Abstract: A motion planner of an autonomous vehicle's computer system uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the vehicle prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the vehicle, the system sets a probability of collision with a dynamic object in the environment based at least in part on the collision assessment. Depending on whether the goal of the vehicle is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively high or relatively low potential of a collision with the particular dynamic object. The system then causes the actuator system of the vehicle to implement a motion plan with the applicable identified path based at least in part on the optimization.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change. Various aspects of such robot motion planning are implemented in particular systems and methods that facilitate motion planning of the robot for various environments and tasks.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change. Various aspects of such robot motion planning are implemented in particular systems and methods that facilitate motion planning of the robot for various environments and tasks.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change. Various aspects of such robot motion planning are implemented in particular systems and methods that facilitate motion planning of the robot for various environments and tasks.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change. Various aspects of such robot motion planning are implemented in particular systems and methods that facilitate motion planning of the robot for various environments and tasks.

Abstract: A motion planner of an autonomous vehicle's computer system uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the vehicle prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the vehicle, the system sets a probability of collision with a dynamic object in the environment based at least in part on the collision assessment. Depending on whether the goal of the vehicle is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively high or relatively low potential of a collision with the particular dynamic object. The system then causes the actuator system of the vehicle to implement a motion plan with the applicable identified path based at least in part on the optimization.

Abstract: A motion planner of an autonomous vehicle's computer system uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the vehicle prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the vehicle, the system sets a probability of collision with a dynamic object in the environment based at least in part on the collision assessment. Depending on whether the goal of the vehicle is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively high or relatively low potential of a collision with the particular dynamic object. The system then causes the actuator system of the vehicle to implement a motion plan with the applicable identified path based at least in part on the optimization.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change.

Abstract: A motion planner of a computer system of a primary agent, e.g., an autonomous vehicle, uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the primary agent prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the primary agent, the system sets a probability of collision with another agent, e.g., a dynamic object, in the environment based at least in part on the collision assessment. Depending on whether the goal of the primary agent is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively low or relatively high potential of a collision with the particular dynamic object. The system then causes the actuator system of the primary agent to implement a motion plan with the applicable identified path based at least in part on the optimization.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change. Various aspects of such robot motion planning are implemented in particular systems and methods that facilitate motion planning of the robot for various environments and tasks.

Abstract: A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change.

Abstract: A motion planner of an autonomous vehicle's computer system uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the vehicle prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the vehicle, the system sets a probability of collision with a dynamic object in the environment based at least in part on the collision assessment. Depending on whether the goal of the vehicle is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively high or relatively low potential of a collision with the particular dynamic object. The system then causes the actuator system of the vehicle to implement a motion plan with the applicable identified path based at least in part on the optimization.

Abstract: A mechanism for recycling error bits in a floating point unit is disclosed. A system of the disclosure includes a memory and a processing device communicably coupled to the memory. In one embodiment, the processing device comprising a floating point unit (FPU) to generate a result value from applying an operation on floating point number inputs to the FPU and generate an error value using the result value. The FPU also writes the result value to a first register of the processing device dedicated to storing results from the operation of the FPU and writes the error value to a second register of the processing device dedicated to storing errors from the operation of the FPU. The second register is separate from the first register.

Abstract: A mechanism for recycling error bits in a floating point unit is disclosed. A system of the disclosure includes a memory and a processing device communicably coupled to the memory. In one embodiment, the processing device comprising a floating point unit (FPU) to generate a result value from applying an operation on floating point number inputs to the FPU and generate an error value using the result value. The FPU also writes the result value to a first register of the processing device dedicated to storing results from the operation of the FPU and writes the error value to a second register of the processing device dedicated to storing errors from the operation of the FPU. The second register is separate from the first register.

Abstract: A mechanism for recycling error bits in a floating point unit is disclosed. A system of the disclosure includes a memory and a processing device communicably coupled to the memory. In one embodiment, the processing device comprising a floating point unit (FPU) to generate a result value from applying an operation on floating point number inputs to the FPU and generate an error value using the result value. The FPU also writes the result value to a first register of the processing device dedicated to storing results from the operation of the FPU and writes the error value to a second register of the processing device dedicated to storing errors from the operation of the FPU.

Abstract: A mechanism for recycling error bits in a floating point unit is disclosed. A system of the disclosure includes a memory and a processing device communicably coupled to the memory. In one embodiment, the processing device comprising a floating point unit (FPU) to generate a result value from applying an operation on floating point number inputs to the FPU and generate an error value using the result value. The FPU also writes the result value to a first register of the processing device dedicated to storing results from the operation of the FPU and writes the error value to a second register of the processing device dedicated to storing errors from the operation of the FPU.

Abstract: A level of indirection is utilized when writing to a microprocessor array structure, thereby masking hard faults in the array structure. Among other benefits, this minimizes the use of a backward error recovery mechanism with its inherent delay for recovery. The indirection is used to effectively remove from use faulty portions of the array structure and substitute spare, functioning portions to perform the duties of the faulty portions. Thus, for example, faulty rows in microprocessor array structures are mapped out in favor of substitute, functioning rows.