10.2.1 Motivations for shared autonomy
357(2)
10.2.2 Capabilities for the future of interaction
359(1)
10.2.2.1 Research on signifiers from human agent -- sensors and neuro-ergonomics
360(1)
10.2.2.2 Research on natural mapping and feedback mechanisms -- embodied interaction/humanoids
360(1)
10.2.2.3 Research to support human capabilities -- crew autonomy
361(1)
10.2.2.4 Research of different interaction paradigms of human--robot teaming
361(1)
10.2.2.5 Research to simulate operation realism and pressure -- working with time delay
362(1)
10.3 Case study: a future crew assistant
363(4)
10.3.1 CIMON® -- the intelligent astronaut assistant
363(1)
10.3.1.1 Hypothesis
363(1)
10.3.1.2 Implementation as fast track experiment for the horizons mission
364(1)
10.3.1.3 Functionalities of CIMON on board
365(1)
10.3.2 The case for crew assistance robot -- for space and earth
366(1)
10.4 Recommendations and trends
367(1)
10.5 References
368(9)
PART IV System engineering
11 Verification for space robotics
377(32)
Rafael C. Cardoso
Marie Farrell
Georgios Kourtis
Matt Webster
Louise A. Dennis
Clare Dixon
Michael Fisher
Alexei Lisitsa
11.1 Formal specification and verification techniques
378(2)
11.1.1 Formal specification and verification for autonomous robotic systems
378(1)
11.1.1.1 Methodology
378(1)
11.1.1.2 Answering RQ1: challenges
379(1)
11.1.1.3 Answering RQ2: formalisms, tools and approaches
379(1)
11.1.1.4 Answering RQ3: limitations
380(1)
11.1.1.5 Application to space robotics
380(1)
11.2 Theorem proving for space robotics using modal and temporal logics
380(2)
11.2.1 The multi-modal logic K
381(1)
11.2.2 Metric temporal logic
382(1)
11.3 Verifiable space robot architectures
382(4)
11.3.1 FOL contract specifications
383(1)
11.3.2 Measuring confidence in verification
384(1)
11.3.3 Related work
385(1)
11.4 Case study 1: Simulation and verification of the Mars Curiosity rover
386(6)
11.4.1 Simulation
387(4)
11.4.2 Model checking
391(1)
11.4.3 Runtime verification
391(1)
11.5 Case study 2: Verification of astronaut-rover teams
392(4)
11.6 Modelling and verification of multi-objects systems
396(7)
11.6.1 Motivation
396(2)
11.6.2 Logics for parameterised systems
398(2)
11.6.3 Translating broadcast protocols to MFOTL
400(3)
11.7 Conclusions, recommendations and future trends
403(6)
References
403(6)
12 Cyber security of new space systems
409(21)
Carsten Maple
Ugur Ilker Atmaca
Gregory Epiphaniou
Gregory Falco
Hu Yuan
12.1 A reference architecture for attack surface analysis in space systems
410(3)
12.2 Threat modelling
413(9)
12.2.1 Cyber security requirements
418(2)
12.2.2 Evaluation of threat modelling approaches
420(2)
12.3 Risk management
422(4)
12.4 Security-minded verification of space systems
426(3)
12.4.1 Security-minded verification methodology
426(3)
12.5 Discussion
429(1)
12.6 Conclusion
430(1)
References 430(7)
Index 437
Yang Gao is the Professor of Space Autonomous Systems at Surrey Space Centre of the University of Surrey, UK. Prof. Gao founded and heads the multi-awards winning Space Technology for Autonomous and Robotic systems Laboratory (STAR LAB), which specializes in robotic sensing, perception, visual GNC and biomimetic mechanisms for industrial applications in extreme environments. She has been the Principal Investigator of internationally teamed projects funded by UK Research Councils, InnovateUK, Royal Academy of Engineering, European Commission, European Space Agency (ESA), UK Space Agency, as well as industrial companies. She has also been actively involved in real-world space missions such as ESA's ExoMars, Proba3 and VMMO, UK's MoonLITE/Moonraker, and CNSA Chang'E3.



Prof. Gao is an elected Fellow of the Institute of Engineering and Technology (IET) and the Royal Aeronautical Society (RAeS). She was named by the Times Higher Education in 2008 as one of ten UK's young leading academics who are making a very significant contribution to their disciplines, and was also awarded the Mulan Award in 2019 for Contributions to Science, Technology and Engineering. Research work under her leadership and supervision has also received many international recognitions such as the IAF's 3AF Edmond Brun Silver Medal, COSPAR's Outstanding Paper Award, Top places in ESA Grand Challenges, etc.



Prof. Gao holds a B. Eng (1st Hons) and Ph.D. on electrical and control engineering from the Nanyang Technological University, Singapore.