Reconfigurable Motion Planning Processor for Risk-Aware Collision Avoidance
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Solution Overview
Problem
Existing motion planning algorithms for autonomous vehicles are computationally intensive and struggle with real-time collision detection and path planning in dynamic environments, particularly when dealing with uncertain movements of dynamic obstacles.
Innovation Solution
A specialized reconfigurable processor and motion planning module that performs collision detection and path planning entirely in hardware or software, or a combination of both, using a planning graph that adjusts probabilities of collision based on perception data and trajectory samples to generate a risk-aware motion plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion planning algorithms perform real-time collision detection and path planning in dynamic environments, then the ability to avoid collisions and navigate obstacles is improved, but the computational load increases significantly
Solution Approach 1:
The motion planning system is segmented into multiple independent modules: perception module for environmental sensing, prediction module for obstacle trajectory forecasting, planning module for path generation, and control module for execution. Each module processes specific tasks independently, reducing the computational burden on any single component while maintaining overall system reliability for collision avoidance
Solution Approach 2:
The system performs preliminary actions by pre-processing environmental data through perception algorithms, pre-predicting obstacle trajectories using motion models, and pre-generating multiple candidate paths before actual navigation decisions are required. This advance preparation reduces real-time computational load during critical collision avoidance scenarios
2Adaptability or versatility
If motion planning algorithms process perception data and adjust collision probabilities in real-time, then the ability to handle uncertain dynamic obstacles is improved, but the processing time increases
Solution Approach 1:
The system implements periodic action by updating obstacle predictions and collision probabilities at fixed time intervals rather than continuously processing all data. Perception data is sampled periodically, trajectory predictions are refreshed at regular intervals, and path planning is re-executed periodically, reducing processing time while maintaining adaptability to dynamic obstacles through consistent periodic updates
Solution Approach 2:
The system applies partial action by selectively processing only the most relevant perception data and obstacle trajectories that significantly impact collision risk, rather than processing all available data equally. It focuses computational resources on high-priority dynamic obstacles with uncertain movements, achieving adequate adaptability with reduced processing time by ignoring low-priority elements
3Speed
If specialized hardware circuitry is used for motion planning operations, then the speed of collision detection and path finding is improved, but the power consumption increases
Solution Approach 1:
The system employs dynamic configuration of hardware resources, activating specialized hardware circuitry for motion planning operations only when required by the navigation task, rather than maintaining constant high-speed processing capability. The hardware can be dynamically reconfigured between different planning algorithms and obstacle detection modes based on real-time environmental complexity, achieving high speed when needed while reducing power consumption during simpler navigation scenarios
Solution Approach 2:
The system replaces general-purpose software-based motion planning with specialized hardware circuitry implemented through reconfigurable processors or FPGAs. This substitution provides accelerated collision detection and path finding speeds through dedicated hardware logic, while the reconfigurable nature allows the hardware to be optimized for specific algorithms, improving speed efficiency relative to the computational task at hand
4Adaptability or versatility
If a reconfigurable processor is used to implement motion planning, then the adaptability to different robots and scenarios is improved, but the device complexity increases
Solution Approach 1:
The system implements a universal reconfigurable processor architecture that can execute multiple motion planning algorithms and support different robot types through a single unified hardware platform. The processor contains configurable modules for various planning approaches (RRT, A*, Dijkstra, etc.) and can be programmed to handle different scenario requirements, achieving broad adaptability across robots and environments while maintaining a consistent underlying hardware structure that reduces overall system complexity
Data Source
AI summary
A system for motion planning for autonomous vehicles can include a plurality of sensors, a plurality of detectors in electrical communication with the plurality of sensors, and a motion planning module in electrical communication with the plurality of detectors and a computing system of an autonomous vehicle. The motion planning module stores a planning graph with each node representing, explicitly or implicitly, time and variables defining a state of the autonomous vehicle, an operating environment, or both the state of the autonomous vehicle and the operating environment. A reconfigurable processor can include a collision detection module and, optionally, a shortest path module. Pre-computed collision data and planning graph data reflecting logical/physical node mapping can be communicated to the processor during a programming phase and used during runtime.