Dynamic Modeling, Energy Analysis, and Path Planning of Spherical Robots on Uneven Terrains

Sabet, Sahand; Poursina, Mohammad; Nikravesh, Parviz E.; Reverdy, Paul; Agha–mohammadi, Ali–akbar

doi:10.1109/lra.2020.3010489

Cited by 18 publications

(11 citation statements)

References 10 publications

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“…NeBula relies on multiple simulator configurations at different fidelity levels to enable faster and more focused development. Examples include a high-fidelity Gazebo simulator for testing ground vehicle behaviors, flight stack software-in-the-loop simulations for analyzing flight performance, a docker-based multi-robot Autonomous Rollocopter (Fan et al, 2019;Lew et al, 2019a), DrivoCopter (Kalantari et al, 2020), Baxter (Choi et al, 2020), Spherical Rollocopter (Sabet et al, 2020;Sabet et al, 2019).…”

Section: Simulation Resultsmentioning

confidence: 99%

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

Agha–mohammadi¹,

Otsu²,

Morrell³

et al. 2021

Preprint

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This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.

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Section: Simulation Resultsmentioning

confidence: 99%

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

Agha–mohammadi¹,

Otsu²,

Morrell³

et al. 2021

Preprint

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“…Furthermore, the scope of aerial vehicle designs spans as far as extra-terrestrial environments [20,21]. Control systems for such newly developed vehicles are another topic of study to improve resilience [22] or efficiency [23].…”

Section: Related Workmentioning

confidence: 99%

BAXTER: Bi-modal Aerial-Terrestrial Hybrid Vehicle for Long-endurance Versatile Mobility: Preprint Version

Choi¹,

Wee²,

Corah³

et al. 2021

Preprint

Self Cite

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Unmanned aerial vehicles are rapidly evolving within the field of robotics. However, their performance is often limited by payload capacity, operational time, and robustness to impact and collision. These limitations of aerial vehicles become more acute for missions in challenging environments such as subterranean structures which may require extended autonomous operation in confined spaces. While software solutions for aerial robots are developing rapidly, improvements to hardware are critical to applying advanced planners and algorithms in large and dangerous environments where the short range and high susceptibility to collisions of most modern aerial robots make applications in realistic subterranean missions infeasible. To provide such hardware capabilities, one needs to design and implement a hardware solution that takes into the account the Size, Weight, and Power (SWaP) constraints. This work focuses on providing a robust and versatile hybrid platform that improves payload capacity, operation time, endurance, and versatility. The Bi-modal Aerial and Terrestrial hybrid vehicle (BAXTER) is a solution that provides two modes of operation, aerial and terrestrial. BAXTER employs two novel hardware mechanisms: the M-Suspension and the Decoupled Transmission which together provide resilience during landing and crashes and efficient terrestrial operation. Extensive flight tests were conducted to characterize the vehicle's capabilities, including robustness and endurance. Additionally, we propose Agile Mode Transfer (AMT), a transition from aerial to terrestrial operation that seeks to minimize impulses during impact to the ground which is a quick and simple transition process that exploits BAXTER's resilience to impact.

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“…The parameters c i (i = 1, 2) and c j ( j = 3, 4) in (18), λ i (i = 1, 2) and λ j ( j = 3, 4) in (21), ℓ 1 and ℓ 2 in (22) and n 1 , n 2 in (24) are all positive to be designed. Taking ( 23) into (25), we can get function H 1 , H 2 of the estimation errorΞ x and Ξ y , which are…”

Section: Controller Designmentioning

confidence: 99%

“…In [3], a Beta function-based feedforward controller is established for tracing spherical contact curves and this method is verified in simulations, tracing Viviani's curve and Loxodrome. By analyzing energy consumption, based on inverse dynamics, the optimal path between two points is investigated in [22]. More recently, multiple control techniques are proposed in [11] to achieve the balance and movement of a ballbot, including robust nonlinear control, sliding mode control, and backstepping control.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Estimator-Based Sliding Mode Control For a Novel Spherical Robot With Cable Transmission Mechanism

Zhang

Ren

Zheng

2021

Preprint

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This paper presents a new spherical robot with a cable transmission mechanism. Cable transmission mechanism replaces conventional gear train to eliminate the influence of gear backlash, lower the costs on mechanical customization, and can be arranged flexibly. By projection method, the 3D robot dynamic model with structural asymmetry is decoupled into balance subsystem and velocity subsystem, and the kinetics equations are established based on Newton-Euler's law. To estimate the unknown structural dynamics in the balance subsystem and external disturbance in the velocity subsystem, adaptive law containing both control and estimation error information is proposed for the uncertainty estimator (UE) design. Then, an uncertainty estimator-based sliding mode controller (UESMC) is introduced for balance and velocity control, leading to enhanced disturbance rejection capability and a reduced steady-state error. Simulations and experiments on a real spherical robot are conducted to demonstrate the efficacy of the proposed control strategies.

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Dynamic Modeling, Energy Analysis, and Path Planning of Spherical Robots on Uneven Terrains

Cited by 18 publications

References 10 publications

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

BAXTER: Bi-modal Aerial-Terrestrial Hybrid Vehicle for Long-endurance Versatile Mobility: Preprint Version

Uncertainty Estimator-Based Sliding Mode Control For a Novel Spherical Robot With Cable Transmission Mechanism

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