A hybrid multi-objective evolutionary approach for optimal path planning of a hexapod robot

CARBONE, Giuseppe and DI NUOVO, Alessandro (2016). A hybrid multi-objective evolutionary approach for optimal path planning of a hexapod robot. In: BLESA, Maria J., BLUM, Christian, CANGELOSI, Angelo, CUTELLO, Vincenzo, DI NUOVO, Alessandro, PAVONE, Mario and TALBI, El-Ghazali, (eds.) Hybrid Metaheuristics : 10th International Workshop, HM 2016, Plymouth, UK, June 8-10, 2016, Proceedings. Lecture notes in computer science (9668). Springer International Publishing, 131-144.

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Link to published version:: 10.1007/978-3-319-39636-1_10


Hexapod robots are six-legged robotic systems, which have been widely investigated in the literature for various applications including exploration, rescue, and surveillance. Designing hexapod robots requires to carefully considering a number of different aspects. One of the aspects that require careful design attention is the planning of leg trajectories. In particular, given the high demand for fast motion and high-energy autonomy it is important to identify proper leg operation paths that can minimize energy consumption while maximizing the velocity of the movements. In this frame, this paper presents a preliminary study on the application of a hybrid multi-objective optimization approach for the computer-aided optimal design of a legged robot. To assess the methodology, a kinematic and dynamic model of a leg of a hexapod robot is proposed as referring to the main design parameters of a leg. Optimal criteria have been identified for minimizing the energy consumption and efficiency as well as maximizing the walking speed and the size of obstacles that a leg can overtake. We evaluate the performance of the hybrid multi-objective evolutionary approach to explore the design space and provide a designer with an optimal setting of the parameters. Our simulations demonstrate the effectiveness of the hybrid approach by obtaining improved Pareto sets of trade-off solutions as compared with a standard evolutionary algorithm. Computational costs show an acceptable increase for an off-line path planner. © Springer International Publishing Switzerland 2016.

Item Type: Book Section
Additional Information: Series ISSN: 0302-9743
Research Institute, Centre or Group: Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Sheaf Solutions
Identification Number: 10.1007/978-3-319-39636-1_10
Depositing User: Carmel House
Date Deposited: 04 Aug 2016 12:15
Last Modified: 19 Oct 2016 23:42
URI: http://shura.shu.ac.uk/id/eprint/13057

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