Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and Aerial Robot Coordination for Search and Rescue Tasks

TL;DR

This paper introduces a terrain-aware MPC framework for heterogeneous humanoid and aerial robots, enabling safe navigation and search in rough terrains for rescue operations, with online target estimation and task allocation.

Contribution

It presents a novel integrated planning and control framework combining terrain-aware MPC, Gaussian process terrain modeling, and logic-based task allocation for heterogeneous rescue robots.

Findings

Effective navigation in rough terrains demonstrated in simulation.

Successful online estimation of rescue subjects' locations.

Robust task allocation among heterogeneous robots shown in uncertain environments.

Abstract

Humanoid robots offer significant advantages for search and rescue tasks, thanks to their capability to traverse rough terrains and perform transportation tasks. In this study, we present a task and motion planning framework for search and rescue operations using a heterogeneous robot team composed of humanoids and aerial robots. We propose a terrain-aware Model Predictive Controller (MPC) that incorporates terrain elevation gradients learned using Gaussian processes (GP). This terrain-aware MPC generates safe navigation paths for the bipedal robots to traverse rough terrain while minimizing terrain slopes, and it directs the quadrotors to perform aerial search and mapping tasks. The rescue subjects' locations are estimated by a target belief GP, which is updated online during the map exploration. A high-level planner for task allocation is designed by encoding the navigation tasks using syntactically cosafe Linear Temporal Logic (scLTL), and a consensus-based algorithm is designed for task assignment of individual robots. We evaluate the efficacy of our planning framework in simulation in an uncertain environment with various terrains and random rescue subject placements.