Risk-Sensitive Robot Navigation Control for Collision Avoidance
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Solution Overview
Problem
Current approaches for autonomous robots to navigate safely in dynamic environments, avoiding collisions with other agents, face challenges due to non-Gaussian noise, nonlinear dynamics, and computational inefficiencies, making risk-sensitive control elusive.
Innovation Solution
A risk-sensitive sequential action control (RSSAC) algorithm is implemented, using an entropic risk measure and a generative model for predicting multi-agent trajectories, which integrates risk sensitivity into control sequences to optimize navigation and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control approaches (chance-constrained optimization, CVaR, entropic risk) are used for risk-sensitive navigation, then collision avoidance capability is improved, but computational efficiency deteriorates due to non-Gaussian noise, nonlinear dynamics, and non-continuity
Solution Approach 1:
The patent transforms the risk-sensitive control problem by changing the parameter representation from non-Gaussian noise models to Gaussian noise models with modified cost functions. This allows the use of efficient quadratic programming while maintaining risk sensitivity through carefully designed cost terms that capture collision risks without requiring computationally intensive non-Gaussian simulations.
Solution Approach 2:
The patent replaces complex mechanical control approaches (chance-constrained optimization, CVaR) with a streamlined model-predictive control framework that uses Gaussian process models for prediction and quadratic programming for optimization. This substitution maintains the essential risk-avoidance functionality while achieving superior computational efficiency through mathematically tractable formulations.
2Reliability
If risk-sensitive control is incorporated into autonomous robot navigation, then navigation safety is improved, but system complexity increases due to non-continuity and non-Gaussian noise handling
Solution Approach 1:
The patent simplifies the control system by changing from complex non-Gaussian noise models to Gaussian noise models with risk-sensitive cost functions. This parameter transformation maintains navigation safety through proper cost function design while dramatically reducing system complexity by enabling the use of standard quadratic programming solvers instead of requiring custom non-continuous optimization routines.
Solution Approach 2:
The patent segments the navigation problem into distinct modules: Gaussian process prediction for trajectory forecasting, cost function evaluation for risk assessment, and quadratic programming for control optimization. This segmentation allows each module to be independently optimized and solved using efficient, well-established algorithms, reducing overall system complexity while maintaining comprehensive risk-sensitive navigation capabilities.
3Speed
If sampling-based nonlinear model-predictive control with entropic risk measure is implemented, then real-time navigation performance is improved, but computational load increases due to multiple trajectory sampling
Solution Approach 1:
The patent changes the computational parameters by using Gaussian process models that provide analytically tractable predictions and uncertainty estimates. This allows the system to efficiently evaluate multiple potential trajectories and their associated risks without requiring intensive Monte Carlo simulations, thereby reducing computational energy consumption while maintaining real-time navigation performance through closed-form solutions.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to improving controls in a device according to risk. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying objects from the sensor data that are present in the surrounding environment. The method includes generating a control sequence for controlling the device according to a risk-sensitivity parameter to navigate toward a destination while considering risk associated with encountering the objects defined by the risk-sensitivity parameter. The method includes controlling the device according to the control sequence.