• Skip to primary navigation
  • Skip to main content
  • Skip to footer
  • Career
  • Alumni
  • Employers
  • News

Fung Institute for Engineering LeadershipFung Institute for Engineering LeadershipFung Institute for Engineering Leadership

  • Master of Engineering
    • Master of Engineering Program
      • Engineering Departments
      • Program Design
      • Leadership Development
      • Capstone Experience
      • Career Development
      • Learn More
      • How to Apply
  • Fung Fellowship
    • Fung Fellowship

      The Fung Fellowship is shaping a new generation of entrepreneurial leaders focused on transforming health and wellness.

      • Program Overview
    • The Fung Fellowship
    • Executive & Professional Education
  • Partners
    • Partners
    • Become a Partner
    • Propose a Project
    • Recruit a Student
  • Apply
  • About
  • Career
  • Alumni
  • News
Adapting Humanoid Robots to Aid First Responders

Adapting Humanoid Robots to Aid First Responders

November 15, 2020 by

Team: Jamie Chen (ME), Sonny Li (ME), David Tondreau (ME), Mengyue Wang (ME)

Advisor: Koushil Sreenath (ME)

Disaster relief demands both speed and adaptability to complex terrain; however, modern robots, which offer the potential to aid first responders, are currently specialized either for speed (wheeled robots) or for adaptability (legged robots). Our team is working to enable the transition of bipedal robots between legged locomotion and wheeled transportation. Our approach is to develop a full-stack autonomous system with perception to recognize the hovershoe, trajectory optimization for precise foot placement, and a series of inner controllers to realize the trajectory.

Perception:

The perception module must both identify the location where the robot must place its foot and localize the robot in 3D space. We proposed a two-camera system where each camera has a unique orientation to simultaneously achieve the aforementioned tasks while meeting the requirements of the robot’s system infrastructure.

Gait Synthesis:

A library of gaits is computed offline by leveraging C-FROST, a direct-collocation optimization tool created for hybrid systems. The optimization problem employs the IPOPT numerical solver with an objective function to minimize control effort and pelvis movement under constraints to obtain gaits.

Control:

To realize the optimal gaits, inverse kinematics transformed the desired virtual leg coordinates to joint angles. To enforce the joint angles, PD control was used to calculate the required motor torques. Last, a finite state machine switched between the walking, transition, and riding controllers.

We implemented a standalone perception module, integrated gait optimization with a low-level controller, and achieved discrete walking in simulation. Our contribution established the foundation of a multi-year project for Cassie Cal to aid first responders.

Project Brief

Recent News:

Q&A with the capstone winners of 2020 Fung Institute Mission Award

Q&A with the capstone winners of 2020 Fung Institute Mission Award

← View all Capstone Projects

Fung Institute For Engineering Leadership
Shires Hall
2451 Ridge Road Berkeley, CA 94709

Mudd Hall
1798 Scenic Avenue Berkeley, CA 94709

(510) 642-0633
funginstitute@berkeley.edu

Explore

  • Programs
  • Partners
  • Apply
  • Feedback
  • Job Opportunities

Experience

  • About
  • Career
  • Alumni
  • News
  • Donate

Connect

Copyright © 2023 Accessibility • Nondiscrimination • Privacy • Sitemap

berkeley_engineering

uc-berkeley

Copyright © 2023 Accessibility • Nondiscrimination • Privacy • Sitemap

berkeley_engineering

uc-berkeley

Prospective MEng Students

Sign up for our mailing list to receive program news and updates including information sessions, class visits and opportunities to connect with an admissions advisor.