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Martim Brandão

8 accepted papers

2024

Characterizing Physical Adversarial Attacks on Robot Motion Planners

ICRA 2024poster

As the adoption of robots across society increases, so does the importance of considering cybersecurity issues such as vulnerability to adversarial attacks. In this paper we investigate the vulnerability of an important component of autonomous robots to adversarial attacks—robot motion planning algo…

Cited by 2SourceScholar
2024

Generating Environment-based Explanations of Motion Planner Failure: Evolutionary and Joint-Optimization Algorithms

ICRA 2024poster

Motion planning algorithms are important components of autonomous robots, which are difficult to understand and debug when they fail to find a solution to a problem. In this paper we propose a solution to the failure-explanation problem, which are automatically-generated environment-based explanatio…

Cited by 2SourceScholar
2023

Noise and Environmental Justice in Drone Fleet Delivery Paths: A Simulation-Based Audit and Algorithm for Fairer Impact Distribution

ICRA 2023poster

Despite the growing interest in the use of drone fleets for delivery of food and parcels, the negative impact of such technology is still poorly understood. In this paper we investigate the impact of such fleets in terms of noise pollution and environmental justice. We use simulation with real popul…

Cited by 5SourceScholar
2021

Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach

IROS 2021poster

In this work we propose a holistic framework for autonomous aerial inspection tasks, using semantically-aware, yet, computationally efficient planning and mapping algorithms. The system leverages state-of-the-art receding horizon exploration techniques for next-best-view (NBV) planning with geometri…

Cited by 23SourceScholar
2021

Towards providing explanations for robot motion planning

ICRA 2021poster

Recent research in AI ethics has put forth explainability as an essential principle for AI algorithms. However, it is still unclear how this is to be implemented in practice for specific classes of algorithms—such as motion planners. In this paper we unpack the concept of explanation in the context…

Cited by 37SourceScholar
2020

GaitMesh: Controller-Aware Navigation Meshes for Long-Range Legged Locomotion Planning in Multi-Layered Environments

RA-L 2020

Long-range locomotion planning is an important problem for the deployment of legged robots to real scenarios. Current methods used for legged locomotion planning often do not exploit the flexibility of legged robots, and do not scale well with environment size. In this letter we propose the use of n

Cited by 17SourceScholar
2019

Multi-controller multi-objective locomotion planning for legged robots

IROS 2019poster

Different legged robot locomotion controllers offer different advantages; from speed of motion to energy, computational demand, safety and others. In this paper we propose a method for planning locomotion with multiple controllers and sub-planners, explicitly considering the multi-objective nature o…

Cited by 13SourceScholar